Dunzo Daily – Increasing Average Order Value (AOV)
1. Product Background
Dunzo Daily is Dunzo’s quick-commerce grocery delivery business, offering delivery of essentials—fresh produce, dairy, meat, packaged foods, and household items—typically under 19 minutes.
For this case study, I focused exclusively on Dunzo Daily’s grocery delivery business, which operates on a dark-store model and competes directly with Instamart, Blinkit, Zepto and BigBasket Now.
Given the high frequency and low margin nature of grocery delivery, the business heavily depends on improving basket economics. One of the most critical levers here is the Average Order Value (AOV), which directly impacts revenue per order and determines whether unit economics can be sustainable.
2. Problem Statement
Every grocery order includes fixed costs—delivery, picking/packing, and payment gateway fees. When users place low-value or single-item orders, these costs cannot be recovered, pushing the business into negative contribution margins.
To improve unit economics sustainably, Dunzo wanted to explore product-led interventions that increase AOV by 30–40%, specifically for high-potential user cohorts:
- Young Couples (24–30 years)
- Families with Kids (28–40 years)
The goal: Increase average cart size and encourage high-value items per transaction without harming user experience or conversion.
3. Understanding Unit Economics
Dunzo Daily completed ~55 lakh orders in June (year referenced). The business operated with a monthly burn of ₹176 Cr, driven by delivery cost, storage/handling/packaging and payment gateway charges.
AOV = Revenue / Number of Orders
To improve AOV, we can:
- Increase revenue per order (larger baskets)
- Reduce the proportion of small-cart orders
4. User Research & Personas
Primary Segments
- Young Couples (24–30 years)
- Families with Kids (28–40 years)
Persona Examples
Rahul Dravid – 34, Married, Bengaluru
Chief of Technical Staff at a sports startup. Planned shopper; prefers ordering weekly essentials. Wants a fast, frictionless checkout.
Rishabh Pant – 26, Single, Hyderabad
Product Designer, lives with flatmates. Shops impulsively, values speed and convenience.
5. Key Insights
- Users often forget essential items when directly jumping to checkout.
- Complementary products (e.g., razor → shaving cream) are underutilised as upsell opportunities.
- Decision fatigue leads to cart abandonment—users prefer bundled solutions.
- Post-checkout screen real estate is underused and could help with future-order discovery.
6. Solution Strategy
1. Reposition “Add More” in Checkout (Medium Effort, High Impact)
By repositioning “+ Add More” prominently at the end of the Review Items list, it triggers a last-minute review of essentials, encourages cart top-ups and leverages the psychology of “I’m already paying for delivery; let me get everything in one go.” This reduces low-value orders and improves average basket size.
2. Item Prompts – Contextual Recommendations (High Effort, High Impact)
Introduce intelligent “Customers Also Bought” recommendations across Checkout and Product category pages (e.g., Razor → Shaving cream, Pasta → Pasta sauce).
3. Combos – Item Bundles (High Effort, High Impact)
Launch a dedicated Combos section with pre-curated bundles (Weekly vegetable packs, Breakfast kits, Cleaning essentials). Designed based on popular regional buying patterns and seasonal changes.
4. Post-Checkout Nudges (Low Effort, Low Impact)
After order placement, replace the idle delivery-tracking screen with “Popular purchases in your neighbourhood”, discovery nudges, and new/seasonal item suggestions to educate users for the next purchase.
7. Competitor Benchmarking
Competitors already use a mix of bundled SKUs, high-visibility upsells, and smart recommendations. Dunzo can close the gap via better personalization + placement.
| Company | Funding |
|---|---|
| Swiggy Instamart | $3.6B |
| BigBasket | $1.3B |
| Blinkit | $1B |
| Zepto | $360M |
| Milkbasket | $38.5M |
| JioMart | Subsidiary of Reliance |
8. Impact Estimation
Proposed Solution, Priority, AOV Impact are summarised below. Highest AOV lift comes from combining Item Prompts + Combos.
| Proposed Solution | Priority | AOV Impact |
|---|---|---|
| Reposition “+ Add More” | Medium | High |
| Item Prompts (Upsell) | High | High |
| Combos / Bundles | High | High |
| Post-Checkout Nudges | Low | Low |
9. Success Metrics
To measure impact on both behaviour and business outcomes:
| Question | Metric |
|---|---|
| Are users discovering features? | % users interacting with Add More / Combos |
| Are they converting? | Funnel progression (view → add → purchase) |
| Are they satisfied? | NPS, post-checkout feedback |
| Is AOV increasing? | AOV of exposed users vs control group |
10. Final Recommendation
The most effective approach is a stacked strategy:
Phase 1 – Quick Wins
- Reposition “+ Add More”
- Enable basic complementary recommendations
Phase 2 – High Growth
- Launch region-specific Combos
- Upgrade recommendation engine using purchase + behavioural data
Phase 3 – Habit Building
- Post-checkout nudges
- Weekly curated packs for retention
Expected outcome:
30–40% improvement in AOV over 8–12 weeks for targeted user cohorts, with meaningful improvement to contribution margins.