Case Study Summary: Winning Delhi’s Commute – How Rapido Can Reduce Cancellations and Earn User Loyalty

This capstone case study, conducted as part of the Airtribe Product Management Launchpad, focuses on addressing high cancellation rates, low trust, and poor retention faced by Rapido in the Delhi NCR market. The study applies structured product thinking, user research, competitive analysis, and growth strategy design to propose scalable, trust-building solutions for a two-sided mobility marketplace.

Problem & Market Context

Rapido riders in Delhi experience frequent last-minute cancellations, unreliable ETAs, opaque pricing, forced tipping, and weak customer support. At the same time, captains cancel rides due to low and unpredictable earnings, unclear fare visibility, delayed payouts, and poor platform support. This incentive mismatch has resulted in a 35% cancellation rate and low repeat consideration (~25%), significantly weakening Rapido’s brand position against competitors like Ola and Uber in a highly competitive urban mobility market.

Research & Methodology

The study used a mixed-methods research approach:

Primary research via Google Surveys and user interviews to understand rider expectations, cancellation behaviour, pricing sensitivity, and trust drivers.

Secondary research through extensive Play Store review analysis of both riders and captains to extract recurring pain points, emotional triggers, and sentiment patterns.

Comparative competitor analysis covering Ola, Uber, InDrive, BluSmart, regional players, and public transport alternatives to identify best practices in transparency, reliability, and trust mechanisms.

Key Insights

Riders perceive Rapido as less transparent and less reliable, particularly around surge pricing, tipping prompts, and fare fairness.

Captains feel undervalued and exploited due to unclear payment calculations, inconsistent incentives, penalties for rider cancellations, and unresponsive support.

Trust breakdown exists on both sides of the marketplace, creating a systemic retention challenge rather than an isolated UX issue.

Lack of predictable income for captains directly fuels cancellations, which in turn drives rider churn and brand distrust.

Personas & Journey Mapping

Two core personas were defined:

Urban Commuter (Rider): Price-sensitive, time-constrained, digitally savvy, seeking predictable and reliable daily commute options.

Gig Worker (Captain): Earnings-driven, incentive-sensitive, juggling multiple platforms, and highly impacted by policy fairness and payout reliability.

Detailed user and retention journey mapping highlighted friction at onboarding, booking, driver acceptance, pre-ride experience, post-ride support, and repeat usage-pinpointing where trust erosion and drop-offs occur most frequently.

Solution Ideation & Prioritization

Nine product initiatives were ideated and prioritized using the RICE framework, focusing on trust, retention, and cancellation reduction. The highest-impact solutions were:

Subscription-based Ride Passes for regular commuters (weekly/monthly fixed-fare plans with assured availability).

Delhi Commuter Streaks, a gamified loyalty system rewarding consistent daily rides.

Supporting initiatives like predictive ride matching, behavioural feedback loops, adaptive pricing, and transparency-driven UX enhancements.

Product Design & Prototyping

Low-fidelity and interactive prototypes were built to demonstrate the Ride Pass and Streaks experience, including pass history, streak tracking, rewards, and engagement nudges. Design focused on habit formation, loss aversion, and trust reinforcement rather than discounts alone.

Growth Strategy & Loops

Three compounding growth loops were designed:

Reliability Reinforcement Loop aligning high-quality captains with loyal users.

Social Proof Virality Loop leveraging streak badges, sharing, and referrals (target K-factor: 1.2).

Corporate Partnership Flywheel using office parks as demand anchors to stabilize supply and reduce cancellations.

A phased GTM strategy outlined captain onboarding, early adopter seeding, influencer-led streak campaigns, and B2B2C corporate partnerships to scale adoption sustainably.

Metrics, OKRs & Business Impact

The North Star Metric defined was Completed Rides per Active User per Month (CRPU), supported by metrics such as completion rate, repeat rides, NPS, and captain activity. Clear OKRs targeted:

Reducing cancellations from 35% → 20%

Achieving ≥90% ride completion

Increasing repeat consideration to 40%

Improving 30-day retention and NPS by double-digit gains

Projected impact showed a potential 13.8× increase in completed rides/month over six months through compounding loop effects.

Constraints & Learnings

The study acknowledges regulatory uncertainty around bike taxis in Delhi, limited local access due to geographic constraints, and prototype tooling limitations. Despite this, robust triangulation of data and realistic assumptions ensured actionable insights and feasible product recommendations.

Case Study Link: file:///D:/Dimple/Airtribe/Capstone%20Project/Dimple_Rapido_Capstone.pdf

Prototype Link: https://rapido-ride-joy.lovable.app/

Google Survey Form: https://docs.google.com/forms/d/1MUx6TDApFl40cXKolJ2gaj_p_GUp3qXYozdpksQT8eo/edit#responses

Google Responder Form: https://docs.google.com/spreadsheets/d/1goOwanTGDuDUVM9ei3Jcps1XoP4FcLBMWO3eSQJ5ZGM/edit?resourcekey&usp=forms_web_b&urp=linked#gid=921181235