Context & Problem

• Zepto’s instant (10-minute) promise drives growth but creates high peak operational costs and unreliable outcomes during peak windows.

• Scheduled deliveries can materially lower per-order fulfillment cost through batching and predictability, yet adoption among frequent shoppers is low.

• This project asks: how can Zepto convert habitual instant buyers to scheduled slots (groceries, fruits, dairy, household supplies) with minimum ops risk and measurable economics?

What I Analyzed

• Market & competition: Benchmarked slotted actors (BigBasket, Amazon Fresh) and instant-first players to understand slot UX patterns and incentives.

• User research: Survey (n=40) + interviews (n=8) focused on frequency, scheduling awareness, trust, slot preference, and willingness-to-pay.

• Data & instrumentation design: Designed required analytics events and SQL queries to measure funnel & cohort economics.

• Product design & prioritization: Generated 3 solution concepts, scored with RICE/ICE, wireframed the MVP flow (Suggest & Book + Price Nudge), and wrote a production-ready PRD.

Key Research Findings

• Baseline scheduled usage in sample: 8/40 = 20%.

• Core target segment: weekly buyers (≥4×/month) who don’t schedule = 18/40 = 45%.

• Top reasons for not scheduling: “Prefer ASAP” (13/40), “Unaware” (6/40), “Don’t trust on-time” (5/40).

• Slot importance score: ≈ 4.28 / 5; trust score: ≈ 2.9 / 5.

• Preferred slots: Evening (5–9 PM) (50% of sample), Afternoon (12–4 PM) (40%).

• Median acceptable fee (willing-to-pay respondents): ₹30.

Proposed Solution

• MVP: Combine visibility-driven UX (Suggest & Book chip on PDP/cart) + a targeted ₹30 nudge (promo waived or applied) for first-time scheduled users in the pilot cohort.

• Follow-up (Phase 2): If pilot shows adoption but retention/trust lags, introduce “Guaranteed Slot” (pre-assigned rider, live ETA, auto credit on miss) and rider batching incentives.

Execution & Tools

• Design: wireframes for PDP chip, cart banner, slot modal, confirmation, pre-delivery comms, reschedule flow.

• Data & instrumentation: event schema (scheduled_option_impression, slot_selected, order_created(is_scheduled), delivery_attempt, compensation_awarded), dashboards for funnel & cost metrics.

• Tech stack / org touchpoints: mobile/web front-end, slot/reservation API, promo engine, analytics pipeline, ops dashboard, CS playbooks.

Pilot & Experiment Plan

• Pilot cohort: 5k–10k weekly non-schedulers across 2–4 dark-store catchments (mix of metro + tier-2).

• Experiments:

  • A/B: Suggest & Book visibility vs control (primary metric: slot_selection_rate).

  • 3-arm price test: control / waive handling fee / ₹30 coupon (primary metric: scheduled_conversion_rate).

• Duration: 14-day active experiment window; evaluate 8–12 weeks for conversion-to-repeat and economics.

• Primary metrics: scheduled_share_segment, slot_selection_rate, scheduled_on_time_rate, repeat_scheduled_rate, delivery_cost_per_order.

Outcomes & Quantified Targets

• Observed (research sample): scheduled usage 20%; weekly non-schedulers 45%; median fee ₹30; preferred evening slots 50%.

• Conservative target (pilot objective): convert 20–30% of the pilot segment to scheduled within 8–12 weeks (projected target based on research patterns, to be validated).

• Economics estimate (ops-informed range): scheduled deliveries can reduce delivery cost/order by ~20–40% through batching and routing efficiencies if adoption scales (estimate derived from ops modeling; pilot to validate).

Showcase

• Product leadership: I led hypothesis framing, prioritized experiments (RICE/ICE), and delivered a PRD with wireframes and API/DB design.

• Execution readiness: Pilot-ready experiment design with instrumentation, sample sizing rationale, and launch playbooks (CS & ops).

• Analytical rigor: SQL templates, event schema, KPI tree, and success criteria pre-registered to avoid bias.

• Cross-functional orchestration: coordinated design, analytics, fulfillment ops, rider ops, and CS for pilot readiness.

Why This Matters

• This approach converts behavioral insight into a measurable product experiment that directly addresses unit economics - enabling Zepto to reduce variable delivery cost while preserving its instant promise for urgent consumers.

Tools & Artifacts Referenced

• Survey CSV (S01–S40), Interview transcripts (I01–I08), Milestone decks (1–4), Product Concept Note, Full PRD, Figma wireframes.