Revamping SRP at AbhiBus:
A Data-Driven Approach 

 

In 2019, I led the initiative to revamp the sorting logic and redesign the Search Results page at AbhiBus. The objective was to enhance the user experience, reduce friction in the booking process, and drive measurable improvements in conversion rates. Here’s a detailed account of how the project was structured, the challenges faced, and the results achieved. 

1. Understanding User Data

The first step was gaining a comprehensive understanding of user behavior within the app. While the funnel was functional, frequent app opens—sometimes even during successful booking sessions—indicated friction or unmet needs.

Key Insights from Customer Behavior

  • App Switching: Many users switched between apps to compare prices, confirming that the same seat could often be booked on competitor platforms for a lower cost. This reaffirmed the need for competitive sorting logic.
  • Booking Time vs. Success Rate: Data revealed that the time taken to book was inversely proportional to success rates. Shorter booking times significantly increased the likelihood of a completed transaction.
  • Navigation Patterns: Users often jumped back and forth between the Search Results page and the seating layout page, likely to find their preferred seat. This indicated room for improvement in clarity and navigation flow.

Analyzing Variables and Correlation

To pinpoint friction points, I conducted a detailed analysis:

  • Using Spearman correlation analysis, I isolated variables such as price sensitivity, ratings, and tags like "AbhiAssured" and "Recommended" as key drivers of user trust.
  • Frequent users who had booked previously showed a 23% higher likelihood of completing future transactions.

These insights were enriched by qualitative research:

  • Customer Interviews: Conversations with users highlighted confusion in the booking process, often stemming from unclear sorting logic or inconsistent tag placements.
  • Negative Reviews: App store reviews emphasized dissatisfaction with outdated ratings and difficulties finding seats aligned with user preferences.


2. Competitive Analysis

Users had numerous alternatives for bus ticket bookings, many of which provided basic sorting options and intuitive designs. Our competitive analysis revealed:

  • Competitors excelled in price transparency but lacked depth in personalization or loyalty-based sorting.
  • AbhiBus could differentiate itself by integrating behavioral insights, such as loyalty patterns and navigation tendencies, into its sorting logic.


3. Building the Sorting Formula

Using the insights gathered, I developed a sorting formula designed to prioritize user confidence and minimize booking time.

Key Factors in the Formula

  1. Price Sensitivity: Ensured budget-friendly options appeared prominently while maintaining balance with other variables.
  2. User Ratings: Redesigned the star system to factor in both average scores and review recency, providing more accurate reflections of operator quality.
  3. Loyalty Indicators: Repeat users exhibited a 31% faster booking time, making loyalty a key factor in the formula.
  4. Recency and Frequency: Activity patterns informed the prioritization of buses with higher engagement levels.
  5. Navigation Insights: Included logic to reduce unnecessary back-and-forth navigation by prioritizing buses with seats that aligned with typical user preferences.

Testing and Iteration

The formula was refined through two rounds of A/B testing:

  • The winning variant resulted in a 23% higher conversion rate on the Search Results page.
  • Overall funnel conversion increased from 3% to 4.3%, with completed bookings improving by 4% on average.


4. Redesigning the Page

With the sorting logic in place, the next step was to redesign the Search Results page to minimize friction and improve clarity.

Simplification and UX Enhancements

The redesign focused on:

  • Flat Icons: Replacing cluttered visuals with flat icons to improve usability.
  • Filters: Introducing interactable filters to allow quick toggling between preferences like price, amenities, and operator types.
  • Micro-Animations: Adding subtle animations to enhance visual feedback and guide user interactions.

Navigation Flow

Addressing the frequent back-and-forth behavior, we introduced:

  • Clearer seat availability indicators directly on the Search Results page to reduce navigation to the seating layout.
  • Improved tags like "Recommended" and "AbhiAssured" for better context.

Structuring Information

Each search result displayed critical elements like price, ratings, departure time, and operator name. These were structured to emphasize:

  • Visual Hierarchy: Key decision-making factors like price and ratings were prominently displayed, followed by secondary details.
  • Consistency: Standardized layout across results reduced decision fatigue and made comparisons seamless.


5. Results and Impact

The revamped sorting logic and redesigned Search Results page delivered tangible outcomes:

  • Conversion Rates: Funnel conversion improved from 3% to 4.3%, with a 23% increase for users landing on the Search Results page.
  • Completed Bookings: Increased by 4% on average, validating the new sorting logic.
  • Booking Time: Reduced by 12% for new users and 31% for loyal users, improving overall experience.
  • App Ratings: Play Store ratings improved from 4.3 to 4.4, reflecting higher customer satisfaction.


Conclusion

This project highlighted the power of blending data-driven insights with user-centric design. By integrating behavioral patterns, addressing competitive gaps, and refining the user interface, we significantly enhanced the booking experience at AbhiBus. This experience underscores the importance of iterative improvement and thoughtful design in creating solutions that are both effective and user-friendly.