Turning Local Discovery Into Stronger Store Performance
Zudio
Illustrative case study · Synthetic data
The activities and performance figures below are hypothetical. They do not establish a Palledian engagement or an endorsement by Zudio.
NAME THE
REAL CHALLENGE.
Make existing stores easier to discover, improve the shopping visit and encourage repeat purchasing while maintaining a clear view of store-level economics.
For a store-led value-fashion scenario, online activity needs a useful connection to a physical visit. A direction request does not prove a store visit, and footfall does not prove a purchase. The fictional brief therefore distinguishes each stage, and keeps the same eight stores throughout the comparison.
A hypothetical six-month comparison across the same eight existing Indian stores. New-store sales and ecommerce checkout are excluded; a store-locator interaction is not counted as a sale.
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 | 2,00,000 | 2,18,000 |
| Measured store-entry events | 7,50,000 | 7,80,000 |
| Store conversion | 40% | 41% |
| Retained orders / store bills | 3,00,000 | 3,19,800 |
| First-time purchasing customers | 45,000 | 49,500 |
| Existing purchasing customers | 1,20,000 | 1,29,600 |
| 90-day repeat-purchase rate | 28% | 30% |
| Average net order / bill value | ₹1,200 | ₹1,236 |
| Net merchandise sales | ₹36.00 crore | ₹39.53 crore |
| Blended acquisition cost | ₹80 | ₹77 |
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.
Researched the needs of each existing catchment
Palledian mapped the hypothetical catchments around everyday wardrobe needs, local shopping patterns and seasonal demand. The research recommendation considered access, opening hours and the categories shoppers expected to find. It did not attribute new-store expansion to improvements in existing-store marketing.
Made local discovery more useful
The assumed discovery work covered accurate store names, addresses, opening information and store-page usability. Search and store-locator journeys provided a clear next step for a nearby shopper. Direction clicks remained an intent measure, with store entry measured independently rather than inferred from a browser action.
Connected the visit with available merchandise
The store recommendations examined category visibility, replenishment priorities and the availability of commonly requested sizes. The objective was to help a customer arriving with a specific need find a suitable purchase. Store conversion in the model uses bills divided by measured entry events, with the same counting method in both periods.
Measured return visits through an identified panel
A real programme would need permitted, dependable customer identification before measuring first-time and repeat buyers. This example assumes a consistent, consented POS-linked panel. Email and SMS reminders would be used only if the client supported the channel and the customer had opted in; the scenario does not assert that Zudio currently runs that system.
Reviewed basket quality and store costs together
The fictional commercial review linked bills, average transaction value, merchandise cost and attributable store costs. Recommendations focused on relevant complementary purchases and stock availability rather than a blanket discount. The model includes an allocated support allowance before describing a scoped operating surplus.
ACCOUNT FOR
THE WHOLE COST.
An invented merchandise-cost share of 60% is held constant in both periods. The service-cost line represents attributable store operations, including the modelled occupancy and staffing allocation. No claim is made about Zudio’s actual margins, store productivity or customer-identification capability.
| Programme economics | Synthetic baseline | Modelled scenario |
|---|---|---|
| Scoped revenue | ₹36.00 crore | ₹39.53 crore |
| Merchandise cost | ₹21.60 crore | ₹23.72 crore |
| Service / store costs | ₹5.40 crore | ₹5.67 crore |
| Acquisition expenditure | ₹36.00 lakh | ₹38.12 lakh |
| Allocated programme support | ₹6.50 crore | ₹6.83 crore |
| Scoped operating result | ₹2.14 crore | ₹2.93 crore |
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.
Within the same eight-store footprint, the model connects modest footfall growth with better store conversion and a higher average bill. Local intent, measured visits, identified new customers and repeat purchases stay separate, providing a more credible growth story than treating every click as an in-store customer.
Use this model to explore a consulting approach. Actual success-story claims require supported project activities and measured client data.
Put your brief forward