Shifting MediBuddy's Revenue Base: From 15% to 23% Cashless Adoption in 3 Months
4 min read · 901 words
Designed a personalised reorder experience for MediBuddy to shift user behaviour from reimbursements to cashless payments, grounded in pan-India user research and cross-functional alignment.
- cashless adoption rate (3 months post-launch)
- 15% to 23%cashless adoption rate (3 months post-launch)
- users on pure reimbursements (3 months post-launch)
- 40% to 21.7%users on pure reimbursements (3 months post-launch)
- daily active users (3 months post-launch)
- 14% to 20%daily active users (3 months post-launch)
- login rates (3 months post-launch)
- 60% to 80%login rates (3 months post-launch)
Every figure and quote in this case study was supplied by its author. None were generated.
The Challenge
MediBuddy operates on a cashless-only revenue model, yet when the project began, only 15% of users were paying cashlessly. The remaining majority relied on reimbursements: submitting receipts after the fact, entirely outside the platform's revenue loop. The business was, in effect, providing infrastructure for transactions it could not monetise.
The identified lever was reorder personalisation. MediBuddy had accumulated significant reimbursement data from its corporate users, but that data sat idle. If it could be digitised and surfaced as ready-to-reorder cards, users would have a low-friction path to completing the same purchases cashlessly next time. The initiative was projected to generate ₹100 crore in annual revenue, which made the design problem both high-stakes and well-scoped.
The challenge was not just interaction design. It required aligning product, operations, tech, and executive stakeholders around a shared understanding of the problem, then delivering flows that could actually shift deeply habitual user behaviour across a dispersed corporate user base.
Key Decisions
- 01
Consolidating Checkout to a Single Page
- Considered
- Maintaining the existing multi-step checkout flow, which users already knew.
- Chose
- A new one-page consolidated checkout to reduce steps and cognitive load.
- What it cost
- Familiarity. Users accustomed to the multi-step pattern would face a relearning moment, and the unified checkout required a tech-stack upgrade that could not ship with the initial release.
I chose consolidation because the multi-step flow was itself a source of drop-off: each additional screen is a decision point, and for users already ambivalent about switching from reimbursements to cashless, any friction compounds. The unified checkout remains an experiment pending a future release while the reorder flow shipped independently. That split was a real constraint, not a rounding error, and it means the full efficiency gain is still deferred.
- 02
Grounding Strategy in 100 User Calls, Not Assumptions
- Considered
- Relying on existing analytics and stakeholder intuition to define pain points.
- Chose
- Running 100 user calls across corporates pan-India before defining solution themes.
- What it cost
- Time. Qualitative research at that scale delayed solution definition, and findings then needed synthesis across a room of stakeholders with competing priorities.
The reimbursement-to-cashless shift was a behaviour change problem, not a feature gap. Designing for it without understanding why users defaulted to reimbursements would have produced flows that addressed the wrong friction. The calls surfaced specific pain points that shaped every downstream decision, including the FTUE onboarding nudge and the tooltip added post-testing.
- 03
Running Cross-Functional Affinity Mapping with the CEO in the Room
- Considered
- Standard PM handoff -- receiving a problem brief and moving to design.
- Chose
- Facilitating a structured affinity mapping session with PMs across service lines, Operations, Tech, and the CEO to analyse research insights collectively and define solution themes.
- What it cost
- Process complexity. Getting that many stakeholders into a room around raw research data requires facilitation skill, scheduling, and the willingness to surface disagreement before alignment.
Including the CEO was deliberate: a revenue-critical initiative benefits from executive-level input at the insight stage, not just sign-off at the delivery stage. It also shortened the alignment cycle later, because the solution themes were built on shared understanding rather than handed down from one function to another.
- 04
Designing the FTUE to Explain the Reimbursement-to-Reorder Transformation
- Considered
- Standard onboarding that introduces features without explaining the underlying data logic.
- Chose
- A first-time user experience featuring a scanning animation that shows, concretely, how reimbursement data becomes personalised reorder cards.
- What it cost
- Design and dev complexity for a moment most users encounter only once.
The reorder card concept only works if users trust that the data behind it is accurate and relevant to them. A generic welcome screen would not have established that trust. The animation gave users a mental model for the feature before they used it, which directly supported the user trust score of 4.2 recorded in usability testing.
The Solution
The delivered experience covered the full reorder journey: information architecture, wireframes, a one-page consolidated checkout, service-specific checkout flows, and an FTUE onboarding nudge. Validation ran across 15 internal and 10 external corporate users, producing scores of 4.7 for ease of navigation, 4.2 for user trust, 4.6 for information clarity, and 4.5 for overall satisfaction.
Iteration based on test findings added three targeted improvements: a tooltip nudge on the reorder tab, order dates on reorder cards to help users distinguish between similar past orders, and an 'Order Again' CTA placed at what had previously been a dead-end in the record verification flow.
To support ongoing development, a component playground was built and reusable organisms were contributed to MediBuddy's Mozaic design system, enabling developer teams to work independently of further design handoffs.
The Results
Within 3 months of launch:
- Cashless adoption rate: from 15% to 23%
- Users on pure reimbursements: from 40% to 21.7%
- Monthly active users (release month): from 31% to 35%
- Daily active users: from 14% to 20%
- Login rates after 3 months: from 60% to 80%
- Average GMV after 3 months: ₹12 crore
- Estimated revenue from the feature for FY26-27: ₹200 crore
The most direct signal of behaviour change is the reimbursement drop: moving 40% to 21.7% of users off pure reimbursements represents a structural shift in how the platform is used, not a marginal improvement in engagement. The login rate increase from 60% to 80% suggests users are returning with intent, not just arriving once.