Making Every Product Discovery Work Harder for Growth
Flipkart
Illustrative case study · Synthetic data
The activities and performance figures below are hypothetical. They do not establish a Palledian engagement or an endorsement by Flipkart.
NAME THE
REAL CHALLENGE.
Turn more relevant product discoveries into fulfilled purchases, bring in genuinely new purchasing customers, and improve the economics of the selected category without relying on deeper discounts.
A large marketplace can generate plenty of browsing without every visit creating commercial value. An attractive listing may still fail when delivery information is unclear, the product specification is incomplete, or the preferred payment route is difficult to complete. The illustrative brief therefore focused on purchase quality, rather than the number of accounts created or products added to a basket.
A hypothetical home-and-lifestyle marketplace category programme in India, restricted to a fixed catalogue and serviceable postcode set. These figures cover this fictional programme only, not all of Flipkart.
PUT THE MODEL
ON THE TABLE.
Equivalent six-month baseline and scenario periods. Every value is synthetic.
| Measure | Synthetic baseline | Modelled scenario |
|---|---|---|
| Search landing clicks | 12,00,000 | 13,20,000 |
| Retained orders / store bills | 1,80,000 | 2,00,880 |
| First-time purchasing customers | 90,000 | 1,00,800 |
| Existing purchasing customers | 60,000 | 66,000 |
| 90-day repeat-purchase rate | 26% | 28% |
| Average net order / bill value | ₹1,600 | ₹1,632 |
| Net merchandise sales | ₹28.80 crore | ₹32.78 crore |
| Illustrative platform fee revenue | ₹3.46 crore | ₹3.93 crore |
| Blended acquisition cost | ₹80 | ₹78 |
| Item-return rate | 10% | 9.5% |
New customers means identified first-time purchasers, not registrations. Existing purchasers bought before the period. Repeat rates use matured 90-day first-purchase cohorts. Clicks are not unique customers.
MAKE THE CALL.
SHOW THE REASON.
Mapped demand by customer mission and serviceability
Palledian grouped the example catalogue around specific needs: setting up a home, replacing an everyday item, or buying a practical gift. Search terms, listing information and delivery coverage were reviewed together. The research plan prioritised products that could answer a clear need and be fulfilled reliably in the chosen postcodes, keeping unavailable inventory out of acquisition activity.
Improved the route from search to the right listing
The hypothetical search programme connected non-brand queries to relevant category and product pages. Titles, comparison information and price clarity were aligned with the shopping mission. Budget recommendations favoured serviceable, in-stock products with room for contribution after fees, rather than treating every click as equally valuable.
Reduced uncertainty before payment
The experience plan brought delivery estimates, payment choices, seller information and product specifications closer to the decision. UPI and cash-on-delivery flows were evaluated as separate hypotheses where those options were supported. The aim was to reduce avoidable failure after a shopper had already shown purchase intent.
Built a useful second-purchase journey
Permission-based email and SMS recommendations linked the first order to a relevant next need. A household product buyer might receive suitable care information or a complementary category recommendation after fulfilment. The plan avoided counting a second order until it was completed, and excluded customers without a full retention observation window.
Connected seller quality with commercial allocation
The model gave stock reliability, cancellation patterns and product-information quality a place in the category review. Acquisition was intended to expand alongside seller readiness. A real implementation would reconcile seller settlements and marketing costs before treating a high sales figure as a healthy commercial outcome.
ACCOUNT FOR
THE WHOLE COST.
A fictional 12% net fee yield is applied to net merchandise sales in both periods. It is not Flipkart’s actual commission, take rate or revenue-recognition policy. Seller merchandise sales are never presented as Flipkart revenue.
| Programme economics | Synthetic baseline | Modelled scenario |
|---|---|---|
| Scoped revenue | ₹3.46 crore | ₹3.93 crore |
| Service / store costs | ₹2.20 crore | ₹2.44 crore |
| Acquisition expenditure | ₹72.00 lakh | ₹78.62 lakh |
| Allocated programme support | ₹40.00 lakh | ₹42.00 lakh |
| Scoped operating result | ₹13.60 lakh | ₹28.78 lakh |
A scoped programme surplus is not company-wide net profit. Unallocated head-office, financing, tax and out-of-scope costs are excluded. Growth differences do not establish causality.
KNOW WHAT
DESERVES SCALE.
This example connects better demand capture with a clearer purchase journey and a stronger repeat habit. The commercial story is not simply more marketplace sales: it is more retained orders, more first-time purchasers, and a larger scoped surplus after the modelled service, acquisition and support costs.
Use this model to explore a consulting approach. Actual success-story claims require supported project activities and measured client data.
Put your brief forward