Insightful stories that revolve around technology, culture, and design

The Stanford Graduate School of Business, known as GSB, is a top business school worldwide. It has diverse students and faculty from all over the world. The school offers different business degrees and many subjects to choose from.

Before students enroll, they spend weeks researching courses to learn about the subjects, instructors, ratings & reviews from previous students, past curriculum, credits, timings, and much more.

Big decisions like college, courses, or career changes need planning, and informed students make smoother transitions. To help navigate these choices, online resources should be clear, and informative that guide students towards making well-informed life decisions.

About the course research & registration platform

Students faced significant challenges navigating the outdated Course Research & Registration platform. Here's what frustrated them:

1. Inconsistent navigation:The platform lacked a clear and consistent layout, making it difficult for students to find the starting point for tasks like finding the right course. They often got lost and frustrated trying to navigate between different sections too.
2. Unintuitive registration steps:The process of researching and registering for courses wasn't straightforward. Students were forced to jump back and forth between different sections to complete tasks, leading to confusion and wasted time.
3. Ineffective search tools:Finding specific courses was a struggle because the search function lacked effective filtering options. Students couldn't easily narrow down results based on department, course type, or other relevant criteria.
4. Hidden past information:Information from previous years, which could be crucial for course selection (e.g., professor reviews, and course descriptions), was buried deep within the platform.
5. Data overload:The data on the platform was not well organized, leading to a cluttered interface with numerous tabs and information overload. Finding specific details about a particular course or program proved time-consuming and frustrating.
6. Schedule blindspot:There was no way for students to see their selected courses at a glance. They had to manually add and remove courses from different sections, making it difficult to visualize their overall schedule and identify potential conflicts.
7. Outdated interface and functionality:The platform's outdated design felt clunky and difficult to use. Annoying pop-ups and unresponsive preview windows further hindered user experience and made it a chore to complete tasks.

These challenges made the course research and registration process unnecessarily complex and time-consuming. To address these shortcomings and improve the user experience, we adopted a design approach focused on clarity, efficiency, and user needs.

A look at our design approach

Understanding student needs and the GSB platform

Our journey to improve the user experience began with focusing on student needs. We collaborated closely with the GSB team, gleaning valuable insights into how students typically research courses.

Their research, combined with our exploration of the platform, provided a comprehensive understanding. This ensured we weren't just working with theoretical data, but with the actual functionalities students encounter when researching and registering for courses.

Simplifying and enhancing the platform

Next, we focused on making improvements guided by user needs. Studying the user journeys and personas — detailed profiles of typical users — provided a roadmap to areas that needed enhancement.

We streamlined the platform by removing unnecessary features and making existing ones more intuitive. This reduced clutter and made the platform easier to navigate and use for students.

Based on our findings, we introduced new functionalities to better address student needs and simplify tasks such as course search, registration, and schedule building.

Iterative design and overcoming challenges

Throughout this process, agility was key. We employed a rapid design approach, creating low-fidelity prototypes that could be quickly tested with real users. This iterative cycle allowed us to incorporate their feedback and refine the design efficiently.

User feedback proved invaluable in identifying areas for improvement and ensuring the final design met student needs. While challenges inevitably arose, such as technical limitations or conflicting priorities, they pushed us to think creatively and find innovative solutions.

This resulted in a more user-friendly platform that streamlined the course research and registration process for students.

User testing for optimal results

Since students were the key users of the GSB Course Research and Registration platform, their feedback was essential. We designed tests to see how easy it was for students to use the platform and make sure they liked it.

Through collaboration with the GSB team, we successfully recruited 11 students to participate in our testing sessions.  These sessions were recorded, allowing us to capture detailed user interactions and feedback.  The students' candid conversations provided invaluable insights, revealing perspectives we hadn't previously considered.

Testing Real-Life Tasks

The platform had many different features and ways to get things done. We wanted to make sure each part worked well. So, instead of giving students specific instructions, we gave them tasks that mirrored what they might actually do on the platform.

For example, instead of saying "Find FINANCE 123," we might say "Find a specific course and see all the details about it." This way, students had to explore the platform on their own and use different features. We created several tasks like this to test all the different screens we had designed.

Some testing scenarios

1. Planning Your Schedule: Here, we imagined a student meeting with their advisor to plan their winter and spring classes. We asked them to use the platform to build their schedule for those quarters.
2. Comparing Courses: This scenario involved a friend recommending three courses. Students had to find these courses on the platform, compare them to courses they already saved, and decide which ones to register for.
3. Registration Day: The final scenario focused on actually registering for classes. Students used the platform to register for the courses they chose in the previous step.

Observing User Interactions

To get the most accurate feedback, we had clear rules for how we interacted with students during testing. We didn't tell them what to do or how to use the platform. Instead, we watched them and asked questions to understand their thought process.

For example, we might ask: "Why did you click there?" or "What were you expecting to happen?" or "Do you find this section easy to use?" The questions we planned to ask and these rules helped us run successful user testing sessions.

Identifying Patterns

The testing sessions provided a wealth of information to guide our revisions.  While we couldn't address every suggestion, we focused on identifying common themes that emerged from multiple users. This helped us prioritize the most impactful changes to the platform.

Revising the interface design

The user testing sessions were an eye-opener. We saw how students interacted with the platform and discovered areas that needed improvement.  In addition, the GSB admin team provided valuable suggestions for functionalities that would enhance their ability to manage the platform efficiently.

We took all this feedback and moved on to phase 2 of the project. This phase focused on incorporating user insights and admin suggestions to refine the platform's design.

Conclusion

Usability testing proved to be a powerful tool. By observing how students used the platform, we were able to identify and address their pain points. This data-driven approach led to improvements in information discoverability and overall user experience.

The collaboration with the GSB team was instrumental in achieving this success. Their input on platform management functionalities ensured that the revised platform would be not only user-friendly for students but also efficient for administrators.

Through this collaborative effort, we transformed the platform into a smooth and effective tool, enabling MBA students to navigate the course selection process with greater ease.

Download our discovery playbook with actionable templates to guide you through conducting effective user testing sessions

Dive deeper into our successful collaboration with the Stanford Graduate School of Business! Read the complete case study — Migration & Design System Implementation for Stanford Graduate School of Business

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Given the transformative impact of Drupal 10 on the CMS landscape, it's important to remain informed and ready for change. Drupal 10, along with its latest version 10.2, represents a significant milestone in the evolution of the Drupal platform. Building upon the successes of its predecessors, Drupal 10 introduces a host of new features, improvements, and security enhancements, unlocking the latest capabilities and helping to maintain a secure and future-proof website.

As the title suggests, in this blog, we'll discuss Drupal 10's impact on the CMS landscape, sharing insights to facilitate a seamless migration journey, regardless of whether your website currently operates on Drupal or a different CMS platform like Joomla or WordPress.

Understanding the importance of migration:

Before delving into the specifics of the process, let's take a moment to understand why migrating to Drupal 10 is important by exploring the details of its features and functionalities.

1. Future-Proofing Your Website: Drupal 10 is built on the rock-solid foundation of Symfony 5.4 LTS, which means extended support and stability for years to come. This ensures your website stays compatible with modern technologies and avoids the need for frequent migrations in the future. As of January 30, 2024, Symfony 5.4 LTS boasts over 3 years of remaining support, providing ample time for future advancements and adaptations within the Drupal ecosystem.
2. Boosted Performance and Scalability: Drupal 10 introduces significant performance optimizations and caching mechanisms, resulting in faster page load times and improved user experience. The enhanced architecture allows for smoother website scaling, making it easier to handle growing traffic and complex functionalities without compromising performance. With the ever-increasing demand for responsive and efficient websites, this real-time benefit of Drupal 10 can be a game-changer for user engagement and conversion rates.
3. The Headless Potential: While Drupal shines as a traditional CMS, version 10 opens doors to the exciting world of headless architecture. With its robust REST API and decoupled front-end capabilities, Drupal 10 empowers you to deliver content to any platform or device seamlessly. This flexibility unlocks incredible possibilities for building immersive web experiences, mobile apps, and custom integrations, making your website truly future-proof and adaptable to evolving digital landscapes.
4. Streamlined Content Management: Drupal 10 introduces several user-friendly improvements that elevate content creation and management. The intuitive interface, powerful automation tools, and enhanced media handling features let you focus on crafting your message, not wrestling with technical hurdles. This translates to increased efficiency, improved team collaboration, and ultimately, a more engaging website for your audience.
5. Embrace Accessibility and Inclusivity: Drupal 10 champions inclusivity with built-in accessibility features and adherence to WCAG 2.2 guidelines. This translates to a website that caters to a wider audience, including users with disabilities and diverse needs. As accessibility considerations become increasingly important from both legal and human aspects, upgrading to Drupal 10 allows you to stay ahead of the curve and commit to inclusivity.

Getting ready for Drupal 10

Now that we understand the importance of upgrading, let's explore the steps involved in preparing for the transition to Drupal 10

1. Assess Your Current Setup: Start by evaluating your current Drupal setup, including the version you're using, installed modules, and any custom code. Look for potential compatibility issues or outdated features that might affect the upgrade process.
2. Review System Requirements: Check the system requirements for Drupal 10, such as PHP version compatibility, database requirements, and server configurations. Make sure your hosting environment meets these requirements to avoid compatibility issues during the upgrade.
3. Backup Your Website: Before upgrading, make a complete backup of your website and database. This ensures you have a secure backup in case anything goes wrong during the upgrade process.
4. Update Contributed Modules: Review and update any contributed modules on your website to their latest compatible versions. Pay attention to modules with dependencies or compatibility issues with Drupal 10.
5. Test, Test, Test: After preparing, thoroughly test your website to ensure it functions correctly post-upgrade. Test essential functions like content creation, user registration, and site navigation to identify and fix any issues promptly.

Migration Paths

Depending on the current Drupal version that is being used on your platform, there are different paths to upgrade to Drupal 10:

• From Drupal 9: Drupal 9 reached its end-of-life on November 1st, 2023. Upgrading from Drupal 9 is relatively easy with tools like Upgrade Status and Rector to fix any compatibility issues with Drupal 10.
• From Drupal 8: Since Drupal 8 is no longer supported, you can move directly to Drupal 10, considering the complexity of your website and necessary customizations.
• From Drupal 7 and Earlier: The official end-of-life date for Drupal 7 is January 5th, 2025. Upgrading from Drupal 7 or earlier versions requires a detailed plan. Ensure to allocate enough resources and involve a team of expert developers in the process.

Key Updates for 2024

In 2024, Drupal powers over 12 million websites worldwide, demonstrating its resilience and adaptability as its share continues to grow steadily. This year, several updates and enhancements are pertinent to the Drupal 10 upgrade process:

1. Drupal 10.2 Release: Drupal 10.2 is now available, offering bug fixes and minor enhancements to further enhance stability and performance.
2. Symfony 5.4 LTS Support: Drupal 10 leverages Symfony 5.4 LTS, ensuring long-term support and compatibility with the latest Symfony framework.
3. CKEditor 5 Integration: CKEditor 5, the new default editor in Drupal 10, introduces improved accessibility and usability features for content creation and editing.
4. Improved Content Modeling and Block Management: Streamline your content creation and organization with enhanced options for structuring and managing your website's content and blocks.
5. Refined Menu and Taxonomy Organization: Navigate your website with ease thanks to improved menu and taxonomy structures, facilitating user accessibility.
6. Granular Permission Administration: Fine-tune user permissions with greater control and precision, ensuring optimal access management for your team.

Upgrading to Drupal 10 may seem difficult, but reaching out to experienced Drupal establishments, developers and tapping into the wealth of resources available within the Drupal community and With careful planning and the right approach, your migration to Drupal 10 can be a smooth and successful endeavor.

Looking ahead

Drupal is driven by a commitment to open-source development and continuous improvement. Its modular architecture and extensive third-party integrations ensure relevance and adaptability, enabling enterprises to customize their websites to meet evolving user needs, particularly focusing on security, privacy, and impeccable speed.

As we move forward into 2024 and beyond, Drupal remains at the forefront of the CMS landscape with capabilities to embrace headless architecture, immersive web experiences, and personalized content delivery. It continues to pave the way, offering stability, security, community support, and endless possibilities for groundbreaking digital experiences, benefiting users and businesses alike.

This blog is an updated version of "How to Prepare and Upgrade from Drupal 9 to Drupal 10,". For more insights, feel free to refer to the older blog.

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Drupal is constantly evolving, embracing change as its driving force. From older versions like Drupal 7 to the newest ones, Drupal 10 and 10.2, the people behind Drupal keep working hard to make it easier to use, relevant in web development, delivering advanced solutions, and meeting the evolving needs of businesses and users.

The latest versions, Drupal 10 and 10.2, enhance website building with improved editing tools, customizable pages, easier development, stronger security, and modern tech.

These improvements mean businesses get more out of their investment.

As Drupal 7 will stop getting support on January 5th, 2025, it's a good time to think about moving to a newer version. This blog aims to help streamline the decision making process.

Drawbacks of sticking with Drupal 7

It's essential to recognize the limitations of continuing with Drupal 7. Opting to stay with this version might subject both you and your users to various risks, including exposure to vulnerabilities, encountering bugs, and facing challenges when integrating with new web standards. Other issues could include:

• Unsupported modules, themes, and outdated Drupal frameworks can make security risks worse.
• Compatibility problems with newer technologies and browsers can affect user experience.
• Maintenance can become complicated and costly, needing custom fixes.
• Delaying migration could lead to data loss and website downtime.
• Unsupported software versions are often targeted by hackers.
• Compliance issues may arise with regulations like GDPR, HIPAA, or PCI DSS.
• Drupal 7 may become less compatible with modern environments over time.

Reasons to upgrade to Drupal 10

1. Strengthened security measures

The new version focuses on security, with stricter coding standards, better access controls, and regular security updates. Audits and patches keep websites protected from evolving security risks.

2. Better content management

Teams get enabled with easier content editing, customizable workflows, and improved revision controls. Streamlined workflows and collaboration help deliver engaging and better content.

3. Optimized performance

Drupal 10 and 10.2 prioritize performance by using BigPipe caching to speed up page loading for dynamic content. This, along with improved caching mechanisms and optimized database queries, reduces page load times and improves scalability.

4. Advanced dev tools

Updated APIs, better documentation, and expanded development frameworks support modern practices like decoupled architecture and headless CMS, allowing for modern digital experiences.

5. Native WebP image support

Drupal 10.2 adds native support for WebP images, making pages load faster and improving the user experience, especially on mobile devices. This ensures images are optimized for better performance.

6. Accessibility improvements

Drupal 10.2 includes an updated CKEditor with better accessibility features, meeting standards like WCAG. These improvements make it easier for content authors to create accessible content, ensuring inclusivity for all users.

7. Enhanced media library management

Drupal 10.2 improves media asset management with better categorization options, improved search, and enhanced metadata management. This streamlines content creation, making it easier to manage and organize media assets.

Potential ROI of migrating to Drupal 10 and other latest versions

Let's take a closer look at the numerous benefits of upgrading to Drupal 10 and other recent versions, including increased ROI, streamlined operations, and good UX.

• Cost-Effective Redesign — Save 30-40% on redesign costs by addressing user experience during site migration, making it the ideal opportunity to enhance UX.
• Efficient Administration — Simplified user management and permission settings minimize administrative time and effort.
• Streamlined Content Creation — User-friendly interfaces and optimized workflows make content creation easier and more engaging.
• Improved User Engagement — Faster loading times and better mobile responsiveness lead to lower website bounce rates.
• Higher Conversion Rates — Enhanced user journeys and improved experience result in increased conversion rates.
• Time-Saving Content Management — Advanced editing features and automated workflows reduce content management time.
• Enhanced Security and Compliance — Strengthened security measures and compliance ensure data protection and instill trust.
• Minimized Website Downtime — Improved stability and scalability guarantee uninterrupted website access.
• Resource Optimization — Reduced downtime and administrative burden free up resources for other essential functions.

These represent some potential benefits, and the actual ROI for your organization will vary based on your specific needs and goals. Schedule a consultation with our Drupal experts for an insightful conversation.

Should you wait for Drupal 7 to cease before migrating?

First, let’s look at the reasons to wait

• Migrating your website can be complex and require significant resources. If you're working with budget constraints or lack technical expertise, it might be more practical to wait until closer to the end of life.
• If your website depends on custom modules or features not yet compatible with Drupal 10, you may need to wait for compatibility solutions or develop alternatives before migrating.
• Starting a migration project too close to the end of life could be risky and stressful, especially if you have tight deadlines for website updates.

Reasons you should migrate to the latest version

• As Drupal 7 approaches its end, finding experienced developers becomes increasingly difficult and costly. Migrating to Drupal 10 now allows you to access a larger pool of developers and secure future support for your website.
• Initiating the migration process early ensures a more manageable and controlled transition. You'll have ample time to address compatibility issues, train your team, and prepare your users for the change.
• Drupal 10 offers significant performance improvements, enhanced scalability, and exciting new features such as CKEditor 5 and a modular architecture. This results in a faster, more flexible, and feature-rich website for your users.
• With Drupal 7 reaching its end-of-life on January 5th, 2025, waiting makes your website vulnerable due to the lack of security updates. Most importantly, upgrading to Drupal 10 ensures long-term security and peace of mind.

Looking ahead

Adjusting to change can feel uneasy initially, but the benefits often outweigh the initial challenges — this perspective is crucial when considering migration.

Ultimately, the decision to migrate is yours. Consider the potential security risks of delaying the upgrade versus the advantages of early adoption, and choose the timeline that best fits your resources, priorities, and website needs.

Even if you decide to postpone the migration, it's important to plan your strategy in advance to avoid any last-minute rushes and ensure a smooth transition.

Contact our Drupal Migration experts for further information or visit to explore our migration services.

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As our planet changes, a complex challenge that has emerged is climate change-induced human migration. Millions are being forced to leave their homes, seeking safety and stability. Recognizing this critical issue, the United Nations (UN) leads coordinated efforts to support vulnerable communities.

The International Organization for Migration (IOM), the UN migration agency, promotes orderly and humane migration that benefits all. We had a great opportunity to join forces with IOM, contributing our expertise to a data-driven solution that sheds light and raises awareness on this complex issue.

IOM's commitment

Recognizing the urgency of climate change and its profound impact on human mobility, IOM has taken a leading role in addressing this global challenge. At the recent COP28 (28th United Nations Climate Change Conference) summit in Dubai, IOM aimed to foster international collaboration and drive ambitious action towards a low-emission and climate-resilient future.

As part of its strategic approach, IOM’s Global Data Institute (GDI) wanted a data-driven solution that leverages analytics to forecast where and when populations worldwide will be exposed to mobility-related climate hazards, such as floods, storms, and droughts so that decisions can be made based on evidence. This initiative helps the world understand climate-related displacement risk better and explore ideas to address it properly.

We designed and developed an interactive data visualization solution that IOM could use to present its research and approach at the COP28 summit.

The power of data and insights

We used the power of data and its ability to reveal hidden patterns and insights. Our solution processes large datasets and employs advanced analytics techniques to visually represent the complex dynamics of mobility-related climate hazards. It analyzes future trends and identifies emerging patterns based on state-of-the-art climatic, demographic, and economic datasets.

Take a look at GDI’s Climate Mobility Impacts dashboard on the Global Migration Data Portal. Users can explore and analyze how different climate hazards are expected to affect humans in the future.

This data-driven visualization approach provides a vital foundation to allocate resources and make informed policy decisions. International organizations can now interactively explore where and when climate hazard exposure, high population densities, and economic vulnerability will coincide in the future. It enables them to provide effective and targeted support for vulnerable populations.

This shift towards evidence-based decision-making ensures that aid and resources reach those who need it most, maximizing their impact and fostering a more resilient future for all.

Collaborative effort and action

This was a high-priority project for us with tight deadlines to meet. We had to design a portal that reads climate change data and presents it in interactive 2D and 3D maps, graphs, and bar charts.

This project demanded strong collaboration and dedication from a diverse team of experts. Our team of designers and JavaScript developers closely worked with the IOM team to research extensively, design, and develop a solution that meets current goals and is scalable for future goals.

Agile methodologies were at the core of our approach, and we were able to adapt to emerging challenges and navigate complexities with remarkable efficiency. Our team’s commitment to agility proved invaluable, enabling us to successfully deliver the project on time and within budget.

Summing up

We will delve deeper into the design and development of our data visualization portal, offering insights into its features and unique capabilities in our case study. We believe that by sharing our knowledge and expertise, we can inspire further collaboration and advancement with data as a catalyst for positive change.

We are glad that we could help IOM present its data and approach visually to empower communities to navigate the complexities of climate change-induced migration. We look forward to working on more solutions that will positively impact lives worldwide. Talk to our experts to know more about how we can customize digital solutions that fit your needs.

Today's AI, powered by machine learning algorithms, has introduced us to semantic search. Essentially, semantic search interprets queries in a more human way by not just reading the keywords we type but also understanding the context, emotions, and intent behind our queries.

In our first blog in the semantic search series, we discussed how it can transform how we search. We also presented a demo application that we built to experiment with semantic search.

In this blog, we will explore a combination of Pinecone and OpenAI which are emerging as one of the key players in creating intelligent, user-friendly AI experiences. We'll also unravel the code that powers our demo application and illustrate how AI is revolutionizing data search.

Our approach to building the solution

We are moving away from traditional keyword-based searches towards searches that grasp the purpose and context behind our query. A key element enabling this smarter search is the integration of embedding models within Large Language Models (LLMs).

These models create vector embeddings that represent data in a multi-dimensional way, which helps in understanding content on a deeper level.

Tackling challenges with the right tools

While the potential of semantic search is impressive, there are some challenges in making it work effectively. These challenges include handling a robust infrastructure, reducing the time it takes to get search results, and keeping data up-to-date to ensure it remains relevant and useful.

However, when we have the right tools in place, these challenges become much easier to handle. An optimized vector database, for example, enhances the user experience by reducing delays and enabling real-time updates to the search results. It means we no longer have to choose between fast query responses and keeping our data current, resulting in a smoother and more efficient search experience.

How vector databases enhance semantic search

In semantic search, a vector database plays a crucial role as a storage hub for data embeddings. These embeddings capture the intricate contextual nuances of data.

When we perform a search query, instead of just matching words, we're looking out for vectors that carry similar meanings. This action not only sharpens the relevance of search results but also tailors them to fit the context. It proves beneficial in various scenarios, such as:

• Precise information retrieval: It helps teams to precisely locate specific internal data or knowledge without sorting through irrelevant information.
• User-friendly applications: It fine-tunes search results to match individual queries and elevates user engagement.
• Enhanced data integration: It amplifies search capabilities for large user bases by combining insights from diverse data sources.
• Personalizedrecommendation systems: Users receive suggestions closely aligned with their search history and preferences, making their experience more personal.
• Detecting anomalies in data streams: It assists in identifying and flagging outlier data or information that significantly deviates from established patterns, ensuring data quality and consistency.
• Streamlined content classification: It automates the grouping and labeling of data based on similarities, simplifying data management and utilization.

Why we chose Pinecone as our vector store

While looking for the right solution to seamlessly implement semantic search, we needed something that could easily work with our systems.

Pinecone stood out as a great choice, thanks to its user-friendly REST API. Apart from being easy to integrate, Pinecone provides:

• Extremely fast search results, even when dealing with huge amounts of data.
• Ability to update our existing data points in real-time, ensuring that we always have the latest information at our fingertips.
• A fully managed platform, so we can focus on using it rather than dealing with maintenance.
• Flexibility in terms of hosting options, including platforms like GCP, Azure, and AWS.

Selecting OpenAI as our embedding service

To enhance the efficiency and precision of our semantic search capabilities, selecting the right embedding service was important.

We found OpenAI's Embedding API to be an ideal choice for achieving unmatched contextual understanding in data processing.

Here's why we opted for OpenAI:

• Deep text understanding: OpenAI's embedding service is known for its ability to understand text deeply, making it skilled at finding important patterns and connections in large datasets (powered by the advanced GPT-3 model).
• Easy integration: OpenAI's well-documented API seamlessly fits into our existing systems, making it simple to add advanced search features. It's a quick choice for trying out new ideas.
• Continuous improvement: OpenAI is committed to making its services better over time. It means we can expect regular updates and enhancements, ensuring our search capabilities stay at the cutting edge of technology.

Opting for Rust as our backend API

To enhance our backend infrastructure, selecting the right programming language is crucial, but not necessarily restrictive.

Rust stood out as a great option, but it's not a strict requirement. Languages like JavaScript, Python, or any language capable of making cURL requests and handling data can work just fine.

However, there were some compelling reasons that made us choose Rust, especially as we explore Rust-based libraries for near real-time LLM inference on cost-effective hardware, which we'll discuss in a future instalment of this series.

Here's why Rust was an excellent choice for our API development:

• Speed: Rust is known for compiling efficient machine code, delivering performance similar to languages like C and C++. This speed makes it a strong choice for high-throughput APIs, although it's important to mention that our current reliance on a third-party API presents a challenge in achieving response times under 150 ms.
• Concurrency: Rust's ability to prevent data conflicts in concurrent programming with its skill in managing asynchronous tasks is very useful when handling many API requests happening simultaneously.
• Memory Safety: Rust proactively detects common errors like trying to access non-existent data, reducing crashes and security vulnerabilities during the coding process. It also works seamlessly with tools like the rust-analyzer plugin in VS Code, making debugging and development smoother.
• Compatibility: Rust works well with C libraries, making it easy to directly use functions from these libraries. This provides flexibility when integrating with existing systems.
• Community and Ecosystem: Rust benefits from a growing library collection and an active community. It's becoming a central hub for strong tools needed to create web services and APIs. The fact that enthusiasts have creatively made LLMs work with Rust, highlights it as a quick and capable option for more experiments.

Streamlining data and AI embeddings on Pinecone

In this section, we'll walk you through the process of collecting data, generating AI embeddings using the OpenAI Embedding API, and conducting semantic search experiments within the Pinecone Vector Database.

OpenAI models, like GPT-3, have been trained on a vast and diverse collection of text data. They excel at capturing complex language patterns and understanding context. These models transform each word or phrase into a high-dimensional vector. This process, known as embedding, captures the meaning of the input in a way that's easy for systems to understand.

For example, the word "lawyer" might be represented as a 1536-dimensional vector (using the 2nd gen OpenAI embedding API model text-embedding-ada-002, which is based on GPT-3). Each dimension in this vector captures a different aspect of the word's meaning.

The below example is from Tensorflow. It uses a completely different model, but the concept remains the same.

These embeddings play a crucial role in semantic search. When a user enters a search query, the AI model creates an embedding of that query.

This embedding is then sent to the vector database, such as Pinecone in our case, which finds and retrieves the most similar vectors, essentially providing the most contextually relevant results.

Think of it as translating human language into a format that machines can easily grasp and work with effectively. By generating these embeddings, OpenAI models enable us to achieve more precise and context-aware search results, a significant advancement over traditional keyword-based search methods.

Let's take an example: imagine a user searching for "rights of a tenant in Illinois." With a traditional keyword-based search, you'd get documents containing those exact words. But when we use an AI model to create embeddings, it understands the real meaning – that the user is looking for information about tenant rights in Illinois.

The system then fetches relevant results, even if they don't use the exact phrasing of the query but discuss the same idea. This could mean providing a comprehensive guide to tenant rights, mentioning a relevant court case in Illinois, or sharing a related law statute. In the end, it gives the user a more detailed and helpful response.

It's the combination of OpenAI's Embedding API and Pinecone's efficient vector search that makes this enhanced, contextually-aware search experience possible.

Implementation of our solution

Please note that this experiment was tailored for a specific website.

Here's what we did:

Step 1

Data Cleanup and Preparation: Our first step involved cleaning up the data and making sure it was compatible with Pinecone. We used Python for data preparation since it is simple for handling large datasets.

Data Collection: We collected a CSV file with over 1600 rows, all related to legal assistance from the Illinois Legal Aid Online site.

Pinecone Database Requirements: Pinecone needs data to have three specific columns: id, vectors, and metadata. We ensured our data met these requirements for seamless integration.

Before we proceed to create the vector, let's organize the data. Column names have been shortened for data privacy.

Note: We are not using metadata in our current experiment. It's primarily used for filtering and faster querying, or as a means to transmit data while querying in a different environment. In our case, Pinecone Query API will perform more efficiently without the inclusion of metadata.‍

# Setup pinecone structured data
from slugify import slugify

def create_doc_index(row):
    """
    An apply function to alter the content.
    
    Creates a new column called "id".
    Which contains the slugified title.
		
		Eg. "hello world" will be 'hello-world'
    """
    row['id'] = slugify(row['Title'])
    return row

def create_data(row):
    """
    An apply function to alter the content.
    
    Creates a new column called "data".
    Which contains the following as a single text string:
    '
        Title: {Title column value},
        Description: {Content description column value},
				...
				Content: {Content column value}
    '
    """
    row['data'] = f"Title: {row['Title']}, Description: {row['Content description']}, Content: {row['Content block']}"
    return row

def create_metadata(row):
    """
    An apply function to alter the content.
    
    Creates a new column called "metadata".
    Which contains the following as a dict:
    {
        'Title': {Title column value},
        ...
        'Legal category (select all that apply)': {Legal category (select all that apply) column value},
    }
    """
    row['metadata'] = {
        'Title': str(row['Title'] if row['Title'] else ''),
        ...
        'Legal category (select all that apply)': str(row['Legal category (select all that apply)'] if row['Legal category (select all that apply)'] else ''),
    }
    return row

Here's an overview of what the data looks like after we completed the initial cleanup and processing. We applied the functions we created earlier to each row in the database.

Next up is the generation of AI embeddings for the vector database. Please note that you'll need OpenAI API keys for this step.

def embeddings(text):
    # Make API request to OpenAI's Embedding endpoint
    response = openai.Embedding.create(
        model="text-embedding-ada-002",  # The model to use for the AI embeddings
        input=text,  # The text to embed
    )
    # Retrieve and return the AI embedding from the response.
    return response['data'][0]['embedding']

# Generate the first 10 components of the AI embedding for "Hello"
embeddings("Hello")[:10]

‍
For example, when we input the test string "hello," we receive the following set of embeddings as output.

[-0.021849708631634712,
 -0.007138177752494812,
 -0.028344865888357162,
 -0.02456468529999256,
 -0.023603402078151703,
 0.028864478692412376,
 -0.012243371456861496,
 -0.0028562454972416162,
 -0.00829431600868702,
 -0.00539747579023242]

‍
We apply this AI embedding function to all the rows in our dataset. You can see the vector column in the image below.

Now, on to the final step - uploading this data to Pinecone. We are using the gRPC protocol provided by the Pinecone library, which makes this upload faster, taking less than 15-30 seconds in total.

# Creating the Pinecone index only if it does not exist
if index_name not in pinecone.list_indexes():
    pinecone.create_index(
        name=index_name,
        dimension=1536,  # specifying the dimension for the index vectors
        metric='cosine'  # specifying the metric for vector comparison
    )

# Creating an interface to interact with the Pinecone index
index = pinecone.GRPCIndex("hybrid-legal-doc-search")

# Inserting/updating vectors in the index from our DataFrame 'ddf'
index.upsert_from_dataframe(ddf, batch_size=100)

‍
Take a peek at the index statistics using

index.describe_index_stats()

{'dimension': 1536,
 'index_fullness': 0.0,
 'namespaces': {'': {'vector_count': 1697}},
 'total_vector_count': 1697}

‍

Lastly, when querying in Pinecone, we need to provide the AI embeddings to get the relevant results.

You might wonder why we're generating AI embeddings before uploading data to Pinecone and prior to querying. The reason is that embeddings can be created using various models, and they are not always compatible with one another.

Different models like Bert and simple Word2Vec, among others, can be used to perform the same task, and they produce embeddings that may not work interchangeably.

This is why it's important to have the embeddings prepared in advance to ensure a smooth and consistent search experience.

# Querying the Pinecone index with the query vector
# 'top_k' indicates we want the top 3 results and 'include_metadata' set to False indicates we don't want additional metadata
xc = index.query(embeddings("family and legal advice"), top_k=3, include_metadata=False)

print(xc)

# Output:
{'matches': [{'id': 'someone-called-dcfs',
              'score': 0.8318765,
              'sparse_values': {'indices': [], 'values': []},
              'values': []},
             {'id': 'divorce-and-parental-responsibilities',
              'score': 0.81308925,
              'sparse_values': {'indices': [], 'values': []},
              'values': []},
             {'id': '7-things-i-can-do-to-safely-talk-to-my-lawyer',
              'score': 0.81211287,
              'sparse_values': {'indices': [], 'values': []},
              'values': []}]

‍
Step 2

Next, we'll create an API that can manage both AI embedding and querying processes seamlessly.

Here is an overview of the backend development process.

Here is the code for Rust backend that couples our Pinecone and OpenAI APIs together.

// The "q" route handeler
pub async fn query_q_api(
    // The query
    query: HashMap<String, String>,
	// Web client
    client: Arc<Client>,
	// LRU Cache
    q_cache: Arc<Mutex<LruCache<String, Vec<ResponseMatch>>>>,
) -> Result<impl warp::Reply, warp::Rejection> {

    // Extracting and decoding the query parameter 'q'
    let input = decode(query.get("q").unwrap_or(&String::new())).unwrap();
    let q_cache_key = input.clone();

    // Attempting to get a response from the cache if it exist
    {
        let mut q_cache = q_cache.lock().await;
        if let Some(cached_response) = q_cache.get(&q_cache_key) {
            // If a response is found in the cache return the cached response
            return Ok(warp::reply::json(&cached_response.clone()));
        }
    }

    // If no response was found in the cache, fetch new data
    let response_matches = fetch_new_data(client, input).await;

    // Put the new data into the cache for future use
    {
        let mut q_cache = q_cache.lock().await;
        q_cache.put(q_cache_key, response_matches.clone())
    }
    
    // return the fetched data as the response
    Ok(warp::reply::json(&response_matches))
}

‍
For our demo, we've implemented a short-term, in-memory cache to prevent unnecessary API calls.

However, in the future, we aim to introduce an on-disk cache and implement local semantic search capabilities for queries with similar meanings. It will enhance the efficiency and responsiveness of our system down the line.

// Creating a shared LRU cache for "q" route and defining the route
    let q_cache = Arc::new(Mutex::new(LruCache::<String, Vec<ResponseMatch>>::new(CACHE_SIZE)));
    let q_route = warp::path("q")
        .and(warp::query::<HashMap<String, String>>())
        .and(with_client(client.clone()))
        .and(with_q_cache(q_cache.clone()))
        .and_then(query_q_api);

‍
Next, we have a helper function ‘fetch_new_data’. This function handles calls to both the OpenAI and Pinecone APIs, ensuring a smooth flow of data retrieval and processing.

// This function fetches new data from OpenAI and Pinecone APIs
pub async fn fetch_new_data(client: Arc<Client>, input: String) -> Vec<ResponseMatch> 
    
    // Getting the AI embeddings from OpenAI API
    let openai_resp = get_openai_response(&client, input, &openai_org_id, &openai_api_key).await;
    let embedding = &openai_resp.data.get(0).unwrap().embedding;

    // Getting the top matches from Pinecone API
    let pinecone_resp = get_pinecone_response(&client, embedding.to_vec(), &pinecone_api_key, &pinecone_endpoint).await;
    
    // Converting the Pinecone response to a list of ResponseMatch and returning it
    return pinecone_resp.matches.into_iter().map(|match_| {
        let metadata = match_.metadata.unwrap_or(json!({}));
        ResponseMatch {
            id: match_.id,
            score: match_.score,
            title: metadata.get("Title").unwrap_or(&json!("")).as_str().unwrap_or("").to_string(),
            description: metadata.get("Description").unwrap_or(&json!("")).as_str().unwrap_or("").to_string(),
            url: metadata.get("url").unwrap_or(&json!("")).as_str().unwrap_or("").to_string(),
        }
    }).collect::<Vec<ResponseMatch>>();
}

// This function sends a POST request to the OpenAI API to get the AI embeddings for the input text
pub async fn get_openai_response(client: &Client, input: String, openai_api_key: &str, openai_org_id: &str) -> OpenAIResponse {
    
    // Sending the request to OpenAI API and getting the response
    let response = client
        .post("https://api.openai.com/v1/embeddings")
        .header("Content-Type", "application/json")
        .header("Authorization", format!("Bearer {}", openai_api_key))
        .header("OpenAI-Organization", format!("{}", openai_org_id))
        .json(&json!({
            "input": input,
            "model": "text-embedding-ada-002"
        }))
        .send()
        .await.unwrap()
        .json()
        .await.unwrap();
    
    response
}

// This function sends a POST request to the Pinecone API to get the top matches for the input embedding
pub async fn get_pinecone_response(client: &Client, embedding: Vec<f64>, pinecone_api_key: &str, pinecone_endpoint: &str) -> PineconeResponse {
    
    // Sending the request to Pinecone API and getting the response
    let response = client
        .post(pinecone_endpoint)
        .header("Api-Key", pinecone_api_key)
        .header("Content-Type", "application/json")
        .json(&json!({
            "vector": embedding,
            "topK": 3,
            "includeValues": false,
            "includeMetadata": true
        }))
        .send()
        .await.unwrap()
        .json()
        .await.unwrap();
    
    response
}

‍

Currently, we're experiencing significant delays with both the Pinecone and OpenAI APIs. Our goal is to cut down this delay further by exploring on-premises solutions.

It involves considering options like Bert for AI embedding and Milvus or similar open-source vector databases that can be used locally.

With the API components in place, take a look at what the demo frontend has to offer.

Here's a simple "useEffect" method within our React app. It's responsible for updating the search results in real-time as the user types in their query.

// searchText is the text from the search box.
useEffect(() => {
		// Check in the client-side cache if found set the results
    if (searchCache.current[searchText]) {
      setResults(searchCache.current[searchText]);
    } else {
			// When the user enters more than 2 letters the fetching process starts
      if (searchText.length > 2) {
				// Defining the fetch function
        const fetchData = async () => {
          try {
						// Calling our previously developed Rust API
            const response = await fetch(
              process.env.REACT_APP_BASE_API_URL + `/q?q=${encodeURIComponent(searchText)}`
            );
            const data = await response.json();
						// Store to user cache, just in case the user searches for the same Query
            searchCache.current[searchText] = data;
						// Set results
            setResults(data);
          } catch (error) {
            console.error("Error fetching data:", error);
          }
        };
				// Caling the fetch function
        fetchData();
      }
    }
	// monitoring the searchText var for changes
  }, [searchText]);

‍
What’s next

We tried the RAG approach which involved feeding the results into a ChatGPT-like system, ultimately providing users with more personalized results without the need to navigate through numerous blog pages. We employed interesting strategies and encountered some challenges with this approach. You can delve deeper into our journey by checking out our next blog in this series.

We are soon planning to launch a platform where you can play with all these experiments. As we move forward, our focus is on matching the right technology with the needs of our customers. The field of semantic search and vector similarity is full of exciting possibilities, such as creating recommendation engines based on similarity.

Right now, we're actively working on several Proof of Concepts (PoCs), where we're balancing speed and accuracy depending on the specific application. We're exploring various models, including BERT-based ones and more, beyond the standard 'text-embedding-ada-002.'

We're also attentive to our customers' preferences for platforms like Azure or GCP. To meet these preferences, we're adjusting our approach to include models recommended by these providers, aiming to create a versatile system that can effectively serve different use cases, budgets, and unique requirements.

‍

Artificial Intelligence (AI) has transformed the way we search for information. Search behavior has evolved from simple keyword searches like "running shoes" to specific and personalized queries like "comfortable shoes for jogging” or “athletic shoes for beginners”. This change will continue as customer behavior evolves with technology.

A McKinsey study reports that 71% of users expect personalized search results and are often frustrated when these expectations are not met.

The traditional keyword-based systems do struggle with natural queries and miss the context in the process, while semantic search understands the intent behind the query and delivers more relevant results.

Hence, we leveraged AI by merging our understanding of Large Language Models (LLM), vector databases, and Retrieval Augmented Generation (RAG), to create an advanced semantic search system to respond to complex but natural human queries.

Limitations of traditional searches and LLMs

We examined the problem closely to ensure that our solution effectively addresses the challenges with traditional keyword-based systems and to build an outcome, that can be put to use.

Here are some reasons why we chose to go ahead with our semantic search and custom result generation experiment:

Challenges with traditional search systems

1. Speed and efficiency: Traditional search methods may not deliver quick or efficient results whereas semantic search can sort through large amounts of data in real time to find results faster.

2. Relevance: Traditional search relies on exact keyword matches and often misses the context of the search queries. Semantic search, on the other hand, understands the intent behind a query and looks for relevant results.

3. Complex infrastructure: When it comes to traditional searches, integrating AI functionalities can be quite a complex task. It involves setting up additional databases and fine-tuning models just to understand search queries better.

4. High latency: Dealing with large sets of records in traditional databases becomes a slow process and at times expensive when it comes to storing and searching for information.

Drawbacks of foundational LLMs

LLMs are advanced AI-driven models designed to understand and generate human-like text. Some examples of foundational LLMs include OpenAI's ChatGPT or GPT-3.5-Turbo, and Anthropic's Claude.

1. Static nature: LLMs, such as GPT, have a fixed knowledge base, and they can't adapt to or include new real-time information once they've completed their training.

2. Lack of domain-specific knowledge: LLMs are trained for general tasks and would not have specific details from private or recent datasets, like a company's latest products or updates.

3. Black box functionality: It can be quite challenging to understand where the answers generated by an LLM come from as it's not always clear which sources or reasoning it relies on.

4. Cost and inefficiency: Developing and launching foundational models like GPT demands significant resources and makes it difficult for many organizations to customize them for specific tasks.

5. Limited contextual understanding: Although LLMs can produce clear responses without extra assistance (like using RAG), they may not consistently provide the most context-aware answers. For example, helping a customer book a flight often requires real-time data and context, which a basic GPT model might lack.

6. Accuracy: Using LLMs without any tweaks or adjustments can sometimes result in errors, incorrect information, or less-than-ideal answers for specific, detailed tasks.

User queries have changed from simple keyword-based questions to more complex, context-heavy inquiries. This shift shows how people increasingly depend on technology to understand detailed and varied questions.

While traditional keyword-based systems such as Solr, Google PSE, and Algolia expect users to adjust their questions to fit the system's limitations, newer AI-enhanced platforms are setting a new standard. They're now adapting to users, grasping their intentions, understanding the context, and even picking up on emotions.

This change marks a move towards more intuitive, conversational, and human-friendly interactions with technology, reflecting our natural desire for clear, relevant, and instant responses.

Designing a semantic search and RAG system

Our goal was to create a system that could understand language more effectively, efficiently manage vast amounts of data, and deliver personalized results. We focused on addressing the challenges posed by traditional search methods and the drawbacks of foundational LLMs.

We aimed to use our insights to build a system capable of finding the most relevant information and providing useful answers. Our approach was simple, focused on practical solutions, and leveraged the following key features:

AI-powered search

We wanted to develop a search system that uses semantic search. We achieved this by taking user search queries and converting them into numerical vectors using machine-learned meaning in the backend. These sets of numbers or vectors were then matched against a database of similar vectors to identify the most similar results.

We enhanced the search results by understanding the user's intended meaning and providing more relevant results. It's worth noting that semantic search can sometimes produce inaccurate results when dealing with short queries, typically comprising just two or three specific keywords.

Contextual response generation with AI

We improved the user experience by providing responses that are aware of the context of the user's queries through an 'Ask AI' function. This function considers all the relevant results gathered from previous smart searches.

The data returned from the smart search can be used as context for the user's query and sent to the OpenAI Chat API to generate a continuous text response. This process is known as Retrieval Augmented Generation.

With these features, we believe our AI-driven search system can greatly enhance the way people search for information.

Our toolbox

We chose the following tools for storing data:

• Pinecone: It's an AI solution that uses vectors for scalability and doesn't require any maintenance or troubleshooting. It speeds up data searches within milliseconds, applies filters to metadata, and supports indexes that help find precise search results for various tasks.
• Meilisearch: This is a free and open-source alternative to Algolia, that comes with an exciting experimental feature allowing us to store vectors with traditional data. It combines the key features of both Algolia and Pinecone and delivers results in just milliseconds.
• OpenAI API: This API gives access to all the general-purpose models, including GPT-4 and more. It can be easily integrated into applications to handle complex tasks and enhance experiences with AI.

As for the demo, we chose the Tokio web server (built on Rust) for the backend and its main query handler due to its high performance. It's reliable and provides a safe environment, making it an ideal choice for building an API.

Combining AI with Rust brings benefits like speed, handling multiple tasks simultaneously, ensuring memory safety, and the ability to work with C libraries. Additionally, Rust provides a supportive community and a growing set of libraries for creating web services and APIs.

The demo search application

Challenges with the RAG system

Limited model context windows and large text blogs

A context window is like the number of tokens or text the model can take in before it generates more text. For example, common models like GPT-3.5-Turbo have a context window of 4,096 tokens, which equals about 3,000 English words. Meanwhile, bigger models like GPT-4 have 32,768 tokens, which would be about 24,500 words. (These are approximate figures and can vary depending on the text and how it's processed by the tokenizer model.)

When we add more data, these can become limitations. Some models, like Claude 2, have a wider context window of 100,000 tokens, but you start seeing diminishing results, and issues like hallucinations pop up.

We tried out different approaches, but for our experiment, we went with the simplest one, "trimming." However, we considered the Pinecone scores before cutting out parts of the context.

We're also exploring other ideas, like creating smaller vectors and using their metadata to connect back to the main data. This could potentially save a lot of tokens.

A steerable prompt for the GPT model

We tried out various prompts to arrive at what we have now. In this experiment, we used the following prompt: (Note that prompting changes with models and version. The one we used here is GPT-4 2023 June 13th snapshot)

    
    {
    // The 'system' role is what drives the model, in other words, defines the goal or purpose of the model to behave.
        role: "system",
        content: `You are an advanced legal aid search engine bot, developed by ILAO - Illinois Legal Aid Online. Your primary role is to deliver highly relevant, accurate, and useful search results to users based on their Query and the available Context.
    Please follow these guidelines strictly:
    1. Provide responses directly related to the user's Query. If the query is unclear or insufficient, summarize the Context and include any pertinent details about the Query.
    2. Don't ask the user questions as they don't have the capability to respond.
    3. Don't introduce yourself. The goal is to provide search results swiftly and efficiently.
    4. Strive to provide the best possible results for each Query, like a dedicated legal search engine. 
    5. Use the Context provided to craft comprehensive, succinct, and user-friendly answers to the Query.
    6. Refer to results from the Context using [context-id] notation for citation. For example: 'some text [1] some other text [2]'.
    7. Do not include the full text of cited sources. These will be managed by separate software. Try to avoid citing the sources too many times.
    8. In cases where the Query relates to multiple subjects sharing the same name, formulate separate responses for each subject to ensure clarity.
    9. Utilize markdown formatting for clarity and readability.
    10. Limit responses to a maximum of 300 words to provide concise and focused answers.
    Remember, your ultimate goal is to assist users in navigating legal information quickly and accurately, in line with the mission of Illinois Legal Aid Online.`,
    }
    
    
    

This prompt may evolve as we work on improving it. We're looking to refine it by referring to the details provided in "What are tokens and how to count them?" by OpenAI.

Streaming responses in JS

When we were working on the demo, the official OpenAI JS library didn't have support for streaming responses. So, we used an alternative library called "openai-ext," which not only allowed us to implement streaming responses but also made it easier to manage the buttons' states.

API latency

Pinecone claims that some customers can get their search results in under 100 ms, but we haven't been able to achieve the same speed with our HTTP requests to the API. We're still figuring out how to reach that kind of latency.

One way could be by using gRPC implementation in Python, or maybe some other method we haven't tried yet. We're also exploring options for on-premises solutions with custom search algorithms that might give us response times faster than 100 ms.

Lately, we've noticed that the OpenAI embedding API has been slowing down. Initially, a few results occasionally took over 300 ms, but now it's happening more often. It's not as fast as we'd like for an instant search experience.

To make it work smoothly, either OpenAI needs to upgrade its servers to generate embeddings faster, or we'll need to find on-premises solutions, like using local Bert models for embeddings, which could give us an average response time of less than 60 ms.

Dive deeper into our demo search experience

Take a closer look at our demo search experience in action. You can explore our semantic search in action in two informative blogs:

Revolutionizing Search with AI: Diving Deep into Semantic Search - This blog will give you an inside look at our demo application, explaining how we implemented semantic search and built the infrastructure using Rust.

Revolutionizing Search with AI: RAG for Contextual Response - In this blog, we uncover the inner workings of RAG paired with GPT. You'll discover how we transform user queries into personalized responses that make interactions feel truly human.

What's next in AI search series

We are continuously seeking new ways to make the experience more personal for our clients and their users. Along with tackling current challenges, here are some of the ideas we're exploring:

On-premises and open-source alternatives

Our clients have diverse needs. Some prefer on-premises solutions, while others rely on open-source software. Non-profit organizations often seek a balance between the two. To cater to this variety, we're considering different technologies to expand our offerings. These include cloud-based LLMs like Azure-hosted GPTs and Claude 2, open-source LLaMA 2, vector solutions such as local Milivus, and hybrid search solutions like Typesense and Meilisearch.

Fine-tuning search experience

We're running experiments with feedback loops and using user data to personalize search results and improve the one-on-one user experience. OpenAI recently announced that their GPT-3.5-turbo model can be fine-tuned with custom data, which makes the Reinforcement Learning through Human Feedback (RLHF) approach easier and more cost-effective.

Summary

We believe that semantic search can transform the search experience, making it more intuitive and user-friendly. It has the potential to simplify the process of finding information, even when dealing with complex queries.

Our journey with AI continues to evolve through these ideas and search experiments. We are constantly striving to innovate solutions that can bring exceptional experiences to today's digital platforms, setting the stage for tomorrow's personalized AI-driven Digital Experience Platforms (DXPs).

Customer behavior has changed significantly. The micro-moments that drive preference and decision-making have the most impact on a customer journey. Businesses try to make the best use of these micro-moments by focusing on digital customer experiences.

Can the pharma industry also adopt the B2B customer journey that has proved efficient for other industries like retail?

Can pharma create connected HCP customer journeys and deliver exceptional experiences?

The marketing landscape is constantly fragmenting itself, from influencers to micro-influencers and market segments to micro segments. The target consumers refuse to be treated like fragments and instead prefer to be treated as individuals. Google reports that 69% of online consumers agree that the timing, quality, and relevance of a brand message influence their decision-making process.

Healthcare professionals are no less and expect the same level of personalization in their customer experience journeys. Modern HCPs prefer digital modes of interaction, especially after the pandemic. HCP engagement is no longer optional, it is a necessity for pharmaceutical companies. 

Evolving HCP engagement landscape

The internet and emerging technologies have empowered HCPs to access crucial and relevant medical information on multiple channels. Traditional digital channels no longer work effectively. Marketing and content are evolving day by day. Starting from engaging with HCPs on a single channel to multiple channels, then omnichannel, and now channel-less, digital experiences have come a long way.

An omnichannel strategy focuses on connecting as many channels as possible whereas a channel-less strategy focuses on the experience delivered on any channel. This progress works in tandem with the preferences of HCPs. The kind of medical information HCPs require has not changed but the timing, format, and delivery of such information have changed.

HCPs require a pharma company that provides them with relevant, authenticated, and curated medical content. The new generation of time-constrained HCPs does not have time to gather and consume copious amounts of information. They want relevant and useful content that they can consume at the right time. When it comes to effective HCP engagement, the when, where, and how matter significantly. And these essential facets are the main pillars of a connected HCP customer journey. 

Why create HCP customer journeys

The pharma industry has a direct relationship with HCPs, not the patients, and yet it plays an important role in the HCP decision-making process that impacts patients directly. A customer journey will help pharma clarify the interplay between its customers, the HCPs, and their customers, the patients. It will also align the latest customer needs with customer behavior and emerging technologies.

A customer journey also enables pharma companies to keep up with the latest developments in the industry that is evolving rapidly. Pharma companies can curate effective marketing communications targeted at a relevant audience and reduce the high costs that come with poorly targeted marketing. To sum up, a customer journey provides insight into HCP behavior and needs and empowers pharma companies to prepare themselves to fulfill those needs most effectively.

Leverage content, data, and technology

Content

The content still matters the most. What is required is content that engages HCPs meaningfully with deep educational information. HCPs desire curated medical information to understand new treatments and make better therapeutic and diagnostic decisions.

A content strategy should be a part of an HCP customer journey. All of the following will help pharma to leverage content in delivering better HCP engagement.

• Personalized and high-quality content
• Content library to engage across the whole journey
• Interactive content for multi-touch experiences
• Relevant information to cut through the clutter
• Medical topics tailored to HCP’s personas
• Content in bite-sized, downloadable, and animated format
• Rich facts, stats, and evidence for products
  

Data

Pharma companies have large volumes of data at their fingertips that can be used efficiently. Sales-based data helps to identify the sales potential of HCPs, such as the level of decision-making or the annual number of prescriptions. Behavior-based data provides insights into the differences between HCPs in their beliefs, prescription habits, or scientific appetite. Data based on content and channel preferences yield results on the level of engagement, popular channels, and modes of engagement.

A data management strategy coupled with predictive and visual analytics will help pharma companies to chart an HCP customer journey based on the insights. The following steps help to include data-driven insights in an HCP customer journey.

• Identifying rich data sources
• Assessing data quality and addressing data gaps
• Sourcing additional data if required
• Centralizing HCP customer data
• Connecting data from across the organization
• Identifying tools and capabilities required to process data
• Defining audience segments from current data insights
• Creating HCP customer personas
• Personalize content as per customer personas  

Technology

Choosing the right technology depends on two main verticals: content and channel. Content as considered above is the information and messaging delivered to HCPs, and channel means the technology that is currently used to deliver the content. Technology in an HCP customer journey needs to fulfill the following engagement criteria:

• Seamless and intuitive engagement across omnichannel
• Recognition of internal and external user workflows
• Optimized user experiences tailored to HCP interests
   

In the past pharma companies have used descriptive analytics to launch traditional business models for communication and engagement. Today, technology has increasingly progressed towards sophisticated analytics that is powered by Artificial Intelligence and Machine Learning. Predictive, visual, and prescriptive analytics transform communication strategies to deliver meaningful interaction with HCPs.

Technology should be an essential part of business strategy for pharma companies. Some capabilities that pharma requires for elevating HCP engagement are:

• A 360° view of HCPs and their customer journeys
• Integrating marketing automation tools with CRM systems
• Aggregating real-time insights across all digital channels
• Real-time engagement through social media
• Insights to identify the right mix of channels for each HCP
• An ideal blend of digital and in-person channels
  

Creating quality experiences for HCPs

HCPs will appreciate and engage fully only if pharma companies deliver quality experiences. Such experiences can transform HCPs into loyal customers who will continue to engage with pharma more in the future. While there is no one-size-fits-all experience, pharma companies can succeed if they consistently fulfill the below criteria in their HCP customer journey.  

Relevant: Interactions must be truly helpful and make the best use of an HCP’s time.

Consumable: Content must be conveniently digestible to an HCP

Accessible: HCPs should be able to access the required support on their own terms.

Connective: HCPs should be able to connect with like-minded peers to share knowledge and insights.

Purposeful: Any HCP interaction or content must authentically add to the purpose of helping patients.

Successful HCP customer journeys

QED42 has been working with multiple big pharma companies to personalize HCP customer journeys and deliver exceptional digital experiences. Consider a few examples. 

Dr. Reddy’s Laboratories

We partnered with Dr. Reddy’s Laboratories to design and build a digital engagement portal for healthcare professionals to access high-value medical resources content tailored to their various practice needs.

Results

• 1772 HCP onboarded
• 3 seconds load time
• 1 day to roll out a new website

Fortune 500 pharmaceutical company

We partnered with a top leading pharmaceutical company to create a digital engagement portal for healthcare providers to access high-value medical education resources, articles, & research papers via an easily accessible healthcare provider portal.

Results

• 40 websites rolled out in 2 years
• Rich data insights from HCP interactions
• Peer-to-peer collaboration
• Facilitated knowledge consumption

Fortune 500 biopharmaceutical company

We partnered with a huge biopharmaceutical company to deliver a seamless user experience across 3000+ websites with improved security, performance, and feature enhancements.

Results

• 3000+ websites managed
• 25+ modules audited
• Regular sites update to secure from breaches
   

If you are looking for a similar elevated HCP engagement for your pharma, plan and work on creating connected and personalized HCPs customer journeys.

According to a World Health Organization (WHO) report, approximately 15% of the global population faces some form of disability. That’s over a billion people.

That being said, the need to make education inclusive, especially at the higher levels, is more apparent than ever. However, current notions around accessibility need to be addressed first. Essentially, the term has taken on a whole new meaning in this digital age.

As of now, accessibility in higher education doesn’t just mean providing differently abled individuals with access to a competent learning experience. It’s also about ensuring that everything is shared and equal amongst different groups.

The term also takes a different meaning when viewed from the perspective of the required changes in the current educational system, with students desiring more flexible and personalized programs.

In short, accessible higher education is about making systemic changes in how institutions operate. More importantly, these changes need to build toward a more adaptable model.

Before diving into it all, let's look at what the concept means. 

What does accessibility in higher education imply?

From flexible course modules and shared learning resources to streamlined admissions and the elimination of financial barriers, accessibility has come to mean several different things in higher education.

Here's a brief glimpse into what the term stands for in the current digital age: 

It starts with admission applications

The first thing an applicant or a prospective student comes into contact with is your institution's admission portals. And designing these gateways to provide a seamless experience for all is critical.

Granted, this can be challenging to accomplish, especially considering the channels and devices users may rely on to access content. However, adhering to universal regulations, such as the WCAG 2.1 or 2.2, could alleviate some of these concerns.

The simplest way to do this is to opt for an accessibility audit. That way, you can review potential code violations and determine the priority you need to assign to a specific site or portal problem. 

Shared access to consolidated learning resources

The demands of modern-age education are split across several different categories. One of the primary ones is the availability of a centralized repository of learning resources. Fortunately, this is easy enough to implement with the help of an experienced team of developers.

Still, the first point that needs to be considered here is not related to consolidating your learning modules. That’s expected of your institution, anyway. Instead, it’s more to do with ensuring that everyone can leverage the same available resources.

More importantly, this should be provided through one access point that works equally well for all your students, regardless of whether they are differently-abled or not. 

Accurate identification of existing financial barriers

There is a misconception that accessibility is solely tied to designing innovative and approachable digital solutions. However, it often boils down to making the simplest changes to your policies.

Consider the example of student tuition. According to a Statista report, the cost of attending an in-state 4-year program in a public university was $23,250 in the academic year of 2022/2023. Meanwhile, out-of-state colleges cost around $40,000.

Now, it’s not practical to assume that the issue of high tuition fees can be addressed by merely cutting down on institutional costs. So, the easiest way to navigate this is to leverage the power afforded by Digital Experience Platforms (DXPs).

These centralized software suites enable you to analyze the online footprint of incoming student batches while measuring their performance in previous programs. Then, you could assign special grants to applicants with relatively promising records but are held back by individual disabilities. 

Flexible remote and online courses

Traditional courses have become outdated in terms of giving students the practical knowledge they need in the current job market. And as a result, the demand for flexible, remote learning modules has only increased in the past few years.

In short, your students desire shorter programs that pack more application-based knowledge. But that's not all—there is also a need for a comprehensive mentoring module. Take the example below to understand this better.

Say Prospect X has just gotten into one of your offered programs. The only issue is that they have additional accessibility requirements to keep pace with the course module. Now, if your institution doesn't provide that, all that effort into designing your systems and pages to be inclusive is wasted.

In other words, accessibility also means curating courses that are in demand while providing everyone an opportunity to benefit from them. 

How can institutions foster universal access to higher education?

While recognizing what accessibility implies is essential, it's even more crucial to put that understanding to practical use.

Here are four practical ways to do precisely that: 

Adopt a digital-first approach

With the rapid advancements that have been made in technology, it’s almost a sin to avoid leveraging the benefits that these developments bring. As such, there are several avenues you could rely on in this case.

For instance, Artificial Intelligence (AI) and Machine Learning (ML) algorithms let you identify special needs students who struggle academically. Then, by simply incorporating that data into future decisions, your institution can introduce the necessary changes to your provided modules.

Even better, the collected data can also help you recognize other related issues, such as a particular student needing on-campus accommodation owing to their existing condition.

In short, such tools enable you to make global improvements regarding institutional policies and the provision of on-site facilities. 

Create a governing body to monitor departmental issues

As implied earlier, there’s more to accessibility than driving digital change. In fact, one of the most effective ways to make your programs more inclusive is to consistently review them with the help of a governing body.

Still, it's best to include members of your faculty and your students in this group. You also must ensure that the student body is appropriately represented in this case. Once that's achieved, you can track inter-departmental issues and conceptualize shared solutions to address them.

The primary benefit of creating such a body is that the remediation process accounts for the entirety of the existing institutional population. In short, it takes care of faculty and student welfare simultaneously. 

Re-evaluate your current systems and touchpoints

It’s almost impossible to comprehensively cover all aspects when designing your touchpoints and existing systems to be accessible. That is precisely why period checks are critical to sustaining your institution's reputation.

Now, this is exponentially easier if you partner with experienced developers, such as QED42, from the very start. That way, you get access to a consolidated review process and comprehensive site suggestions, including interaction patterns, problematic page designs and seamless user navigation.

However, if you already have your digital systems in place and are reluctant to design them from scratch, you could still benefit from general consultancy services. That would allow you to recognize subtle issues that are commonly overlooked when it comes to the site or system accessibility, such as restrictive verification methods that rely on using a specific device or channel. 

Accessibility being detached from accommodation

Too often, there is this assumption that providing access to something means making room for the less fortunate. And this notion of ‘accommodation’ being attached to ‘accessibility’ needs to go for good.

Note that this has nothing to do with designing digital solutions or monitoring departmental issues. Instead, it's about implementing a change in institutional mindset. So, instead of focusing on where the student falls short, ask ‘where has the system or program failed?’

This is easy enough to understand when you draw a parallel between User Interface (UI) design and User Experience (UX). When a site visitor fails to find a particular tab, the developers always focus on why the user couldn’t find it. As such, there’s no discussion that implies the problem lies with the user’s site behavior. 

Building a shared world for all

With how far the world has come in terms of educational developments, it's becoming increasingly important to recognize the needs and requirements of differently abled students. More importantly, this recognition can never stop—it needs to be a systematic process of analyzing current demands, institutional flaws, and remediation policies.

In other words, design your enrollment touchpoints to be inclusive, create shared learning resource platforms and flexible course modules, and review some of the existing financial barriers.

Then, strengthen all the mentioned avenues by taking a digital-first approach and establishing a governing body to track and monitor existing departmental issues.

Higher education can progress in an evolving world by ensuring a seamless digital experience for everyone, regardless of individual differences.

Smart companies are adapting quickly to the paradigm shifts in the industry. Customer experience, or CX, is one such trend that has come to the forefront.

Today, nearly 45% of professionals believe that CX is a main competitive differentiator. To capitalize on this facet, offering top-notch customer experience through apt channels is vital.

As consumers leverage digital applications for several activities, brands must focus on refining their apps to provide an engaging feel. In other words, optimizing in-app CX can enhance your brand’s identity and improve relations with the target audience.

So, how can you meet the dynamic customer expectations through an improved CX? Here are some solid tips for the same. 

Develop A Unique Design Thinking Approach

Recently, the annual mobile app download figure reached a colossal 230 billion mark. This statistic is a testament to the abundant options available to modern consumers.

What does the figure mean for businesses?

The fierce competition implies that professionals should offer their customers an ergonomic, enticing, and unique CX. This way, you can sustain and improve consumer retention rates.

To begin, you should develop a sound design thinking methodology and optimize the in-app experience. Your aim should focus on these crucial elements:

• Empathize: Understand the preferences, challenges, and requirements of your end consumer
• Define: Clearly establish the aspects to enhance CX through the digital mode
• Ideate: Brainstorm the modifications with your development team and eliminate superfluous suggestions
• Prototype: Create a working app that aligns with your CX goals
• Test: Evaluate the performance using analytics and modify the desired aspects accordingly
   

Applying these philosophies to your CX refinement process can work wonders. So, before designing an alluring application, ensure that you study the target audience. Also, comparing your platform’s performance with competitor applications can prove instrumental.

Understanding the consumer is the key to building an apt design-thinking approach. Overall, this tip will help lay a systematic platform for the upcoming stages of developing an impactful in-app CX. 

Know Your User Journey from Scratch

Understanding the buyer’s persona is a critical step in customer experience optimization. For this purpose, it is crucial to analyze the following parameters:

• The goal of your product or service
• Target audience priorities
• Stages in the buying or evaluation process
• The flow of the application
   

Mapping the user’s journey on your app will create a vivid idea of the required behavior. This analysis helps in anticipating the utility of your company’s application.

The best approach to align the user’s journey with your optimization process is by referring to the design thinking outline. This way, it becomes clear to embed vital elements in your app.

Brainstorming is essential to discuss the user journey stages and make necessary changes. For instance, you might have the temptation to install automation and artificial intelligence in your app. However, discussing such critical aspects can lead to more clarity.

Even today, 86% of consumers like the human element over chatbots. So, investing all resources in automation can be self-destructive. This example shows why understanding the user journey and preferences helps refine the optimization process.

You can even address common questions that help understand the user journey in a better way:

• What do customers expect from the brand?
• Does the application meet the expectations?
• What are the areas for improvement?
   

Overall, answers to these questions can help understand the user journey and facilitate your team to take prompt actions. 

Integrate Required Modules for a Seamless In-app CX

Collaborative applications are vital to creating an engaging customer experience in your application. These modules are a blend of the following aspects:

• Source code
• Plug-ins
• Technical components
• Security modules
   

Performance goals are crucial to deciding on the integrations of such collaborative software. The main aim of optimizing CX in the application should be driving engagements.

So, embed components that refine and augment the experience of your customers. The developers should be in the loop to create such applications. This way, it becomes easy to build a chain of multiple integrated apps that can amplify the output of your communication platform.

To evaluate the quality of integration, you should use A/B testing. This technical assessment helps companies understand the ‘whys’ and ‘hows’ of visitor decisions. The following steps are crucial in A/B testing:

• Data analysis
• Split or multivariate test
• Analyze and integrate modules
• Deploy the needful changes
   

Successful implementation of A/B testing prior to app integration can help save invaluable resources. As a result, your developer team will work on the most necessary integrations with utmost focus.

In addition to a swift integration, it becomes crucial to use robust back-end support. For this purpose, you can perform the following steps:

• Optimization of screen rendering
• Minimizing features like push notifications, memory leaks
• Investing in a top-notch back-end server
• Decrease the number of URL re-directs
   

All these measures will refine your app and contribute towards the process of building exceptional customer experience. 

Leverage User Insights for Data-driven Decision-Making

Why would you work on improving an in-app CX?

Well, the answer is straightforward — customer satisfaction. You can utilize the following tools for qualitative analysis:

• Heatmaps
• Funnels
• Form Analytics
   

Each tool can offer different viewpoints. For instance, a heatmap is useful to showcase your customers' interaction with the application. The data from this tool enables you to identify the elements of the app receiving the greatest attention.

Consider heatmaps and funnels as aids in offering a visual peak of the customer journey. On the other hand, form analytics will enable you to know more about the end user.

After assessing the feedback, you can modify the application forms. Consider a hypothetical example of a customer form with the following fields:

• Name
• Email address
• Phone number
• Gender
• Overall app experience rating
• Suggestions
   

To improve your in-app CX, you can gauge the field receiving the highest responses. For instance, the ‘phone number’ option can make most users feel wary. You can alter the next feedback form by eliminating such a field in such a case.

With over 50% of the global population having mobile access to the internet, you should be prompt in installing swift analysis tools. Such data can prove helpful in deciding the following steps to enhance the quality of your brand’s application. 

Portray a Clear and Consistent Brand Message

Consistency is the key to improving your customer’s experience. Your app is the digital face that enables visitors to understand your business.

So, it would be best if you and your team become considerate about the message copies and content on display. After understanding your target audience, it becomes vital to portray your organization’s philosophy effectively.

Here are some tips to follow while optimizing your app:

• Display the products and services in a well-defined manner
• Convey value proposition and focus on enriching the user’s life
• Address customer pain points by subtly promoting your brand value
• Create short messages on your application
• Use graphics and colors that align with your brand’s identity
   

The all-popular Amazon is an excellent example of a consistent brand. Observe the smartphone application of this e-commerce giant. It adheres to the well-known Amazon graphics and portrays the brand message concisely.  

Notably, Amazon focused on keeping the in-app CX minimal. In addition, options like home, user account, cart, and integrated applications are easily accessible. Users can even use the popular voice assistant through a single tap.

Hence, the app enhances customer experience and leverages unique ways to display a consistent brand message.

A must-have ‘great’ CX with the following features can augment your brand identity:

• Simple app user interface (UI)
• Fast loading speed
• Security measures
• Search function
• Quick loading images
• Regular updates
• Social media integration
• Responsive design
   

All these aspects can help develop a fluent app, thereby amplifying the brand message. So, following such steps can always prove crucial to enhance your consumer’s experience. 

Design Personalized and Intuitive UI/UX

An alluring UI and exceptional UX are two pillars of improving your in-app customer experience. With a return on investment (ROI) of 9900%, it makes the perfect sense to focus on developing a great user experience.

Moreover, an application backed with a sound UI/UX combination can offer the following benefits:

• High user engagement
• Increased conversion rates
• Improved customer retention
   

A personalized user interface is key to improved customer satisfaction. In fact, 88% of marketers believe in prioritizing this aspect. You can stand out in the competitive market by implementing personalization for your customers.

So, how should you proceed in developing an enticing UI/UX? Here are some pro tips:

• Focus on a simple and accessible UI
• Simplify the steps in your app
• Invest in fast-loading graphics
• Install AI and chatbots for an enhanced experience
   

In fact, digital chatbots can prove to be quite useful for your UX. Consider the example of the public transportation company Amtrak. This firm utilized chatbots and improved their bookings by 25%. The self-service UX enables these bots to answer over five million user-centric queries annually.  

Overall, a well-laid UI/UX design plan can have a positive impact on refining customer experience. The following roadmap can help you approach this tip professionally:

• Development of Persona: Use online questionnaires or feedback forms to know the exact requirements of your customer
• Wireframe: Create a fundamental skeleton of the app pages considering all the required elements
• Testing for usability: Find the bottlenecks of the UI/UX design before releasing your application
   

An app with user-centric UI/UX will definitely enhance customer experience. Soon, you will notice fewer bounce rates and more retentions. 

Evolve the App to Suit Changing Customer Behavior and User Journeys

A customer-centric mindset enables you to understand the changing priorities of your target audience. Since modern users have access to a plethora of options, it becomes tough to retain end consumers.

For this purpose, you should keep on modifying the app periodically. You can utilize a performance management system to predict the satisfaction levels of your consumers. Machine learning can prove vital in this case.

To optimize your in-app CX periodically, evaluate the feedback on several fronts. Interaction is the key to developing an intuitive application. Here are solid reasons to gauge customer feedback:

• It helps prevent the release of an unsuitable app
• You receive a clear idea of customer expectations
• The team can use feedback to generate new ideas
• Feedbacks drive the research and development process
• You are more aware of changing consumer preferences directly
   

It is also crucial to monitor your app performance regularly. The relevant analytics will enable the implementation of the following facets:

• App uniformity
• Interactive pages
• Relevant features
• Customer-centric UI
   

With an average attention span of eight seconds, it is challenging to meet the dynamic consumer demand. In addition, the attention span decreases by 88% every year. So, digital apps need to evolve accordingly.

Feedback and constant monitoring are essential to refining your CX periodically. Along with the technical aspects, you should also be aware of the cognitive, social, and emotional parameters. This way, it becomes easier to implement new changes and integrate suitable modules.

Remember, the user journey will keep on changing every year. Hence, it would help if you keep up with the pace to optimize the in-app CX. 

In A Nutshell

The scalability of modern apps is a crucial factor. However, in addition to the number of users, you should also focus on customer satisfaction.

Retention of your target audience requires careful planning of in-app CX. For this purpose, you should begin by developing the right design thinking approach. Next, it is crucial to understand the user journey and integrate associated modules.

This way, it will become easier to optimize the in-app experience. Feedback, data, and regular monitoring are essential to fine-tuning your company’s app. Due to the changing customer preferences and shorter attention spans, you need to make quick decisions.

So, always invest in suitable UI/UX and evolve the app accordingly. At the same time, ensure that you adhere to the brand identity through proper tone of voice, colors, and content. It is always pivotal to prioritize customer experience and get the best returns over your developed applications.

As of today, there is nothing more important than curating and delivering personalized experiences. 

This is especially true when it comes to marketing. In fact, a Statista report states that over 60% of online shoppers would switch to a different brand when faced with non-customized content from their current vendors.

In other words, in a world driven by data and subliminal messaging, the importance of delivering highly curated promotional material has never been any higher.

But how do you do that in education? More importantly, is it possible to customize an experience for hundreds of thousands of potential students and alums?

The simple answer is yes. And the secret lies in Customer Data Platforms (CDPs).

These packaged software tools create a unified view of a prospect or consumer by pulling the data from multiple touchpoints. Then, the information is collated, cleaned, and fed into other marketing channels.

The result? A persistent customer profile that works across all promotional avenues.

With that out of the way, let’s talk about how a CDP can help educational institutions and marketers improve their enrollment rates. 

How can you use a CDP to improve enrollment rates?

Notwithstanding the technological advancements that have made CDPs possible, these platforms are essentially tools at the end of the day. And, just like any other tool, what matters is how you leverage them.

So, here’s what you can start with: 

Defining the initial student cohorts

Like all global verticals, Education (ED) marketing is defined by its market segments. The only difference is that a specific term is used to refer to such groups—cohorts.

Simply put, a cohort is a broad student group currently or will be enrolled in an institution. Now, each of these groups has particular behavioral patterns. 

For instance, batches from a school in Massachusetts may prefer Ivy League colleges. As such, the cohorts from that institution could have a high initial drive when sending applications to Brown, Colombia, or Harvard.

Conversely, Texan students, with their robust community colleges and public university systems, could prefer local institutions. 

That’s just one example, though. The idea here is to analyze the broader digital footprint, including past enrollment rates, annual application volume, and graduation rates, and then feed that into your CDP. Following this, you set parameters to define a cohort’s response to your ED marketing pitch.

Now, this is easily achievable through Artificial Intelligence (AI) and Machine Learning (ML) platforms. In fact, QED42 provides services that cater to this specific need, helping set up a system that incorporates the collected information into a consolidated channel.

Remember, you aren’t just dealing with data from public directories or records in this instance. Most of this information will come from the internet. And that requires a single, streamlined approach to navigate it effectively. 

Establishing a standardized prospect profile

Now that your cohorts are defined, it’s time to create a general outline for the prospect/student lifecycle.

Your CDP will already have the necessary information to do this. In short, all that data you collected in the pre-enrollment funnel, including the number of direct from-school transfers, and overall course competition rate, will help you here.

For instance, say a specific student group comprising adults aged over 25 (Group X) has a low graduation rate. This could be due to factors you have no control over, such as individual professional and personal commitments, lack of financial resources, etc. 

However, that doesn’t mean that this cohort is beyond retention. By leveraging the data in your CDP, you can identify the touchpoints that Group X used in initially contacting the related institution. Then, you compare those access points against the prospect profile that you have already built.

While you are at this, ensure that you check a few things. So, is there a disparity between what Group X seeks and what the course offers? Does the site content even provide enough clarity on the syllabus?

These elements are the primary determinants of prospect retention in ED marketing. More often than not, advertisers tend to emphasize campus facilities and accommodation amenities while pitching a particular institution. These are also crucial deciding factors, but they are secondary considerations regarding what the modern-age student wants. 

Understanding engagement metrics

Successful ED marketers always have a comprehensive overview of what type of promotional material is most effective. More importantly, their campaigns deploy different kinds of content based on where a prospect is in the conversion funnel.

So, say a potential student is just beginning to consider a particular college. What will be the first point of contact for that prospect? It will probably be site material, brochures, and other written content.

As that prospect progresses further down the funnel, their requirements may change. Now, they might desire a more insightful look into the institution. This will primarily come from campus tours, in-person events, virtual faculty, and batch meetings.

Whatever the case, the truth is apparent: the data from your CDP for specific market segments will only work in designated stages. To circumvent this challenge, you need to ensure that you are consistently updating the collected information from your current engagement metrics.

This shouldn’t be too difficult, considering that you have already established a standardized prospect profile based on cohort behavior. 

Enforcing data privacy compliance

A prominent concern in most marketing campaigns is the issue of data privacy compliance. In fact, 93% of Americans want more control over their personal information.

This is where a CDP truly shines.

Typically, these systems have integrated support for several privacy regulations, laws, and policies, including CCPA, GDPR, and CPRA. But that’s not all! Prospects have complete control over the information they submit due to the affiliated touchpoint being opt-in channels.

Essentially, marketing teams get access to relevant prospect details while being assured of their responsiveness to promotional material. There’s also the fact that incorporating a CDP into your ED campaigns will enable you to deploy strategic protective provisions, including the Right of Disclosure and Right of Deletion, for all your leads.

Finally, a CDP allows marketers to consolidate prospect preferences across multiple channels. This is especially useful when tracking what a potential student has opted out of and then curating a pitch based on that.

Of course, it also helps institutions and related marketing teams avoid legal liabilities when adhering to data privacy policies. 

Curating diversified programs based on prospect data

There’s no such thing as a ‘perfect’ course. As of now, each cohort, down to its very last member, wants something different when it comes to educational offerings.

In short, the need to create more engaging modules to provide an enhanced learning experience is higher than ever. Fortunately, a CDP can help you tackle this challenge.

Consider what has been discussed so far. You have defined your cohorts, set broad standard prospect profiles for each of them, understood what material they engage with, and finally, have a consolidated view of what they have opted out of.

These four elements will help you create course modules that appeal to specific groups. Take the example of Group X from earlier. This particular cohort has a low graduation rate, has external commitments outside the institutions, and only relies on touchpoints that offer clarity.

Now, by combining and analyzing those factors, you could start a new 6-week course on any given subject that caters to Group X’s behavioral patterns. It’s as simple as that.

What should you be mindful of when adopting a CDP?

It’s easy to get carried away with all the enhancements that a CDP will bring to your marketing funnels. However, there are specific things you must guard against.

First, your institutional departments need to recognize the necessity of sharing the collected data. Most often, departmental members tend to be somewhat reluctant when it comes to this. However, failing to analyze every bit of information against the data collected from other sources will only lead to redundancies in your current strategies.

Second, it never helps to isolate a CDP into one specific group. In short, the generated reports should be available to all members across the organizational hierarchy. That way, everybody has a single view of the truth.

Finally, it is imperative not to view a CDP as being a tool for merely improving statutory rankings. Doing so would be detrimental to fostering an inclusive educational experience in the long run. And no amount of creative and collaborative marketing can remedy that issue.

There is a simple example to understand the importance of all three mentioned elements.

Say, University Y is nearing admission season. Now, each department, including the admission body and marketing team, has different data sets they rely on to create their strategies. Additionally, the board of directors is the sole governing body with access to the reports generated by a CDP.

Do you see where the problem lies? If this specific instance persists, it will translate to a fragmented ED marketing pitch. And that, undoubtedly, will not help your enrollment rates. 

Delivering exceptional experiences through data

From consolidated prospect profile and cohort parameters to driving better engagement and ensuring data privacy compliance, a CDP will deliver on every aspect of your ED marketing campaign. The best part? It’ll even tell you which courses work best without you even attempting to foray into that area.

However, it's essential to understand the common pitfalls associated with implementing such platforms. In short, beware of siloed databases, departmental or otherwise, avoid isolating a CDP to one group, and always ensure that the data is being used to do more than just drive enrollment rates and college rankings.

Remember data and volume go hand in hand. If you are looking to enhance student experiences at your institution, bring these two together in a single streamlined system with intelligent and innovative digital solutions.

Students' expectations from university, especially regarding online interactions and courses, have increased significantly. They expect and demand education that reflects their preferred learning styles, interests, and continued relevance to the professions they wish to pursue.

They want the same modern, fluid, seamless, and personalized experiences on campus, in university offices, and with their professors.

Research shows that 94% of students believe connecting with faculty, and other students, receiving course suggestions, and getting notifications about deadlines based on academic performance and interest would help them feel more connected to their institutions.

With many students now attending college classes remotely, potential students are rating digital interactions at the beginning of their college search as the run-through for the learning experience.

Hybrid and online learning has become the new norm. A sharp drop in the digital experience quality after a student enrolls could affect an institution's reputation and, ultimately, a student's experience.

To succeed in this new landscape, colleges and universities must rise to the occasion and provide a high-quality digital experience for all students, both remotely and on campus. In this blog, we'll explore how a Digital Experience Platform (DXP) provides educational institutions with access to information and applications while offering a new level of personalization to understand better the needs of their students, alumni, staff, and community members. 

Understanding personalized university journeys

Students are looking for a meaningful digital experience that is more personal and relevant, so any differentiator, especially when it comes to personalizing content, can make a huge difference.

University and college websites and apps fail to create a more personalized user experience. Around 58% of students reported that of all the brands they engage with, their institution is furthest behind in personalizing their experience.

Universities need a way to syndicate content across all channels and understand user preferences to better engage with their students.

To understand personalized journeys, we need to look at what end users expect:

• Personalized support and information
• Easy access to information
• They want to avoid too many frustrating barriers

For higher education institutions to achieve this, they must understand their users, create relevant and exciting content, and personalize it. This process should then be automated. 

DXP: Solving higher education’s personalization problems

DXP unifies the siloed systems for managing websites, landing pages, analytics, email campaigns, and CRM-powered content.

DXP enables personalization by defining audience segments with robust rules for fine-grained personalization and engaging these segments with a wide range of personalized experiences across pages, navigation, and assets.

This allows for a much more customized and individualized user experience tailored to each student’s needs and interests.

DXP provides a differentiated digital experience and offers personalization 

Meeting student needs

Each student gets their profile, populated with data from all channels and devices, that helps them to get personalized experiences in real time!

Further, a DXP can

• Segment audiences based on user behavior, location, and role

• Deliver personalized content and web pages to specific segments

• Aggregate student data across silos

Meeting marketing and communication needs

Engaging users with relevant and targeted content can increase conversions

DXPs provide personalized user experiences and actionable content based on analytics-driven insights. With this, users can access all decision-aiding tools, predictive recommendations, and relationship-enhancing features.

Universities can leverage built-in analytics to track customer engagement metrics, calculate ROI, and leverage A/B testing, web analytics, and social analytics to continuously test the effectiveness of the experience. The insights are drawn from metrics to improve user experience and to make informed decisions

Meeting teachers and staff needs

It offers a connected digital tool to deliver personalized news feeds to improve overall productivity and encourage collaboration through blogs, forums, and knowledge management.

DXP can personalize experiences based on different roles and permissions. For example, a user with the administrator role can have a different experience than a user with the role of teacher. 

Bringing collaboration

Teacher, partner, and team member collaboration is essential for fostering engagement. Collaboration enables organizations to innovate, collect data, and provide better customer service in the education sector. 

Converting a visitor into a brand advocate

Through personalized content and seamless omnichannel access, DXPs could develop deep engagements with customers. DXPs can convert a short-term transaction-based customer interaction to a long-term strategic relationship through user engagement features. 

Predictive insights

Omnichannel analytics-driven insights can be leveraged to offer personalized recommendations. Based on trend analysis, business stakeholders can predict future trends, such as seasonal traffic spikes, holiday traffic size, etc., and be prepared to handle the scenarios.

DXP: Providing unified solutions for universities 

Stanford Graduate School of Business

QED42 has redesigned GSB’s web and mobile experiences with an intuitive UI. Delivering a flawless experience for course selections and information access and elevated their mobile experiences.

Results:

• Enriched digital experiences for GSB's aspiring and current students, lifelong learners, alums, and staff
• Elevated and simplified the course selection experiences for students with modernized platforms
• Redefined their mobile experiences for students and faculty with a revamped information architecture
• Powered GSB's non-technical staff to create, launch, & manage pages and content independently

University of London

The University of London adopted a DXP to enhance its student portal with a responsive design and engaging experiences for its students.

Results:

• Improved student experience for over 170,000 students across the globe
• Empowered business users to manage experiences independent of IT
• Increased student engagement by offering a consistent experience across multiple touchpoints

Griffith University

Griffith University designed a personalized experience using DXP to engage new students with a digitalized orientation process.

Results:

The MyOrientation app.

• Guides students through the various activities they must complete before arriving on campus
• Able to track all students' orientation progress
• Administrators are able to send tailored communications to provide the necessary support
• Tailor a personalized experience quickly based on what it already knew about students
• The plug-in components enabled the university to provide automated first-day planners, drawing information from students’ existing attributes

Capella University

Capella University is a leading online university that needed a new platform to integrate all of its technologies for a unified, personalized, intuitive user experience. They built a tool using DXP to support their student's academic growth.

Results:

• Displays real-time academic activity for each student through personalized course dashboards
• Students were able to customize their Degree Completion Plan
• Integrates LMS, student resources, and grades on one platform

DXP: Achieving digital excellence at universities

The digital landscape is constantly changing, and educational institutions must keep up with the pace of change to remain competitive.

Students are looking for more value, innovation, and interconnected digital experiences from higher education institutions.

Leverage DXPs to achieve digital excellence and drive deeper engagement with your students, faculty, alumni, and prospective students. 

With businesses focusing on delivering personalized digital experiences, content becomes a key factor. Content marketing is a crucial aspect to establish a brand presence. Businesses can engage meaningfully with their audience with compelling, unique, and visually attractive content.

Businesses require the capability to publish powerful content faster, on a large scale, and at reduced costs. One way of making this possible is by enhancing their editorial experience. How can you enhance your editorial experience?

Drupal, one of the most popular open-source CMSs in the market, continues to evolve to deliver the best editorial experiences for its customers. Starting from building Drupal pages on nodes to modules, page layouts, paragraphs, and Layout Builder, Drupal has come a long way in empowering content editors.

Drupal 10 is all set to release in December 2022, and with it comes a modern, and versatile WYSIWYG editor, the CKEditor 5. What are the exciting features of CKEditor 5? How does it enhance the editorial experience? 

CKEditor 5 – Collaborative editing features

CKEditor 5 will be the default editor in Drupal 10. The new version was completely rewritten from scratch and took almost two years to complete. CKEditor 5 eliminates all the older code issues that were present in CKEditor 4. CKEditor 5 seamlessly integrates with Drupal 10 and guarantees the best editorial experience for developers and users. 

The CKEditor 5 architecture is designed to encourage collaboration and empower multiple authors to work asynchronously on the same rich-text document. Drupal 10 users can take advantage of the core editor features and the new premium features (which need to be purchased additionally) to boost their content editing experiences.

Consider the premium features available in CKEditor.

Track Changes

Edit content in suggestion mode. Users can accept, edit, or reject the suggestions. 

Comments

Add, delete, or edit comments. Users can start discussions with comment threads. 

Revision History

Create and view document versions in a preview mode. Users can compare, rename, and restore document versions. 

Real-time Collaboration

Combine comments and track changes features to enable real-time collaboration. Users can enable an all-around co-authoring experience. 

Import from Word

Import unlimited Word documents. Users can utilize features, such as track changes, comments, and suggestions on imported Word docs. 

Export to PDF and Word

Export your content into portable Word and PDF formats with a single click. Users can export content in the same design as the original content.

Spell and Grammar Check

Correct grammatical and spelling mistakes. Users can use this feature while creating content or in a separate dialog to polish the writing. 

Pagination

View how your document is laid out for printing. Users can organize lengthy documents by viewing the number of pages. 

CKEditor 5 – Modern writing and editing

Rich text editor

CKEditor 5 comes with an ultra-modern JavaScript rich text editor. With features, such as drag-and-drop, autoformatting, tables, lists, autocomplete, and block quotes, writing and editing content becomes an effortless task.

Customized implementation

It is easy to create a customized rich text editor with CKEditor 5 using the online builder. Currently, CKEditor 5 is the only collaborative rich text editor that provides a ready-to-use solution, with a backend on both on-premises and cloud. The modular architecture of CKEditor 5 makes it easy to make any further customizations as per business requirements.

Collaborative editing

The CKEditor 5 architecture is designed for collaborative editing from the ground up. The import from Word feature means editors do not need to switch between apps to paste content. Integration to an email marketing CRM means marketers can easily report the content and get feedback without using any third-party application. Authors and editors can simultaneously work on the same document, saving time and boosting efficiency.

Scale and growth

CKEditor 5 promises better experiences, happy users, and bigger growth. The CKEditor 5 takes content editing to the next level with rich core features and premium collaborative features. Being the default editor, CKEditor 5 will grow and scale with Drupal 10 to deliver a full-fledged editorial experience to content teams. 

CKEditor 5 - Enhanced experiences

Enhanced UI & UX

CKEditor 5 comes with a well-designed UI and perfect UX for users to manage content creation and editing seamlessly. Enhanced UI and UX include intuitive image insert with automated uploading, simple linking without complex dialogs, autoformatting, a new visible toolbar while page scrolling, and easy styling with the content placed inline in the page.

Customizable & extensible

The CKEditor 5 core is open for reuse and extensions enabling developers to create customized editors with rich features. The CKEditor 5 is implemented as multiple npm packages with a separate repository for each package. It makes it easy for others to contribute and focus on each feature separately.

New data model

The CKEditor 5 comes with a much more efficient data model that is part of the MVC architecture. Defined and controlled by JavaScript, the data model provides more control over the data output and data format.

Modern

The CKEditor 5 is totally rewritten in JavaScript ES6. It provides all the necessary integration tools to connect with modern apps, such as React, Angular, Node.js, npm, etc. CKEditor 5 is a modern rich-text editor with 100% code coverage providing the best quality assurance. 

CKEditor 5 – Next level of content editing

Drupal is always in search of the best editorial experience and keeps evolving to use the latest technologies to achieve that purpose. Drupal 10 with rich features, such as the CKEditor 5, empowers organizations to deliver personalized content.

Businesses all around the world using Drupal testify to the enhanced editing experiences they enjoy. The CKEditor 5 takes content editing to the next level to deliver exceptional digital experiences.

Enjoy the full benefits of CKEditor 5 by upgrading your website to Drupal 10!

The latest major version of Drupal, Drupal 10 is set to release on 14 December 2022. Drupal 10 is built on Drupal 9 as a part of the methodical approach adopted from the release of Drupal 8. Instead of a complete codebase overhaul, new functionalities are added to the later Drupal 9 versions, like the Drupal 9.4. This approach enables Drupal to deliver new value without disruption and gives developers sufficient time to update future API changes.

Getting ready for Drupal 10

Drupal users should keep an eye on current updates. The move from Drupal 9 to Drupal 10 is estimated to be easier than upgrading to Drupal 9 from other old versions. The steps to switch to Drupal 10 would be similar to the steps required to upgrade to Drupal 9.

You need to keep your website ready for the switch now to make the move easier when Drupal 10 is available. Keep the Drupal core updated and note the modules that are compatible or no longer compatible with Drupal 10.

Use the available tools to estimate how much work is required to switch to Drupal 10.

• The Drupal Upgrade Status module helps to find the deprecations to fix. You can estimate the effort you need for the switch.
• The Drupal Rector app helps to automate the process of removing deprecations.
• The Drupal PHPStan module, a static code analysis tool, helps to analyze the code for deprecations.
• The Drupal Check module helps to check static code for deprecation.

The switch from Drupal 9 to Drupal 10 will be more automated with a lesser turnaround time. It will be effortless due to faster iterations and fewer minor versions in Drupal 9.

Preparing for Drupal 10

1. Upgrade to Drupal 9, if not done already
2. Install Drupal Upgrade Status
3. Check whether contributed modules need an update using Administer > Reports > Upgrade Status
4. Find out the deprecated APIs to fix in the custom code
5. Ensure all deprecated APIs are detectable using the latest Drupal 9 release
6. Use Drupal Rector to fix various issues in your custom code automatically

Upgrading to Drupal 10 from other versions

Drupal 9

Drupal 9's end of life is in November 2023. You will no longer receive bug updates or community-supported security updates for Drupal 9 beyond November 2023. The EOL of Drupal 9 also means the EOL of Symfony 4 and CKEditor 4.

You will have 11 months from the release of Drupal 10 to migrate. With Drupal major versions getting easier with each release, upgrading to Drupal 10 from Drupal 9 will be an effortless upgrade.

Start updating contrib projects and custom code now.

Drupal 8

Drupal 8's end of life was in November 2021. If you are still using Drupal 8, you are no longer receiving community support for bug updates or security updates.

Migrate to Drupal 10 upon release or migrate to Drupal 9 and then upgrade to Drupal 10.

Drupal 7

Drupal 7's end of life is in November 2023. While Drupal 7 is still supported by the community, the support for contrib projects has decreased. If you are still using Drupal 7, you are missing out on more than a decade worth of technical innovations and current technologies as Drupal 7 was released in 2011.

Migrate to Drupal 9 now as it is a complex migration involving rewrites of custom code and replacing many contrib projects.

Drupal 6

Drupal 6 end of life was in February 2016. If you are using Drupal 6, you are no longer receiving community support. Migrating from Drupal 6 to Drupal 9 is the same as Drupal 7 to Drupal 9 migration. It involves a complicated process, and it is better to start your migration process now.

Migrate to Drupal 9 and then upgrade to Drupal 10.

Drupal 10 Readiness initiative

The Drupal 10 Readiness initiative oversees the release of Drupal 10. Its main priorities are to

• Support contributed module maintainers while modules are updated
• Track the tasks required to release
• Update dependencies and remove deprecated APIs
• Ensure Drupal 10 is released in December 2022

Keep an eye on the Drupal 10 Readiness initiative to keep yourself updated with the Drupal world.

We’re here to help!

You can start preparing your website now to migrate to Drupal 10. Check out how the exciting new features of Drupal 10 with the enhanced existing features pave the path to creating exceptional digital experiences for your customers across all touch points.

Drupal 10 is all set to release on 14 December 2022. Since Drupal 8, the Drupal community has taken a methodical approach to release new versions of Drupal. This approach allows for releasing new features within a major release and provides a clear path to update to the new major versions more seamlessly.

Drupal 10 was supposed to release in August 2022 but was pushed to December. There are 2 significant reasons for it.

Drupal 10: Why releasing in December

Drupal 10 will be using CKEditor 5 and integration to CKEditor 5 is the most crucial aspect. CKEditor 4 will be deprecated by the end of 2023.

Core developers with CKEditor teams have collaborated and invested numerous hours in integrating CKEditor 5 into Drupal. There were additional critical issues discovered with the work done and those issues need to be resolved to make CKEditor 5 stable. The issues could not be resolved by the 13 May beta deadline, and is the first reason why Drupal 10 was not released in August 2022.

The second reason is that a December release means more time for the Drupal community to stabilize CKEditor 5. Site owners can move content from CKEditor 4 to the new version in Drupal 9 securely. Drupal 10 requires PHP 8.1, and hosting providers have time to support PHP 8.1 for sites to update. PHP 8.1 will be the minimum requirement for Drupal 10 until November 2024. PHP 8.2 version will release in November 2022 and will be compatible with Drupal 10.

The whole Drupal community has worked hard to complete the Drupal 10 release date and goals. They have been working to eliminate deprecated code, update JavaScript, and prepare modules for contribution.

Drupal 10: Deprecated code removed

All core code and libraries in Drupal 9 that are identified as deprecated will be removed. Deprecated code refers to the code that is no longer used as it has been improved and enhanced. Removing the code immediately affects the functionality of a site’s custom code, hence, the code is marked ‘deprecated’ to inform that it will be removed in the next Drupal major version. The community will get sufficient time to update their code to be compatible with Drupal 10.

Some external code and libraries have end-of-life. Symfony 4, used in Drupal 9, reaches its end of life in November 2023. Drupal 10 will use Symfony 6.2, which is the latest version with bug issues resolved and improvements from Symfony 6. The Symfony framework provides major functional capabilities to websites, namely routing and handling incoming requests, and managing cookies. The entire list of deprecated code and libraries are listed on the Drupal website.

Drupal 10: Core modules removed

A few redundant core modules will be removed from Drupal and moved to the ‘Contributed Module’ for continuity. Removing modules that are not used makes Drupal core leaner and easy to maintain.

The following modules are likely to be removed.

• Activity Tracker
• Aggregator
• CKEditor
• Color
• Forum
• HAL
• QuickEdit
• RDF

According to user surveys and statistics, some of these modules were enabled by default but hardly used. In addition, some JavaScript dependencies will also be removed. Some uses of jQuery will be cleared which will reduce the past risks of jQuery and jQuery UI’s security processes.

Drupal 10: Upgrades

Upgrades enhance site performance and user experiences for all users. A few major third-party components are scheduled to update from Drupal 9 to Drupal 10.

• CKEditor: Version 4 to 5
• Composer: Version 1 to 2
• PHP: Version 7 to 8
• Symfony: Version 4 to 5/6

With Drupal 9 released in 2020, and Drupal 10 announced in 2021, site developers have a year to prepare their Drupal sites for migration. The third-party upgrades help improve the overall Drupal experience for all.

Drupal 10: What’s new

Drupal 10, a polished version, comes with some exciting new features.

Claro

The ‘Seven’ theme designed for Drupal 7 in 2009 was giving out an outdated system impression. The new ‘Claro’ theme replaced Seven and has been designed as per the latest standards.

Olivero

‘Olivero’ is the front-end theme designed to work with user-popular features, such as the Layout Builder. The Olivero theme will be compliant with WCAG AA.

Auto-Updates

The ‘Auto-Update’ feature will be available on composer-based sites and will enable the Drupal website to update automatically and securely.

Decoupled Menus

The ‘Decoupled Menus’ feature helps to build small web components that address a common use case. The feature will help to create a large repository of web components rapidly.

Project Browser

“Project Browser’ will help site builders to find and install modules from the admin dashboard. It will be available as a contributed module only at present.

Starter-Kit

The new ‘Starter-Kit’ theme will replace the ‘Classy’ theme. The new theme will not affect the production themes initially and will be easier to maintain.

Drupal 10: Built on Drupal 9

Drupal 10 is built on Drupal 9 instead of a complete codebase overhaul. New functionalities are added to the later Drupal 9 versions. Each minor release until Drupal 9.4.0 will be backward compatible and gives the community ample time to stay updated with future API changes.

This methodical approach allows Drupal to deliver new value every six months without disruption. Once Drupal 10 is released in December 2022, all deprecated code will be removed, and dependencies will be updated. The strategy is the same as the successful Drupal 8 to Drupal 9 migration strategy that helped contributed modules and extensions to become compatible with the new version much sooner.

Drupal 10: For the future

Drupal is much bigger than a CMS and has made the web much better. Each new version aims to enhance development more. Agile infrastructure is essential to deliver exceptional digital experiences and Drupal 10 promises to deliver the same.

Major brands and organizations use Drupal to boost their audience outreach capabilities. Drupal 10 with feature-rich modules will empower organizations to create unique customer experiences across all touch points. The themes and content editing experience will evolve with Drupal 10 making content creation, management, and delivery seamless and meaningful.

Generally, a framework means a combination of rules and standards to follow for the best results. Similarly, an automation framework is a platform that combines tools and practices to run automation tests. Simply put, it provides a complete environment for your development teams to execute automated test scripts successfully.

The significant components in an automation framework are testing tools, procedures, scripts, libraries, and data management. These components facilitate test executions and comprehensive reports of test results.

The guidelines of an automation framework could include coding standards, repositories, data handling methods, results storing processes, or info on accessing external resources. While these guidelines are not mandatory for every automation framework, they make automation testing more organized with additional benefits.  

Automation framework: Accelerating the testing process

Let’s explore how an automation framework can improve testing speed and efficiency.

Maximum test coverage

QA teams can add new scripts to test more features or add in-depth scripts for complex use cases to an automation framework. Your team can execute numerous tests to cover all features and requirements in every test cycle.

Less manual intervention

There is no need for your QA team to input test data manually or monitor the test script for each test. An automation framework will run tests perfectly each time and prevent discrepancies between different coding standards.

Reduced costs

An automation framework is cost-effective as it enables repetitive and parallel testing. It also allows for frequent testing of new features or changed objectives per your business need without extra costs. You can reduce significant costs that accompany changed scripts or bugs in a production pipeline.

Standardization

Coding patterns are different within a team of developers, making multiple testing a necessity. An automation framework consolidates all coding data to enable easy and consistent scripting and reduce duplicate coding.

High scalability

As your project scales in growth, there would be a need for upgrades, new features, and changes. An automation framework will grow with your project and fulfill all these necessities in a short time while maintaining all the required parameters.

Reporting

An automation framework provides a clear and comprehensive report for every test executed, every bug discovered, every script written, and more. Your QA team can extract every inch of information and derive insights for assessing the project’s goals, and organizational goals.

Modularity

An automation framework helps to break down the testing process into smaller, more manageable modules, making it easier to maintain and update the test suite. You can categorize and store specific test data in external databases and configure them quickly, as the framework covers all sorts of apps in the process.

Reusability

Writing effective test scripts takes time and effort, and an automation framework makes it convenient with reusable scripts. You can reuse test scripts as many times as you need, even with cross-browser testing or a change in the device OS.

Integration

An automation framework can integrate with other tools and technologies, such as continuous integration and deployment (CI/CD) pipelines. You can streamline the testing process and reduce the time to market with such powerful integrations. 

Automation framework: Progressive automation testing

With the automation industry progressing immensely, your business can build high-quality applications and meet the increasing security standards with an automation framework. You can make your development process more efficient and achieve optimal testing speed.

Consider, QED42’s open-source Selenium framework. It accelerates automation testing by 30% and increases test accuracy to 90%. Being open-source, Headway is free to use thereby reducing costs to a greater extent. It is available for download on GitHub.

The demand for rapid product development is increasing and driving quality standards higher and higher. Automation testing has transformed from a “good to have” component to a critical component in delivering best-in-class software quickly. The latest technologies have added more capabilities to test automation like codeless automation for example can be considered the future of modern testing.

When it comes to businesses, automation testing increases development speed and adds flexibility to development pipelines. While decision leaders accept test automation as a great idea, they hesitate to adopt it into the business. Reasons range from the uncertainty of costs involved to whether the costs justify the value delivered.

Your development team may be thinking about automated testing for a new project. Would it be productive for the project in the long run? Would your team be more productive with fewer manual regression tests? Can your business derive quantitative benefits from test automation to justify the investment?

We will consider all these questions and more in this blog. You will understand how to get the best ROI and some best practices to optimize automation testing. 

Top business advantages of automation testing

Quick delivery

A major challenge in software development is fixing defects. Longer delays between development and identifying defects increases time, expenses, and efforts to fix them. Test automation creates a tighter feedback loop to reduce the fixing time from weeks and months to minutes. Your business will not miss out on market opportunities due to testing holding up development. You will reduce defect costs by a large margin with automated testing.

Identify more regressions

It is typical to identify more regressions after adopting automation testing. While more regressions may seem like high costs for your business now, regressions found by users after your product is launched in the market tend to cause severe losses for your business. In the past, developers may see a failed test in the CI environment, identify the issue, and fix it quickly without documenting it. Test automation captures all defects and reduces the fixing time drastically enough to justify the regressions found and documents it for future insights.

Highly testable products

Test automation makes testing time trend downward over significant periods leading to qualitative and quantitative benefits. Automated testing enables better test coverage with minimal manual intervention leading to better testable products. Your business can test such products in more ways, reduce the risk of missing use cases in testing, and go to market rapidly with quality applications.

Early evaluation

Shift-left refers to the practice of evaluating quality, testing, and performance early in the development cycle. This practice is crucial to a business as it speeds up development efficiency and addresses any defects early in the development cycle before your apps get into production. Your development teams can deliver applications or software more frequently with high quality.

Less slow regression

Regression is slow and does not help identify significant issues. Automated checking is more beneficial than manual checking as the performance is the same anytime and every time it is done. The procedures don’t change, the costs are lower for repeated testing, and your teams can easily identify and document a regression any time it happens.  

Avoiding test automation pitfalls

A significant aspect in determining the ROI of automation testing lies in how your business approaches the implementation process. Automation testing has evolved enough to identify common pitfalls, and your business would benefit by avoiding these mistakes. Let’s consider a few of them. 

Ignoring manual testing

While test automation is highly beneficial, it will not fit all scenarios or processes. The value of automated testing lies in running tests multiple times, but all cases do not require automation. If your development teams are working on diverse products, features, or processes, you may require manual testing in some cases along with automated testing. Your teams need to discern scenarios that require automated testing and scenarios that require manual testing to save your business money in the long run.

Short-term calculations

Your business needs to plan long-term ROI other than the initial costs and short-term calculations. You need to consider some questions that will help evaluate test automation ROI for now and in the future. Would automation testing help solve more issues for your company in the long run other than reducing QA time? Would it open new opportunities for your business to expand and grow? Will automated testing help to improve the quality of your products and development?

Excluding test maintenance

Automated testing is still based on coding and requires maintenance and upgrades if you want to derive value from them for years. Test maintenance takes time, so your business should consider it while planning implementation. Significant code changes might take away most of your development team’s time. Would your team be able to handle it? Or do you have a third-party partner doing test maintenance for your business? Do you want your business to handle long-term test maintenance? Answering these questions in the initial stage would help your business get the best ROI of test automation.

Not documenting

Documentation is essential to calculate automation testing ROI. Document everything your team does. Even if you have a key developer dropping out, your team will not be at risk of losing all progress or having to re-engineer multiple complex cases with accurate and detailed records. Proper documentation eliminates complete dependence on human expertise and ensures only necessary dependence just like test automation. 

Parameters for advanced test automation

The basic formula for calculating ROI is universally applicable - (ROI = Cost Savings-Investment/Investment). However, the components, cases, metrics, and best practices may differ from business to business. The top three significant parameters that would apply to any business are:

Cost

The most important parameter in automation testing ROI is the cost. A critical decision revolving around automation testing is the investment in infrastructure. Factors like framework, test suite, and cloud hosting have a direct impact on the outcome of automated testing. While it seems expensive initially, it pays off in multiple long-term benefits.

Opting for test automation on the cloud ensures a scalable and secure infrastructure that is available to run tests at any time, regardless of the tester’s location. Benefits, such as reducing critical man hours or savings due to investment in in-house infrastructure result in huge cost savings in the long run. Remember that ROI on automation testing will not be evident right away, but once your business reaches breakeven, you can see the benefits.

Quality

 Test coverage and accuracy are the significant value propositions for automation testing. A higher test coverage means a high probability of detecting bugs quickly, resulting in a high-quality product. You should align automation testing with integrations, such as CI/CD tools, product management tools, and bug tracking tools to expedite the process of defect discovery, issue tracking, and issue fixing.

Execution speed, test reporting, and QA are major factors in the automation testing ROI calculations. While they do not bring in direct financial benefits, they contribute to huge time savings and quality applications that benefit the overall product delivery timeline. Test automation enhances customer satisfaction with better products in less time.

Speed

Test automation can work 24/7 and can run a significantly large number of test cases within a short time. Tests are more accurate than manual tests and are executed at a much faster pace. Integrating test automation with CI/CD tools results in high-quality products and rapid product readiness, making the best out of continuous automation testing.

The time reduced in running rigorous test cycles and creating products with faster time-to-market leads to huge savings on the investment.

You can leverage test automation on a scalable test infrastructure to run:

• Multiple tests on the same test combinations parallelly
• Multiple tests on different test combinations parallelly
• Same tests on different test combinations parallelly

A few parameters that can be integrated or used as stand-alone parameters for your business are:

• Automating new tests
• Automating older tests
• Reusing testing scripts
• Testing all-around environment

Best practices for ensuring proper test coverage

Test automation implementation should include two vital factors: Gradual introduction and long-term strategy. Both factors contribute directly or indirectly to your business costs and returns. Knowing the best practices of the industry will help you implement test automation in the right way. Consider these three best practices while preparing to integrate automation testing into your business.

Do not rush to automate every single test right away

Trying to automate every test from day one is an extreme approach that will not benefit your business. As considered earlier, manual testing is required in some cases while you automate other tests. So, carefully consider your project pipeline and the testing needs of your business to start from the right place. Identify the ROI parameter most vital for your business and start from there.

Remember every test will become a regression in the future

Consider the long-term impact of automation testing as every testing will become a regression testing eventually. The best practice is to integrate new tests with existing tests as a part of your regression testing right away.

Perform test automation early in the development lifecycle

The traditional waterfall method where development and testing are done in different phases is less productive when compared to the advanced shift-left testing process. The shift-left method enables your teams to develop and test simultaneously. This method helps you detect any bugs early in the development cycle and fix them, saving your business much time and money. 

Become a dominant market player with automation testing

Implementing an automation pipeline might seem like a daunting task when it comes to monetary, time, and effort investment. But understanding all the factors and calculating the ROI of automation testing will demonstrate how it adds value to your QA operations and business. Digital customers are prioritizing agility, and your business requires automation testing to deliver an agile experience to your customers.

Adopting automating testing will give you a head start on your market competition and empower you to become a dominant market player. While there is no shortcut to reaping the benefits of automated testing in one go as it depends on multiple factors, the best way is to choose the right test automation provider.

Just as businesses have changed the way they operate and succeed in the post-pandemic world, marketing has had a paradigm shift to adapt to the new normal. The demand to create personalized customer experiences across multiple digital channels has completely transformed how organizations engage with modern customers.

The biggest challenge in marketing lies in the speed of go-to-market. As customers expect real-time connection and fresh content with brands, marketers feel the need for speed. Complete dependency on the IT team for content authoring and management delays go-to-market. The traditional CMS (Content Management System) cannot meet today’s omnichannel content delivery needs.

As technologies and customers’ expectations have evolved, marketing strategy needs to evolve by streamlining the ever-expansive Martech stack. Your marketing should align with the new level of customer engagement and personalization to increase conversions and reap the benefits of digital marketing. While technology alone cannot solve marketing issues, the right combination of digital tools will help you. reach your full marketing potential in the digital age. 

Reach your full marketing potential with a DXP

A Digital Experience Platform (DXP) provides not only a set of the right tools but also a way to connect them all together. You can combine all these future-proof tools into one unit to perfect personalized digital experiences for your customers. 

DXPs divide functionalities and architecture into packaged capabilities that are composable and flexible. Each of these capabilities includes a set of functionalities that helps you fulfill a specific purpose.

With the right capabilities and functionalities, you can use a DXP to transform your entire CX into one cohesive flow. It will equip your marketing team to deliver content personalized to your audience across all channels, from websites to mobile apps and IoT apps. 

Why consider a DXP in your marketing strategy

Most businesses use different tools for marketing, such as CMS, CRM, marketing automation, analytics, and more. But these varied tools form a disconnected suite that is difficult to integrate and results in data silos. Most teams don’t even use all the tools within a suite efficiently.

On the other hand, a DXP contains all tools required for a MarTech stack but with seamless integration to each tool, and to existing software. A DXP gives clear visibility of every customer interaction with your business to efficiently manage end-to-end customer journeys across all touchpoints. Consider various DXP capabilities that will fuel your marketing strategies. 

Deliver winning brand experiences with DXPs

As content plays a significant part in building brand experiences, a DXP will provide the capabilities you require to create and deliver digital content and experiences. Explore DXP capabilities that will fuel your marketing strategy.

Hybrid CMS

A hybrid CMS is a platform combining the capabilities of a traditional CMS and a headless CMS. The combination allows for more flexibility where you can enjoy the user-friendly interface of a traditional CMS with the architectural flexibility of a headless CMS for greater control over your front end.

Content Delivery Network

A CDN comprises servers that distribute cache data globally from the origin server. CDNs enable you to deliver updated content with quality, speed, and performance as your site users can quickly access content from a server near them.

WYSIWYG Editor

The WYSIWYG (What You See Is What You Get) editor enables you to edit content (text, links, graphics) in the way it would appear on the front end. The editor gives you more creative control to create content and focus on the final design that matches the rest of your digital content.

Content-as-a-Service (CaaS)

CaaS is a layer of APIs, REST, and GraphQL that enables you to access functionality and content separately from the backend. You can create and deliver on-demand content using all content types, such as dates, media, location, and text.

Multisite Platform

Multisite is a feature of CMS where you can build, manage, and operate multiple sites from a single platform. You can easily build and launch new sites with the same codebase, components, and design language on the same server to manage them all with a single dashboard.

Multilingual & Localization

You can create and publish content in multiple languages to cater to all your customers across different geographies. This feature also allows you to edit and personalize content for each geography based on the customer and market needs.

Content Workflow

You can improve your content creation with multistep automated workflows that help you coordinate between multiple customer touchpoints along the buyer’s journey lifecycle. Marketing automation boosts conversions and revenue.

Digital Asset Management (DAM)

DAM helps you store and retrieve digital content and assets quickly without the need for an external database. You can organize your content in a centralized location, making it accessible to all team members and aligning the workflows together.

Low Code/No-Code Capabilities

Low code or no code is a process that facilitates software development with components that require minimal or no coding. Your marketing teams can drag-and-drop these components to build content pages without depending entirely on development teams.  

Solving modern marketing challenges with a DXP

Omnichannel Delivery

A DXP generates a 360° view of your customers at every step of the buying journey. It's much easier for your marketing teams to optimize and launch pages and campaigns across multiple channels to maintain an interactive and responsive presence.

Personalization

DXPs integrate Business Intelligence (BI) and advanced analytics microservices that will enable your marketing teams to personalize content for each customer tailored to their preferred touchpoint. You can deliver the right content at the right time to foster customer loyalty.

Better Control

As DXPs integrate with your business commerce, customer support, and marketing platforms, they help you acquire better control over how you engage and interact with your customers. You can collect data from different sources and use insights to deliver relevant content to your customers.

Flexible Architecture

DXPs are agile and flexible enabling marketers and developers to make necessary independent changes and updates to sites without delay or latency. DXP microservices are independently deployable giving your marketing team the flexibility of publishing content in real-time.

Faster Time-to-Market

DXPs enable effective collaboration between marketing teams and IT teams. Your marketing team can create landing pages and manage content updates on their own without requiring IT assistance for these tasks.

Brand Building

DXPs have an agile CMS that will help you to build your brand with consistency. You can easily store and access your digital assets, such as images, videos, graphics, and templates to take your brand story into the future.

Ideal MarTech Stack

DXPs give you the flexibility to choose an integration framework and architecture to build an ideal digital experience stack. You can choose the tools and integration that can meet your business’s unique needs, for now, and in the future.  

Implementing an effective DXP

Implementing an effective DXP will empower your marketing teams to deliver an elevated digital experience that drives customer engagement and revenue. The integration capabilities of a composable DXP improve workflows and collaboration to deliver contextual content at scale. However, you need to assemble the right DXP that works for your business. Consider the factors you need to focus on to put the best DXP together.

Data Analytics: Data insights help to understand customer history and expectations to personalize content. Integrate an analytics engine and marketing automation to get a complete insight into your customers on an individual level.

CMS with Orchestration: Invest in a CMS with orchestration to enable reusability across multiple channels. Create content once and publish it everywhere driving omnichannel content delivery.

Marketing Automation: Automation capabilities enable your marketing teams to focus on creating the right content instead of lead generations and bulk emails. Marketing automation takes care of repetitive tasks allowing you to work on enhancing experiences and driving revenue. 

Marketing experience optimization with a DXP

An Open DXP empowers you to optimize your marketing experience by serving a myriad of customers with unique needs, interests, and expectations. You can not only create content targeting customer needs but also enhance experiences by acting upon the interactions and feedback.

A DXP will be a one-stop platform for your business to create, manage, and deliver personalized content. You can collect data to measure the impact of your experiences and rapidly iterate through improvements with a DXP. 

A DXP will help create and drive a robust, futuristic digital strategy for your business, and manage your digital content and assets at scale. 

Digital infrastructure in higher education helps to enhance student experiences. Universities can attract more prospective students and fulfill the needs of current students with digital support, such as websites, mobile apps, portals, course registration platforms, intranet, and microsites.

Beyond the visible systems, there is an overarching digital infrastructure that supports university operations enabling smarter education. This type of digital infrastructure is not visible to all users but crucial to bringing together the infrastructure that enhances student experiences.

A digital vision is essential to implement long-term infrastructure in universities. All universities require secure and scalable systems to adapt to the ever-changing education landscape and operate on the same platform rather than in silos.

Let’s consider a few examples of long-term digital infrastructure and how they benefit universities. 

Operational agility and efficiency

Cloud computing

Cloud infrastructure enables universities to create, manage, and access information on one platform without requiring specialized hardware and software on individual terminals. Cloud computing, the method to combine IT and computing resources online, provides greater flexibility to universities to scale and access data.

Universities with limited budgets can utilize cloud services without making new or extra investments in ICT (Information Communications Technology) resources. Cloud infrastructure improves operational agility and efficiency in universities by enabling real-time collaboration supporting student communication and faculty management.  

Cloud reduces data storage costs, minimizes data center maintenance, and provides easy access to university resources. No matter how many facilities a university manages, or how many students enroll, the cloud is scalable and will grow along with the university.

Visualized analytics

Analytics use existing data to derive deep insights into student preferences, faculty experiences, digital trends, and business activities to help universities make data-driven decisions. Smart analytic tools, such as dashboards, visualization software, information and records systems, and performance indicator metrics support agile university practices.

A dashboard to visualize university data analysis facilitates decision-making and planning. Processed data insights, available and accessible to university planners and managers, lead to efficient institutional strategies, research & knowledge production, and policymaking & governance.

Visual digital data enables university administrators to monitor organizational performance & improvement and support responsive learning through adaptive platforms. Smart digital tools enable smarter education. 

Innovate and adapt

Artificial intelligence

AI and its various subsets, like Machine Learning (ML), Deep Learning (DP), Natural Language Processing (NLP), and virtual assistance, empower universities to interpret and manipulate data.  Algorithms can predict students who are most likely to apply, progress, graduate, and become engaged alumni, and help faculty to focus their efforts better.

Algorithms can detect early warning signs in students who struggle academically, enabling university personnel to proactively create retention plans to help the students. AI helps to automate several admin tasks, such as the admission process, visa process, course registration, housing selection, and more, making all processes personalized and faster.

AI will work on time-intensive tasks empowering universities to focus on enhancing student experiences. It gives higher-ed the capability to elevate academic performance, anticipate enrollment trends, and optimize recruitment endeavors.

Big data analytics

Big data analytics combined with cloud computing create high-performance and responsive systems that extract and analyze large volumes of data. Universities can gather information from hundreds of sources in real-time and deliver actionable insights quickly to all its sub-schools, admin, faculty, and stakeholders.

Data analytics can inform educators of their student’s progress, engagement, and well-being. Smart tools incorporating data analytics also allow the faculty to interact with students with an overview of their progress at individual and class levels.

Educators can personalize learning experiences for each student while improving teaching outcomes. Big data analytics enables universities to effectively collect, manage, store, and process data utilizing their resources to innovate better university experiences. 

Faculty experience 

ERP software 

ERP (Enterprise Resource Planning) software is a comprehensive system that digitizes and automates university administrative operations. Such operations include admissions, attendance, courses, alumni, tuition, records, faculty, documentation, and inventory management. An efficient university ERP system can automate course allotments, fee payments, payroll, time-table generation, and hall ticket generation.

University ERP reduces manual and repetitive admin tasks, thus enhancing faculty experiences. It can manage faculty profiles, schedules, and recruitment. It can also generate attendance reports, create class rosters, and send mobile or email alerts to respective students.

Document management tasks can be set up for manual approval, auto-approval, and auto-rejection. Faculty can create and manage different courses effortlessly with detailed sections, credits, and hours of each course. A university ERP increases daily productivity, connects faculty from multiple departments, and facilitates smooth communication ensuring all stakeholders in the university are on the same page. 

Building an education ecosystem

We have considered different types of digital infrastructure for universities. It may seem expensive and time-consuming to invest in and adopt all these tools and technologies. But progressive technology simplifies digital transformation and adoption.

Digital Experience Platforms (DXP), combine content management, analytics, integration, personalization, collaboration, and processes to deliver excellent university experiences on multiple channels. An Open DXP utilizes intelligent architecture to connect with all systems improving operational efficiency.

Composable DXPs with Headless CMS and powerful integration will help universities to manage all services, websites, portals, apps, tools, and information on one platform.

An Open DXP evolves with advancing technology and university growth to adapt and meet the ever-changing expectations of current students, prospective students, faculty, staff, alumni, researchers, and visitors. If your university is looking to enable smarter education, an Open DXP is the solution.