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Amazon Pay: Data-Driven Churn Reduction and Segment-Specific Retention Strategy for India’s UPI Payments Market
A data analytics case that uses LTV, NPS, and engagement clustering to redesign Amazon Pay’s retention strategy — segment by segment, not with blanket cashback.
1. About the Case Competition
Data Conclave is the flagship analytics competition organized by Organon, the Analytics Club of IIM Rohtak, as part of the Vinfusion’24 management festival. Team Drill-Down Analysts — Madhur Purswani, Nikhil Sharma, and Rohan Misra — claimed the National Winner title at this data-heavy event. The case provided a real Amazon Pay dataset and challenged participants to extract actionable retention insights using both analytical rigor and strategic storytelling.
2. Problem Statement Overview
Amazon Pay faces a retention crisis disguised as a cashback dependency. With a 19% churn rate and a disturbing pattern — Tier 1 Prime Members, the platform’s highest-value users, report an average NPS of -1.6 — the brand is losing its most critical cohort to experience gaps rather than product failures. Cashback generates transactions (75.75 average monthly) but fails to convert activity into engagement or positive experience, leaving users vulnerable to churn the moment a competing offer appears. The core question: how do you retain users who transact frequently but feel nothing?
3. What This Winning Deck Covers
The deck moves in three clean layers. First, a rigorous NPS-LTV-AES diagnostic surfaces the critical paradox: city tier is the only variable with a negative NPS but positive LTV relationship — Tier 1 users generate maximum revenue and maximum dissatisfaction simultaneously, creating a retention time bomb.
Second, a 2×2 LTV-NPS customer segmentation matrix creates four precisely defined cohorts — High Value Loyal (12.54%), High-Value At-Risk (12.46%), Growth Customers (36.8%), and Low-Value Disengaged (38.2%) — each with a distinct priority, objective, and intervention logic rather than a blanket retention spend.
Third, the segment-specific retention strategy debunks the cashback myth: cashback amplifies engagement when experience is already positive, but substitutes for it when experience is broken. Each persona — Aarav (Power User), Neha (Frustrated Regular), Rohit (Explorer), Kunal (Deal Hunter) — receives a differentiated retention playbook with tactics calibrated to their exact churn trigger.
The Tableau-backed NPS and LTV dashboard annexures give this deck rare credibility and visual depth for analytics competition submissions.
5 tactical learning takeaways:
- Building an LTV formula (monthly transactions × transaction value × tenure) to prioritize retention investment
- Using a 2×2 segmentation framework to avoid blanket incentive overspend
- Distinguishing cashback as activity driver vs engagement amplifier in platform businesses
- Designing segment-specific retention playbooks with differentiated tactics per cohort
- Using NPS as a leading churn predictor rather than a lagging satisfaction metric
4. The Numbers
Churn rate: 19% | Average App Engagement Score: 5.5 | Prime Member share: 45% | NPS of churned vs retained users: -0.98 vs 0.30. Tier 1 Prime Member NPS: -1.6. High-Value At-Risk segment: 12.46% of users, highest immediate revenue loss risk. High Value Loyal: 12.54%. Growth Customers: 36.8%. Low-Value Disengaged: 38.2%. Average monthly transactions: 75.75.
5. Who Should Study This Deck
Essential for students targeting data analytics, fintech, or product strategy cases. This deck teaches how to convert raw behavioral data into a prioritized retention strategy — a skill directly applicable to analytics competitions, consulting interviews, and product management roles. Particularly valuable for anyone preparing for data-heavy case formats or analytics club competitions. Explore more decks at CaseBuzz.
6. Related Decks on CaseBuzz
FutureForce Case Challenge — Salesforce CRM and customer data strategy — directly complementary to the retention architecture and segment-specific intervention logic in this deck.
InsightX Winners — Masters Union: Blue Tokai Gen Z Growth Strategy Customer behavior analytics and persona-driven growth strategy — strong companion for understanding how to translate data insights into targeted brand actions.
Case Code X — IIM Calcutta + Indore: Quick Commerce Profitability Strategy Ecommerce profitability and customer retention logic — pairs well for understanding LTV-driven prioritization in high-transaction digital platforms.
Prodigy — GTM Strategy Case Competition 2025 by IIM Calcutta Growth strategy and market entry frameworks — useful for contextualizing Amazon Pay’s competitive positioning within India’s fintech landscape.
Conquest 2026 — FMS Delhi: GradNext Consulting Prep Growth Strategy Consulting-style structured problem solving — ideal complement for students wanting to pair data analytics depth with McKinsey-style strategic framing.