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Improving adoption of a feature through targeted user feedback


Response rate


Increased feature adoption

CashBook, a digital record-keeping app, stands as a frontrunner in the fintech startup ecosystem. With a strong user base of 3.5 million, CashBook has significantly transformed expense management for small and medium businesses in India using a mobile-first approach.

One such transformative feature they offer is PassBook. PassBook allows users to track all their bank transactions in a single place by making it easy to integrate with various banks. Initially, there were a lot of users who tried this feature, and repeat users found a lot of value in PassBook and kept coming back to it, however, CashBook noticed that the percentage of users converting into repeat users was lower than expected, leading them to look towards getting user input to understand what the gaps in adoption were.

Methodology: A Strategic Approach to Capture User Feedback

To understand barriers to repeat usage of the PassBook feature, CashBook deployed Blitzllama’s event-based surveys; these surveys were targeted at both first-time, as well as repeat users of the feature. Ensuring this cohort segmentation was crucial to gather relevant and accurate feedback.

CashBook is accessible in 12 languages. To ensure they accurately sampled the audience, CashBook leveraged Blitzllama’s unique capability to support multiple languages with the launch of just a single survey. This functionality is not offered by other user research tools in the industry; companies would have to run 12 different surveys, then spend additional time and resources translating the responses and combining results together for analysis.

CashBook also utilized Blitzllama’s real-time AI-based summarization features for open-ended free-text responses, effectively saving hours they would have spent in categorization. Additionally, for CashBook, it was important to categorize feedback per feature (such as Group Books, PassBook, Multiple Business Profiles, PDF and Excel reports, etc.). Blitzllama’s AI tools support custom categorization based on each company’s individual product taxonomies which their product team used to glean instantly actionable insights from their Blitzllama surveys.

Insights: Unveiling User Challenges

Surveying their users for their experiences with PassBook yielded game-changing insights for CashBook. In particular, the ability to launch cohort-based surveys allowed CashBook to gather very targeted sentiment and feedback.

Cohort A (repeat users cohort) rated the PassBook feature 4.2/5 in terms of user satisfaction. Seeing this positive sentiment quantified for the first time (as this was the first feature satisfaction study they had done at scale) was a very refreshing and motivating insight for the team. The repeat users also suggested a bunch of new features that make their workflows easier such as scanning account statements and new filter options.

Cohort B (users who had used PassBook but had churned) voiced several crucial challenges that affected their adoption of PassBook. Balance inaccuracies, transaction sync issues, a complex feature setup process, and the lack of certain banks' support emerged as significant obstacles.

These insights provided CashBook with a clear direction to enhance the PassBook feature and boost its adoption.This was one of the first surveys they ran and they were surprised with a response rate of 48% across both cohorts.

Solution: Translating Insights into Improvements

Based on user feedback, CashBook made key enhancements to address the identified issues. The team introduced contextual messaging around transaction updates and sync timings for each linked bank account, bringing user expectations in line with the operational realities of the system.

For first-generation smartphone users who faced difficulties connecting CashBook with their bank accounts, on-ground representatives and customer support stepped in, providing guided assistance for a smoother setup process.

Subsequent to these improvements, they resolved technical issues that were highlighted in the feedback and broadened bank support.

Continuous Learning and Iteration for Success

Post the initial enhancements, CashBook continued to gather and analyze user feedback, usage data, and adoption metrics. This ongoing review process facilitated quick iterations, led to continuous strategy enhancement, and resulted in a marked increase in the adoption rate of the PassBook feature.


CashBook's story shows how using feedback from users can make a big difference in improving products and how people use them. This case study demonstrates how a focus on data and user needs can lead to more people using a feature and make the work of product teams more effective.

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