Zepto’s Repeat Rate Dropped by 15%. Investigate Why.

PM Interview Question: Problem Solving / RCA question - Zepto’s Repeat Rate Dropped by 15%. Step by step guide using my 6 step framework.

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You’re in your PM interview at an Indian quick-commerce startup, Zepto.

The interviewer leans forward and asks:

→ “Our repeat rate dropped 15% last month. Find out why.”

This isn’t just any metric drop - it’s repeat rate. The lifeblood of quick-commerce. The metric that determines whether you’re building a sustainable business or just burning cash on one-time users.

Most candidates panic.

They start listing obvious things w/o any structure:

• “Maybe delivery got slower?”

• “Price increased?”

• “Competitors offering discounts?”

But, here’s what separates good answers from great ones:

• Understanding that repeat rate isn’t just about product quality - it’s about habit formation.

• It’s about whether you’ve become essential to someone’s daily routine or just a convenient one-time solution.

In this walkthrough, I’ll show you exactly how to tackle this question using the 6-step framework to answer Problem Solving (or, Root Cause Analysis) questions.

This is the same structured approach that impresses interviewers at Zepto, Blinkit, Swiggy Instamart, Dunzo, and any other quick commerce.

Bonus: Infographic summary at the end

How to Answer Problem Solving / RCA Questions?

Here’s the framework I recommend for every Problem Solving or RCA question. Master this, and you’ll be able to tackle any variation thrown at you.

Use the below framework:

1. Problem - Clarify the Problem

2. Hypotheses - Structure Your Hypotheses

3. Prioritize - Prioritize What to Investigate

4. Data - Deep Dive with Data

5. Validate - Validate Your Hypotheses

6. Recommend - Recommend Next Steps

Refer this deep-dive to read in detail - how to answer any problem-solving or RCA question in a PM interview.

Now, Let’s think through this question out loud using the framework, just like you should in your interview.

Step 1: Clarify the Problem

Before I jump into analysis, I need to understand exactly what we’re measuring and the context around it.

Questions I’d ask the interviewer:

I) About the Metric:

• “When you say ‘repeat rate,’ are we measuring the percentage of users who made at least 2 orders in a given period? Or is it something else like orders per user?”

• “What’s the time window we’re looking at? Repeat within 7 days, 30 days, or something else?”

• “Is this repeat rate for all users, or just users who made their first order during a specific period (cohort-based)?”

• “Are we looking at repeat order rate or repeat user rate?”

II) About the Timeframe:

• “Which month are we comparing? For example, November vs October?”

• “Did the drop happen suddenly or gradually throughout the month?”

• “Is this statistically significant, or could it be within normal variance?”

III) About Scope:

• “Is this drop across all cities, or concentrated in specific markets?”

• “Are all user segments affected equally - new users vs existing users?”

• “Any differences by order value, category mix, or time of day?”

IV) About Context:

• “Have there been any product changes - delivery fees, minimum order values, UI changes?”

• “Any operational changes - delivery time promises, dark store locations, inventory availability?”

• “What’s happening in the competitive landscape - Blinkit, Swiggy Instamart, Dunzo pricing or promotions?”

• “Any major marketing campaigns that might have changed user acquisition quality?”

Interviewer’s Response:

“Good questions.

Repeat rate is defined as the percentage of users who place a second order within 30 days of their first order.

We’re comparing November cohort (users who made first order in November) vs October cohort. The drop was gradual throughout November. It’s statistically significant.

The drop appears most pronounced in Tier-2 cities where we recently expanded. We did increase delivery fees from ₹20 to ₹35 in early November, and our average delivery time increased from 10 minutes to 14 minutes due to dark store optimization.

Competitors have been aggressive with discounting.”

---

Step 2: Structure My Hypotheses

Now I’ll organize my thinking using the Internal vs External framework, with special attention to quick-commerce specific factors.

Internal Factors (Product/Business/Operations)

I) Pricing & Economics:

• Delivery fee increase (₹20 to ₹35 - mentioned by interviewer) making second order less attractive

• Reduced promotional offers for repeat users

• Minimum order value changes

• Price increases on frequently purchased items

• Removal of loyalty benefits or cashback

II) Delivery Experience:

• Delivery time increase (10 to 14 minutes - mentioned)

• Dark store optimization reducing coverage or increasing distances

• Delivery slot availability issues

• Order accuracy problems (wrong items, substitutions)

• Packaging quality deterioration

III) Product Availability:

• Stockouts of popular items increasing

• Category breadth reduction

• Dark store inventory management issues

• SKU rationalization affecting variety

IV) User Experience:

• App performance issues or bugs

• Checkout friction increases

• Payment failures

• Push notification fatigue or reduction

• Search and discovery problems

• Reorder functionality issues

V) User Acquisition Quality:

• Expansion to Tier-2 cities (mentioned) bringing lower-intent users

• Marketing campaigns attracting deal-seekers, not habit-builders

• First-order discounts too aggressive, attracting one-time users

• Referral quality deteriorating

External Factors

I) Competitive Dynamics

• Aggressive discounting by Blinkit and Swiggy Instamart

• Faster delivery promises from competitors

• Better product selection on competing platforms

• Exclusive partnerships secured by competitors

II) Market & Seasonality

• End of the November festival season leading to a post-Diwali demand slump

• Monthly income cycles impacting mid-month purchase behavior

• Weather changes influencing order frequency

• Tier-2 city behavior patterns differing from metro cities

III) User Behavior

• Novelty wearing off in newly launched markets

• Users comparing quick-commerce with traditional grocery options

• Macroeconomic conditions affecting discretionary spending

• Shift from urgent, impulse-driven needs to more planned purchases

IV) Measurement Issues

• Changes in cohort definitions

• Attribution issues with repeat orders

• Data pipeline delays

• Bot activity or fraudulent transactions affecting metrics

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Step 3: Prioritize What to Investigate

I’ll prioritize based on likelihood, impact given the context, and ease of verification.

Top Priority (Investigate First):

1. Delivery Fee Increase Impact (₹20 → ₹35)

• Why: 75% price increase on delivery is massive; mentioned by interviewer

• Expected Impact: Very High - doubles the cost barrier for low-value orders

• Verification: Easy - compare repeat rates before/after fee change, segment by order value

2. Tier-2 City Expansion Effect

• Why: Mentioned as where drop is “most pronounced”; different user behavior

• Expected Impact: High - new market dynamics, lower purchasing power, different habits

• Verification: Easy - compare Tier-1 vs Tier-2 repeat rates, cohort by city tier

3. Delivery Time Increase (10 → 14 minutes)

• Why: 40% slower; core value proposition of quick-commerce is speed

• Expected Impact: High - erodes competitive advantage

• Verification: Medium - analyze satisfaction scores, competitive delivery times

Secondary Priority:

4. User Acquisition Quality in Tier-2

• First-order discount economics attracting wrong users

5. Competitive Discounting

• Mentioned aggressive competitor activity

6. Festival Season Ending

• November post-Diwali effect on consumption

7. Inventory/Availability Issues

• Stockouts driving users to competitors for second order

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Step 4: Deep Dive with Data

Now I’ll walk through how I’d segment and analyze the data for each priority hypothesis.

Analysis 1: Delivery Fee Impact

Segmentation approach:

1. By Order Value:

   1. Users with average order value (AOV) < ₹200

   2. AOV ₹200-₹500

   3. AOV > ₹500

Hypothesis: The ₹35 delivery fee disproportionately affects low-value orders, making them economically unattractive.

What I’d investigate:

• Compare repeat rates for each AOV segment before (delivery fee ₹20) and after (₹35)

• Calculate the “effective markup” the delivery fee creates:

  • ₹150 order: Was 13% fee, now 23% fee

  • ₹300 order: Was 7% fee, now 12% fee

  • ₹500 order: Was 4% fee, now 7% fee

Expected findings if this is the primary cause:

• Sharp decline in repeat rate for users with AOV < ₹200 (35-45% drop)

• Moderate decline for AOV ₹200-₹500 (15-20% drop)

• Minimal impact for AOV > ₹500 (0-5% drop)

• First order AOV unchanged, but users not returning for small top-up orders

• Drop in order frequency per user (users batching orders to avoid multiple fees)

Further segmentation:

1. By purchase category:

   1. Quick top-ups (milk, bread, eggs) - high fee sensitivity

   2. Emergency purchases (medicines, baby products) - lower fee sensitivity

   3. Full basket grocery - moderate fee sensitivity

2. By time of day:

   1. Late night urgent orders (11 PM - 2 AM) - lower fee sensitivity

   2. Planned orders (morning/evening) - higher fee sensitivity

Questions to answer:

• Did users shift to larger basket sizes on their first order?

• Are users abandoning cart when they see the ₹35 fee?

• What’s the cart abandonment rate at checkout?

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Analysis 2: Tier-2 City Expansion Impact

Segmentation approach:

1. By City Tier:

   1. Metro cities (Mumbai, Delhi, Bangalore, etc.)

   2. Tier-1 cities (Pune, Ahmedabad, Jaipur, etc.)

   3. Tier-2 cities (recent expansion markets)

2. By Tenure in Market:

   1. Markets operational > 6 months

   2. Markets operational 3-6 months

   3. Markets operational < 3 months (new Tier-2 expansions)

What I’d investigate:

• Repeat rates by city tier

• First order behaviour differences (AOV, category mix, time of day)

• User demographics and purchasing power differences

• Delivery density and dark store coverage in each tier

Expected findings if Tier-2 is the issue:

• Metro repeat rate: Down 5-8% (less affected)

• Tier-1 repeat rate: Down 10-12%

• Tier-2 repeat rate: Down 25-35% (driving overall decline)

Why Tier-2 might have lower repeat rates:

Economic factors:

• Lower disposable income making delivery fees more painful

• Price sensitivity higher (comparing to local kirana stores)

• Less “time poverty” - people have time to shop at nearby stores

Behavioral factors:

• Novelty-driven first orders (”trying out the new app”)

• Acquired through aggressive marketing, not organic need

• Lower density of “urgent need” moments

• Stronger relationships with local kirana stores

Infrastructure factors:

• Fewer dark stores = longer delivery times in practice

• Lower inventory breadth due to demand uncertainty

• Higher stockout rates in new markets

Validation questions:

• What’s the first-order satisfaction score by city tier?

• Are Tier-2 users complaining about specific issues (price, selection, delivery)?

• What’s the competitive landscape in Tier-2 (is Blinkit/Swiggy even there)?

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Analysis 3: Delivery Time Impact

Segmentation approach:

1. By Actual Delivery Time:

   1. Orders delivered in < 10 minutes

   2. Orders delivered in 10-12 minutes

   3. Orders delivered in 12-15 minutes

   4. Orders delivered in > 15 minutes

2. By User Expectation:

   1. Users promised 10 minutes (old promise)

   2. Users promised 15 minutes (new promise)

   3. Users who experienced both (early Nov cohort)

What I’d investigate:

• Correlation between delivery time and repeat rate

• Customer satisfaction (CSAT) scores by delivery time bucket

• Complaint rates about “slow delivery”

• Competitive delivery times (are we still faster or now slower?)

Expected findings if delivery time is a key driver:

• Users who got delivered in > 15 minutes: 30-40% lower repeat rate

• Users who experienced degradation (were promised 10, got 14): frustrated, lower repeat

• Time-sensitive categories (midnight snacks, baby products) most affected

• Satisfaction scores dropping proportionally with delivery time

Dark store optimization analysis:

• Did optimization reduce number of dark stores or relocate them?

• Has average distance from user to dark store increased?

• Are we now competing with local stores on speed rather than beating them decisively?

Critical question to answer: At what delivery time does quick-commerce stop being “quick” and become just “online grocery”?

• 10 minutes = magical, habit-forming

• 12-15 minutes = still good, but not magical

• 15-20 minutes = why not just use Swiggy/Dunzo?

• 20+ minutes = why not plan ahead and use BigBasket/Grofers?

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Analysis 4: User Acquisition Quality

Segmentation approach:

1. By Acquisition Channel:

   1. Organic (direct app open, word of mouth)

   2. Paid (Google/Facebook ads)

   3. Referral (from existing users)

   4. Offline (billboards, flyers in Tier-2 cities)

2. By First Order Behavior:

   1. Discount used (%) on first order

   2. AOV on first order

   3. Category purchased (daily essentials vs impulse)

   4. Time of day (planned vs urgent)

   5. Days since app install to first order

What I’d investigate:

• Repeat rate by acquisition channel

• Cost of acquisition vs. lifetime value by channel

• First-order discount dependency by cohort

Expected findings if acquisition quality declined:

• Tier-2 offline/paid acquisition: 8-12% repeat rate (deal-seekers)

• Metro organic/referral: 35-40% repeat rate (habit builders)

• High discount dependency: Users who used >50% discount have <10% repeat

• Low intent signals: Users who took >7 days from install to first order have lower repeat

Behavioural indicators of low-quality users:

• Downloaded app during discount campaign, waited for maximum discount code

• Single category purchase (only bought what was on offer)

• Unusual purchase patterns (50 packs of instant noodles)

• Doesn’t enable notifications

• Uninstalls app after first order

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Analysis 5: Competitive Pressure

Market intelligence to gather:

Competitor Pricing:

• What’s Blinkit/Swiggy Instamart charging for delivery?

• Are they running aggressive first + second order discount campaigns?

• Price comparison on top 50 SKUs

Competitor Experience:

• Delivery time promises and actual performance

• Availability/selection comparison

• Exclusive brand partnerships

User behavior analysis:

• Are users trying multiple platforms? (survey data)

• Multi-homing behavior (using 2-3 quick-commerce apps)

• What triggers platform switching?

Expected findings if competition is a major factor:

• Users in markets with aggressive Blinkit presence: 20-25% lower repeat

• Markets without strong competition: minimal repeat rate decline

• Second order timing: users comparing prices before ordering again

• Category switching: using Zepto for some categories, competitors for others

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Analysis 6: Festival Season Effect

Temporal analysis:

Compare year-over-year:

• November 2023 vs November 2022 repeat rates

• October-November transition patterns historically

• Post-festival consumption patterns

Category analysis:

• Festival-driven categories (sweets, snacks, decorations): one-time spike

• Daily essentials (milk, bread, eggs): more stable repeat behavior

• Premium products: purchased during festivals, not maintained

Expected findings if seasonality is significant:

• Repeat rate always dips 10-12% post-Diwali historically

• Festival category purchasers: <15% repeat rate

• Daily essentials purchasers: >30% repeat rate

• November 2024 worse than 2023 due to compounding factors (fees + delivery time)

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Analysis 7: Inventory & Availability

Operational metrics to analyze:

Stockout Rates:

• % of orders with substitutions

• % of items marked “out of stock” when browsing

• Popular items frequently unavailable

By Dark Store:

• New/optimized dark stores vs established ones

• Tier-2 dark stores vs Metro dark stores

• Dark store size and SKU capacity

User impact:

• Correlation between stockout experience and repeat rate

• Users who got substitutions: satisfaction scores

• Users who abandoned cart due to stockouts

Expected findings if inventory is a driver:

• Users who experienced stockouts on first order: 40-50% lower repeat

• Tier-2 dark stores: 2x higher stockout rates than metros

• High-frequency items (milk, eggs, bread) stockouts = devastating

• Weekend stockouts worse than weekdays

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Step 5: Validate My Hypothesis

Based on my analysis framework, let me work through validation for my primary hypothesis.

Primary Hypothesis: Delivery Fee Increase + Tier-2 Expansion = Perfect Storm

The hypothesis in detail: The 15% repeat rate drop is primarily driven by the combination of:

1. 75% delivery fee increase (₹20 → ₹35) making small, frequent orders economically painful

2. Tier-2 expansion bringing in price-sensitive users with lower purchasing power

3. These two factors compounding: Tier-2 users hit harder by fee increase

Evidence that would validate this:

✅ Delivery Fee Impact Validation:

• Tier-2 cities with ₹35 fee: Repeat rate = 12-15% (down from 25-30%)

• Metro cities with ₹35 fee: Repeat rate = 28-32% (down from 35-40%)

• AOV shift: Average order value increased by 40-50% as users batch purchases

• Order frequency drop: Users who previously ordered 3x/week now order 1-2x/week

• Cart abandonment spike: 25-30% increase when users see ₹35 fee at checkout

• Low-value order collapse: Orders under ₹200 down 60-70%

✅ Tier-2 Expansion Impact Validation:

• Metro repeat rates: Down only 5-8% (delivery fee + slight competition impact)

• Tier-1 repeat rates: Down 12-15% (fee + moderate competition)

• Tier-2 repeat rates: Down 30-40% (fee + wrong user profile + novelty wearing off)

• Cohort quality: Tier-2 first-time users acquired through aggressive marketing show <10% repeat vs 35% in metros

• Demographics: Tier-2 users have 40-50% lower AOV and 60% higher price sensitivity

✅ Combined Effect Validation:

• Tier-2 users with low AOV (<₹200): Almost zero repeat rate (delivery fee makes order 30%+ more expensive)

• Metro users with high AOV (>₹500): Minimal repeat rate impact (fee is <10% of order)

• Timing: Repeat rate decline accelerated in markets that launched in Tier-2 in late October/early November

✅ User Behavior Signals:

• Survey data showing #1 complaint in Tier-2: “Too expensive compared to local stores”

• Metro users complaining about fee but still ordering (habit formed)

• Tier-2 users: “Used once to try, but will stick to nearby kirana store”

• Support tickets about delivery fee up 200% in November

Evidence that would refute this hypothesis:

❌ Metro cities showing similar 15% repeat rate decline (would indicate broader issue)

❌ High AOV users also showing significant decline (would point to delivery time or availability)

❌ Tier-2 users acquired organically also showing same low repeat (would indicate market fit issue, not marketing quality)

❌ Pre-fee increase (October) Tier-2 users showing strong repeat rates (would isolate fee as primary cause)

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Secondary Contributing Factors

Delivery Time Degradation:

• Contribution: 20-25% of the decline

• Validation: Users experiencing >15-minute deliveries show 20% lower repeat vs <10 minute deliveries

• Compounding effect: Combined with higher fees, users questioning value proposition

Competitive Discounting:

• Contribution: 15-20% of the decline

• Validation: Markets with aggressive Blinkit presence show 12-15% additional decline

• Multi-homing behavior: 40% of Tier-2 users trying multiple platforms, choosing whoever has best deal

Festival Season Ending:

• Contribution: 10-15% of the decline

• Validation: Similar 8-10% seasonal dip in previous years post-Diwali

• Category specific: Festival category purchasers showing <5% repeat regardless

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Final Validated Model

Overall 15% repeat rate decline breakdown:

• 50-55%: Delivery fee increase impact, especially on low-AOV users

• 25-30%: Tier-2 expansion bringing wrong user cohort

• 10-15%: Delivery time degradation reducing “wow factor”

• 5-10%: Competitive discounting creating multi-homing behavior

• 5-10%: Normal seasonal post-festival dip

Geographic split:

• Metro cities: 6-8% decline (mostly fee + slight delivery time impact)

• Tier-1 cities: 12-14% decline (fee + competition + some expansion quality issues)

• Tier-2 cities: 28-35% decline (fee + wrong users + weak infrastructure + competition)

Since Tier-2 now represents growing share of user base, overall blended repeat rate = 15% decline.

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Step 6: Recommend Next Steps

Now I’ll provide concrete, actionable recommendations across immediate, short-term, and long-term horizons.

Immediate Actions (This Week)

1. Emergency Delivery Fee Adjustment

• Action: Implement tiered delivery fee structure immediately

  • Orders > ₹500: ₹0 delivery fee

  • Orders ₹300-₹500: ₹20 delivery fee

  • Orders < ₹300: ₹35 delivery fee

• Why: Incentivizes larger baskets while making small top-ups affordable

• Expected impact: Recover 30-40% of repeat rate decline

• Test: Roll out to 25% of users, measure over 7 days

2. Tier-2 Specific Interventions

• Free delivery for 2nd & 3rd orders for Tier-2 users (habit formation subsidy)

• Localized pricing: Lower prices on high-frequency items in Tier-2 to match kirana economics

• Expected impact: Improve Tier-2 repeat from 12% to 18-20%

3. Win-Back Campaign

• Target: November cohort users who haven’t placed 2nd order in 15+ days

• Offer: “₹100 off + free delivery on your next order”

• Messaging: “We’ve reduced delivery fees - come back and save”

• Expected impact: Convert 20-25% of dormant users

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Short-Term Solutions (This Month)

1. Delivery Time Recovery Plan

• Audit dark store locations: Identify coverage gaps created by “optimization”

• Add 3-5 micro dark stores in high-density Tier-2 neighborhoods

• Promise accuracy: Under-promise (15 min) and over-deliver (12 min) rather than opposite

• Target: Get 80% of orders delivered in <12 minutes within 30 days

2. Subscription/Membership Model

• Launch “Zepto Pass”: ₹199/month for unlimited free delivery

• Value proposition: Pays for itself after 6 orders (vs ₹35 × 6 = ₹210)

• Psychology: Pre-commitment device; users order more to “get their money’s worth”

• Target: Convert 15-20% of high-frequency users

• Expected impact: 50-60% higher repeat rate among Pass members

3. Smart Recommendations & Reorder

• One-tap reorder: “Buy your November groceries again”

• Predictive reminders: “You usually buy milk every 3 days - running low?”

• Bundle suggestions: “Add ₹150 more to save ₹35 on delivery”

• Expected impact: Increase order frequency by 25-30%

4. Tier-2 Go-to-Market Reset

• Pause aggressive acquisition: Stop burning money on low-quality users

• Focus on quality over quantity: Target urban, employed, nuclear families

• Hyperlocal marketing: Partner with apartment complexes, offices

• Education: TV spots showing “kirana convenience at home” not “cheap deals”

5. Inventory Excellence Initiative

• Stockout tracking: Real-time dashboard for top 100 SKUs

• Auto-replenishment: ML-based prediction for fast-moving items

• Category expansion in Tier-2: Add more local brands/preferences

• Target: Reduce stockouts from 15% to <5% in 30 days

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Long-Term Solutions (Next Quarter)

1. Dynamic Pricing & Fee Structure

• Time-based fees: Higher fees during peak (8-10 PM), lower during off-peak

• Loyalty tiers: Bronze/Silver/Gold based on order history, progressive fee reduction

• Basket intelligence: Free delivery on “essential bundles” (milk + bread + eggs)

• Zone-based fees: Higher fees for low-density areas, lower for high-density

2. Dark Store Network Optimization (for Speed, Not Just Cost)

• 15-minute promise vs 10-minute reality: Rebuild around guaranteed fast delivery

• Micro-fulfillment centers: Smaller, more distributed dark stores in Tier-2

• Inventory depth vs breadth trade-off: Stock top 500 SKUs deeply, long tail lighter

• Target: 95% on-time delivery, <12 minutes average

3. Category & Assortment Strategy

• Build frequency drivers:

  • Milk subscription (daily delivery, auto-reorder)

  • Baby care essentials (high frequency, high loyalty)

  • Pet food (recurring, predictable)

• Tier-2 localization: Add regional brands, local preferences

• Private label: Develop Zepto-branded staples at 15-20% discount

4. Habit Formation Features

• Scheduled orders: “Deliver milk every morning at 7 AM”

• Smart lists: “Your monthly essentials - ₹2,500, delivered every 1st”

• Streak gamification: “5-day ordering streak - unlock free delivery!”

• Social sharing: “My quick-commerce routine” content for Instagram

5. Metro vs Tier-2 Differentiation

• Metro strategy: Premium, fast, comprehensive (maintain 10-min magic)

• Tier-2 strategy: Value, reliability, essentials (15-min is fine if price is right)

• Accept different economics: Tier-2 will have lower AOV but can still be profitable at scale

• Don’t force metro playbook on Tier-2 markets

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Measurement & Prevention

1. Early Warning System

• Daily cohort tracking: Flag any cohort with <20% repeat rate immediately

• Real-time alerts: Delivery time > 15 min for >30% of orders

• Competitive monitoring: Track Blinkit/Swiggy pricing/delivery times weekly

• User feedback loop: Post-order survey for every 10th order

2. Retention Metrics Dashboard

• Track by city tier, cohort, AOV segment, delivery time bucket

• Weekly reviews: What’s working, what’s not

• Experimentation culture: Run 5-10 A/B tests monthly on repeat drivers

• Target metric: 30% repeat rate as minimum threshold

3. Unit Economics Guardrails

• Don’t optimize for growth at expense of retention

• CAC:LTV ratio: Must stay above 1:3

• Delivery fee needs to cover 60-70% of delivery cost (subsidize rest for habit formation)

• Tier-2 expansion only when playbook proven (don’t scale broken model)

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Executive Summary

Root Cause Analysis:

The 15% repeat rate decline is driven by a combination of pricing, expansion strategy, and operational factors:

Primary Drivers (75-80% of decline):

1. Delivery fee increase from ₹20 to ₹35 (75% hike): Made small, frequent orders economically painful, especially for users with AOV < ₹300. This destroyed the “quick top-up” use case.

2. Tier-2 city expansion with wrong user acquisition: Aggressive marketing in Tier-2 cities attracted novelty-seekers and deal-hunters, not habit-builders. These users have lower purchasing power and stronger alternatives (local kirana stores).

Secondary Drivers (20-25% of decline):

1. Delivery time degradation (10 → 14 minutes): Dark store optimization reduced the “magic” of instant delivery

2. Competitive discounting: Blinkit/Swiggy aggressive promotions creating multi-homing behavior

3. Seasonal effects: Post-Diwali consumption normalization

Geographic Breakdown:

• Metro cities: 6-8% decline (mostly delivery fee impact)

• Tier-1 cities: 12-14% decline (fee + moderate competition)

• Tier-2 cities: 28-35% decline (fee + wrong users + weak infrastructure)

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Immediate Recommendations:

Week 1 Actions:

1. Implement tiered delivery fees: Free delivery on ₹500+, ₹20 on ₹300-500, ₹35 on <₹300

2. Tier-2 habit formation subsidy: Free delivery on 2nd and 3rd orders

3. Win-back campaign: ₹100 off + free delivery for dormant November users

Expected Impact: Recover 30-40% of repeat rate decline within 14 days

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Strategic Pivot Needed:

Quick-commerce success requires habit formation, not transaction maximization.

Wrong approach (current):

• Aggressive expansion into Tier-2

• One-size-fits-all pricing

• Optimizing for cost over speed

• Acquiring users with heavy discounts

Right approach (recommended):

• Focused expansion with proven playbook

• Differentiated pricing by basket size/geography

• Speed as non-negotiable value prop

• Acquiring the right users, not maximum users

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Success Metrics to Track:

Primary:

• 30-day repeat rate by city tier and cohort

• Orders per user per month

• Zepto Pass adoption and usage

Secondary:

• Average order value trends

• Delivery time consistency (<12 min %)

• Stockout rates on top 100 SKUs

• CAC:LTV ratio by acquisition channel

Target State (90 days):

• Overall repeat rate: 28-30% (recover to pre-decline levels)

• Metro repeat rate: 35-38%

• Tier-2 repeat rate: 18-22% (accept lower but profitable)

• Zepto Pass members: 15-20% of active base with 50%+ repeat rate

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Infographic Summary to Investigate Drop in Zepto’s Repeat Rate

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Key Takeaways from This Answer

Notice how this answer demonstrates:

✅ Deep quick-commerce understanding: Not just generic e-commerce, but specific to 10-minute delivery dynamics

✅ Pricing psychology: Understanding how ₹35 fee on ₹150 order creates 23% markup vs 7% on ₹500 order

✅ Market segmentation sophistication: Metro vs Tier-1 vs Tier-2 require different strategies

✅ Unit economics awareness: Balancing growth, retention, and profitability

✅ Operational depth: Dark store network, inventory management, delivery time promises

✅ User psychology: Habit formation, frequency drivers, subscription mechanics

✅ Competitive dynamics: Multi-homing behavior, promotional warfare

✅ Clear prioritization: Primary vs secondary drivers with quantified impact

✅ Actionable recommendations: Immediate (this week), short-term (this month), long-term (this quarter)

✅ Realistic expectations: Accept that Tier-2 will have different economics than metros

This is the depth and structure you need to demonstrate in Problem Solving / RCA interviews for marketplace and quick-commerce companies.

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Practice this framework on the below 30+ questions, and you’ll be ready for interviews at Zepto, Blinkit, Swiggy, Dunzo, Uber, DoorDash, Instacart, or any consumer marketplace!

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3. DoorDash orders per customer fell from 3.2 to 2.4 per month. Diagnose the issue.

4. Average Zoom meeting duration dropped from 35 minutes to 25 minutes. Why?

5. Reddit’s DAUs decreased by 8% over the last month. Investigate the root cause.

Funnel Issue Questions

1. On Amazon, users are adding items to cart normally, but cart-to-checkout conversion dropped from 75% to 55%. What’s wrong?

2. Uber’s driver sign-up completion rate dropped from 40% to 25% at the background check stage. Diagnose why.

3. On your dating app, match rates are stable, but match-to-first-message conversion dropped from 60% to 40%. What happened?

4. Your SaaS product’s free trial-to-paid conversion rate fell from 25% to 18% after changing the trial period from 14 to 30 days. Investigate.

5. Player completion rate for your game tutorial dropped from 80% to 60% after a redesign. Why?

Anomaly Questions

1. Venmo transaction volume suddenly spiked 300% on a Tuesday afternoon. What’s happening?

2. YouTube’s video storage costs increased 40% overnight with no corresponding increase in uploads. Investigate.

3. Slack messages sent across your organization dropped to near-zero for 2 hours midday. What happened?

4. Your app normally gets 50 reviews per day, but today you received 2,000 negative reviews. Diagnose the cause.

5. Profile views for all LinkedIn users increased 250% over 48 hours. What’s going on?

A/B Test Questions

1. You A/B tested a cleaner homepage design. Variant B has 25% fewer clicks but 15% higher conversions. Should you ship?

2. Testing increased push notifications: Variant B (3x more notifications) has 20% higher engagement but 30% higher uninstall rate. What do you do?

3. You tested a $5/month price increase. Variant B has 15% lower conversions but higher revenue per user. Ship or not?

4. New search algorithm (Variant B) shows 10% faster results but 5% lower click-through rate on search results. What’s your decision?

5. Variant B adds a personalization survey to onboarding: 20% lower completion but 40% higher Day 7 retention for those who complete. Ship it?

Multi-Metric Questions

1. YouTube video uploads are up 30% but watch time per user is down 15%. Explain and investigate.

2. Pinterest engagement (pins, saves) increased 25% but ad revenue decreased 10%. What’s happening?

3. Spotify premium subscribers grew 20% but premium user listening hours only grew 5%. Diagnose.

4. Website traffic increased 40% but revenue only grew 10%. What went wrong?

5. Uber ride volume grew 15% but revenue grew only 5%, while driver earnings stayed flat. Explain.

Growth Issue Questions

1. Instagram’s Day 1 activation rate (users who post or engage on first day) dropped from 45% to 30%. Why?

2. Users maintaining 7+ day streaks on Duolingo decreased from 35% to 25% of active users. Investigate.

3. Medium’s new writer retention (writers who publish second article) dropped from 40% to 28%. Diagnose.

4. Peloton users’ average monthly workouts declined from 12 to 8 over six months. What happened?

5. New Discord servers are seeing 30% lower message volume in their first month compared to six months ago. Why?

Geographic Anomaly Questions

1. Uber Eats order volume in India dropped 40% in one week while other markets are stable. Investigate.

2. TikTok daily time spent per user in the UK fell 25% while US/EU are flat. Diagnose the issue.

3. Airbnb bookings in Japan increased 200% in 48 hours with no marketing campaign. What’s happening?

4. WhatsApp crashes increased 500% in Brazil only, with minimal issues elsewhere. Why?

5. Shopify stores in Canada are seeing 35% higher conversion rates than US stores suddenly. Investigate.