Retention Strategy · OKRs

Zomato — CY2026 Retention Strategy

30-day repeat order rate had plateaued at 40% — well short of where a maturing food-delivery market should sit. I built a CY2026 retention strategy anchored on one OKR: take repeat orders from 40% to 62%, grounded in a 7-stage funnel teardown and 4 paired user cohorts.

Role
Solo PM — retention strategy & OKRs
Method
Funnel teardown, paired cohort analysis
Scope
Post-order retention, India food delivery
Deliverable
CY2026 OKR plan + retention roadmap
The problem

Repeat orders had stalled at 40% — the OKR was to close that gap, not just nudge it

A flat repeat-order curve usually means one of two things: the product isn't worth returning to, or something specific in the post-order journey is quietly losing people. I needed to know which, before writing a single initiative.

40%
Current 30-day repeat order rate
62%
CY2026 target — the single OKR this plan is built around
7
Stages mapped in the post-order funnel teardown
4
Paired user cohorts compared side-by-side
Diagnosis

A 7-stage funnel teardown of the post-order journey

Rather than treating "retention" as one number, I broke the journey from discovery to a second order into 7 discrete stages — so any fix could be pointed at the exact place intent breaks down.

Stage 1
Discovery — app open, push notification, or search re-entry.
Stage 2
Browse & search — restaurant and dish discovery.
Stage 3
Cart build — item selection, upsell and combo exposure.
Stage 4
Checkout — delivery fee, ETA and payment friction.
Stage 5
Delivery experience — ETA accuracy and order condition on arrival.
Stage 6
Post-delivery feedback — rating prompts and complaint resolution.
Stage 7
30-day return window — whether a second order actually happens.
Research

4 paired cohorts, to separate correlation from cause

A single retention curve hides more than it reveals. I paired active-vs-lapsed and loyal-vs-price-sensitive users across the same 7 stages, so the plan would target what actually predicts a second order — not what merely correlates with one.

COHORT A

Active, high-frequency orderers

Baseline for "what good looks like" at each of the 7 stages — the behaviors the other cohorts are missing.

COHORT B

Lapsed (35+ days since last order)

Paired against Cohort A to isolate exactly where the journey diverges, stage by stage.

COHORT C

Loyalty-program members

Tests whether rewards mechanics alone explain repeat behavior, or whether journey friction still dominates.

COHORT D

Price-sensitive, promo-driven users

Paired against Cohort C to separate "retained by habit" from "retained by discount."

Strategy

From a flat curve to a 90-day-phased OKR plan

Before

40% repeat order rate

One-size-fits-all retention emails, sent on the same schedule to every user regardless of cohort or funnel stage.

After (target)

62% repeat order rate

Cohort-personalized nudges, each targeted at the specific funnel stage where that cohort actually drops off.

Phase 1
Diagnose — funnel teardown + cohort mapping complete; root causes ranked by stage.
Phase 2
Design — OKR and initiative backlog defined, each initiative tied to one funnel stage and one cohort.
Phase 3
Pilot — test the highest-confidence initiatives against the lapsed and price-sensitive cohorts first.
Phase 4
Scale — roll out validated initiatives across the full base, tracking weekly progress against the 62% OKR.