Analytics & Metrics Framework to Improve Zomato Retention in Tier-1 Cities

This project focuses on improving user engagement, retention, and repeat purchases for Zomato’s food delivery platform in Tier-1 cities by applying a structured analytics, metrics, and cohort-based approach. The objective is to optimize the complete user journey, from discovery to post-delivery - using personalization, habit-forming features, and operational reliability.

Goal & Problem Statement

Tier-1 Zomato users frequently drop off due to decision fatigue, friction during discovery and checkout, inconsistent delivery experiences, and rising expectations. While users want fast discovery, seamless ordering, and reliable delivery, these gaps lead to lower engagement and reduced repeat usage, directly impacting Zomato’s retention and revenue growth.

OKRs & Strategic Focus

Two primary OKRs were defined:

Personalize and streamline food discovery to increase engagement and reduce decision fatigue

Improve order completion and repeat purchases to strengthen habit formation and lifetime value

Key initiatives included:

Smart Meal Planner with contextual nudges

Gamified discovery (spin-the-wheel suggestions, short-form food content)

“Explore Nearby in 10 minutes” filters and dynamic restaurant bundles

“Reorder in 10 seconds” widget with streak rewards

Split payments / BNPL and transparent pricing to reduce checkout drop-offs

Meal subscriptions to increase order frequency and AOV.

Metrics & KPIs

The project identified and mapped supporting KPIs to each OKR, including:

DAU/MAU ratio

Average session time

Restaurants explored per session

Repeat order rate (7/30 days)

Checkout drop-off rate

Average Order Value (AOV)

Delivery time variance

NPS and in-app feedback

Each KPI was tied to actionable product insights, A/B testing strategies, and segmentation by city, cuisine, and user type.

Funnel Analysis

A detailed funnel analysis identified drop-off points at every stage:

App launch → discovery (lack of personalization)

Discovery → consideration (decision fatigue)

Menu → cart (high AOV concerns)

Checkout → payment (cost surprises, payment friction)

Delivery → re-order (lack of habit loops)

Targeted hypotheses and feature interventions were proposed at each stage to improve conversion and reduce churn.

Cohort Analysis & Retention Insights

Weekly cohorts were analysed across Day 7, Day 30, and Day 90 retention, along with average orders per month. The cohort framework enabled:

Identification of critical churn moments (especially 1st → 2nd order)

Measurement of feature impact over time

Data-driven prioritization of retention initiatives

Results showed clear improvements after feature rollouts:

Day 7 retention improved from ~33–35% to ~39–40%

Day 30 retention increased from ~22–27% to ~28–30%

Day 90 retention rose modestly to ~18–19%

Average orders/month increased from ~1.1–1.3 to ~1.4–1.5

Total monthly orders per cohort grew significantly, indicating revenue impact.

Key Learnings & Business Impact

Habit-forming features like 1-click reorder and streak rewards had the strongest impact on early retention

Gamified discovery and personalization improved mid-term engagement

Subscriptions and delivery consistency contributed to higher order frequency and long-term loyalty

BNPL reduced checkout friction but had limited impact on long-term retention

Delivery reliability remains the biggest lever for sustained loyalty in Tier-1 markets.

Overall Outcome

The project demonstrates a strong ability to connect product strategy, analytics, and business outcomes. By combining OKRs, KPIs, funnel analysis, and cohort tracking, the assignment shows how data-driven decision-making can directly improve engagement, retention, and revenue for a large-scale consumer product like Zomato.