Before the Launch: Testing a Life Insurer's Health App Concept
When you're launching a wellness app to millions of policyholders, assumptions about what users want can be expensive
One of India's largest life insurers was preparing to launch HappyYou—a health and wellness app designed to help policyholders track their wellbeing, build healthier habits, and engage with their insurance beyond annual premiums. The vision was clear: transform the relationship between insurer and insured from transactional to transformational.
But the product team faced a critical question:
Would Indian consumers actually use a health app from their life insurance company—and what would make them keep coming back?
The stakes were significant. Launch with the wrong features, and users would download once and never return. Miss key demographic expectations, and the app would fail to achieve its engagement goals. Get the UX wrong, and trust—the foundation of insurance—would erode.
We designed a two-phase study to test assumptions against reality.
The research unfolded across two distinct phases. First, we conducted in-depth qualitative interviews with 300 respondents across six cities—from metros like Mumbai and Delhi to Tier 2 markets like Coimbatore and Indore—to understand how Indians actually think about health, wellness, and technology adoption.
Then came the real test: a custom 7-day in-app experience built on the SuperJ platform, where 100 users lived with HappyYou and provided validated feedback at every stage.
The 7-day testing journey:
Day 1: Users downloaded HappyYou and explored key features. We nudged them to use the app at least 3 times daily, capturing first impressions on usability and interface.
Day 3: After 2+ days of usage, users shared deeper insights on what features truly stood out—and what felt confusing or unnecessary.
Day 5: The feedback became granular and nuanced. Users identified features they hadn't noticed before, and articulated specific health areas where they needed app support.
Day 7: Final survey plus 1-on-1 interviews with all 101 respondents who completed the full study—capturing satisfaction scores, likelihood to recommend, and feature requests.
Every response was validated through screenshot uploads—users had to prove they'd actually used specific features before their feedback was accepted. This wasn't claimed behaviour; it was verified engagement
The findings reshaped the product roadmap.
The data revealed patterns that challenged internal assumptions. The Face Scan feature—which the team had considered a secondary innovation—emerged as a standout differentiator, with users describing it as "cool" and "cutting-edge." The health trackers drove daily engagement, but users wanted deeper personalisation tied to their specific health conditions.
Perhaps most valuable: the research surfaced a demographic roadmap. Millennials emerged as the ideal target segment—highest tech adoption, highest wellness spending, and most likely to engage with personalised health features. GenZ prioritised mental health support. GenX needed reactive benefits tied to existing health conditions.
Regional language revealed a paradox. A majority said they'd switch platforms for vernacular support—but in trade-off scenarios, language ranked lowest. It wasn't a differentiator. It was a barrier-remover. The real insight: the vast majority faced language-related challenges that made them avoid certain features entirely.
The research also identified friction points before they became churn drivers. Users loved the app's visual design but found the home page cluttered. Performance issues were flagged early. And 38% of users specifically requested personalised nutrition features—a clear signal for the next development sprint
Now they have a validated product with a clear path to retention.
The app launched with confidence backed by consumer validation. The team moved from guessing at feature priorities to knowing exactly which capabilities would drive daily active usage.
Before
Uncertainty about which features would drive engagement
After
Consumer-validated feature hierarchy with clear development priorities
Before
Uncertainty about which features would drive engagement
After
Consumer-validated feature hierarchy with clear development priorities
Before
Broad assumptions about target demographics
After
Millennials identified as primary segment with specific feature expectations by age group
Before
Broad assumptions about target demographics
After
Millennials identified as primary segment with specific feature expectations by age group
Before
Unknown UX friction points
After
Specific improvement areas identified before mass launch
Before
Unknown UX friction points
After
Specific improvement areas identified before mass launch
A Jupiter Meta Company
