Redesigning Search and Filters to Boost SaaS Product Retention

As a Senior UX/UI Designer working closely with tech lead, I was responsible for revamping the search and filter experience in PredictHQ, a SaaS platform that helps businesses enhance forecast accuracy. PredictHQ enables companies to anticipate fluctuations and capitalize on key demand drivers by analysing the impact of events on demand.

The product has a free trial which is a crucial mechanism for generating new leads, and if users have a positive experience during this period, they are more likely to become long-term paying customers.

To improve the search feature, I've conducted a user research project focused on identifying pain points and challenges users face. Using methods like usability testing and user interviews, I've gathered data on issues such as confusing filters, unclear controls, and complex search behaviour.

Filters need sensible defaults, remember user input, handle dependencies, and provide clear error messages. Additionally, filters are closely tied to the results they generate, requiring a fast and responsive UI to ensure a smooth user experience. Addressing these challenges is a significant task, and mishandling them can lead to user frustration, high abandonment, and bounce rates.

I have organized the user testing sessions by recording them on Zoom, allowing us to capture detailed feedback and interactions for later analysis. To recruit a diverse set of participants, we leveraged networking and offered Uber Eats vouchers as compensation for their time.

During the sessions, participants were asked to complete a series of tasks such as signing up for the product, using search to find a specific event, and filtering to locate events of interest.

We also gathered feedback on the navigation structure and terminology used in the search feature, such as "Rank," "Labels,", "Predicted event spend" and others. Additionally, participants were asked to export their search results, providing insights into that aspect of the user experience.

User testing revealed that many first-time users attempted to search for locations in an interface designed only for previously saved locations, highlighting a critical issue that needs to be addressed.

Here is an example from one of the recordings that leads to results in Location search:

The product currently has a location control with tabs, one of which is used for saved location searches. Seven out of eight participants attempted to use the "saved locations" search for a location that hadn't been saved, resulting in no search results.

It's essential that new users quickly find what they need and have a positive initial experience to reduce churn. Since many new users rely on the search feature, a poor search experience increases the likelihood of them leaving the product. As search is the most used feature in the product, improving its user experience is crucial for retaining active users.

Other research findings:

My goal was to optimize the search and filtering experience for most users by combining industry best practices with research findings that highlighted product-specific issues.

With search and filtering users usually have a specific intent, that they need to somehow communicate to the interface. That intent might be fairly specific or quite general, but in both cases, the design should minimize the time needed for customers to get from the default state (when no filters are selected) to the final state (when all filters are successfully applied).

Throughout the project, I gathered feedback from multiple internal teams, including Customer Success, Marketing, and Development, to ensure the solution addressed diverse perspectives. By closely collaborating with the tech lead, we overcame technical challenges, such as implementing an immediate filter application, and developed a design that was both feasible and achievable within the agreed timeframe.

While developers focused on backend changes to support new features, I wanted to ensure my new design effectively addressed the pain points identified during research. I created a step-by-step testing script for an interactive Figma prototype and reached out to some of the original interview participants and some new ones for a quick "hallway usability testing."

Overall feedback on the prototype was positive, with most users completing all user testing tasks with ease. Users particularly appreciated the quick filter application, the improved filter order, and the clear visual distinction between applied and non-applied filters. The CEO was impressed by the prototype and agreed to expand the project scope to address additional pain points.

The project delivered significant improvements to the Control Center search feature, positively impacting user experience and overall product engagement.

One of the key successes was the increase in filter usage among trial users, which rose from 18% to 34.8% of all searches. This improvement is a direct result of making filters more intuitive and accessible, with less clutter on the screen and a more visually organized hierarchy.

By reducing the usage of non-primary filters and focusing on the most frequently used options, I have simplified the interface and made it easier for users to navigate the search functionality.

The updated design also aligns more closely with the updated design system UI, ensuring a cohesive look and feel across the product. Additionally, we added more tooltips to explain complex parameters, making the system more user-friendly, especially for first-time users. These enhancements have not only improved usability but also contributed to a more polished and consistent user experience overall.

This project taught me valuable lessons about the importance of effective search and filtering and the impact of thoughtful UI improvements on user retention and engagement.