Nandini Chaudhary

Case study 01 · Discovery and prioritization

Finding the real reason customers disappeared.

The team had a plausible interface hypothesis. Customer evidence showed that building against it would have solved the wrong problem.

My roleResearch design and synthesis
MethodsInterviews + funnel evidence
Scale352 contacted · 88 reached
OutcomeRoadmap redirected
Outreach became evidence25% response rate disclosed

The situation

Customers had completed onboarding, verification, and underwriting. They received an approved offer—and then left without accepting it.

The natural explanation was friction in the final product experience. That explanation was specific enough to generate redesign ideas, but it had not been tested with the people who actually dropped.

What I owned

  • Converted a vague “why are users leaving?” question into a structured six-question interview instrument.
  • Coordinated the target cohort and dialer workflow with customer-service operations.
  • Defined response categories that could be compared while preserving room for the customer’s own explanation.
  • Synthesized 88 completed conversations from 352 outreach attempts and documented the response-rate limitation.

What changed

The app was not the issue.

The largest drivers were mismatch between expectation and the final offer: approved amount and price. Seventy-seven percent said the final offer differed from what they expected. In contrast, 94% reported no technical problem and 96% reported no hesitation with digital acceptance.

The finding shifted the question from “How should we redesign the final screen?” to “How do we align expectations and offer policy earlier?”

Product judgment

A case study does not need to end with a feature to contain product impact. Here, the valuable decision was what not to build.

Redirecting the roadmap protected design and engineering time, challenged an internally comfortable story, and gave the risk and product teams evidence for a more consequential conversation about offer structure.

What I learned

Qualitative evidence becomes more useful when the instrument makes it comparable, and quantitative evidence becomes more useful when people explain the behavior behind it. I also learned to state the limitation—the response rate was 25%—without treating it as a reason to ignore a strong directional result.