Footfall Falling at a Neighbourhood Supermarket
A neighbourhood supermarket of 4,200 sq ft in Jaipur has seen monthly footfall fall from 24,000 to 19,800 over six months. A quick-commerce dark store opened 3 km away and promises delivery in 12 minutes between 7 a.m. and 11 p.m., free above ₹199. The supermarket's average basket has stayed at ₹640, so sales have fallen roughly in line with footfall. Category data: staples down 22%, fresh produce down 8%, packaged beverages and impulse snacks up 5%. About 60% of customers live within 2 km; the store closes at 10 p.m. Rent and 14 staff salaries are fixed. The owner proposes cutting 300 slow-moving SKUs and converting 600 sq ft near the entrance into a pickup counter for online orders. The landlord has offered a three-year lease renewal with a 9% rent increase. The store does not yet have a loyalty database, and footfall is counted by a manual clicker at the door.
1. Using the data, explain what is driving the sales decline and what is not. Which two data points would you check before accepting the owner's plan?
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Sales = footfall × conversion × basket. Footfall is down 17.5% ((24,000 − 19,800) ÷ 24,000) while the basket is flat at ₹640, so the decline is a traffic problem, not a basket-size or pricing problem. The category pattern is diagnostic: staples are the most exposed to quick commerce because they are planned, searchable and easy to deliver; fresh is less affected because customers still want to inspect it; impulse is up, which suggests the remaining footfall is more convenience-led, possibly in the evening. Do not conclude that the store's prices are too high or that the range is wrong without more evidence.
Before accepting the plan, check (a) how footfall is counted - a manual clicker can double-count staff, deliveries and repeat entries - and (b) the conversion rate, ideally from billing data. If conversion is stable, the store has a catchment-traffic problem; if conversion is falling, availability or service is also involved. Confirm the decline is not concentrated in one daypart or one customer segment.
2. Evaluate the pickup-counter proposal. Recommend two alternative or additional actions, each with a metric.
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The pickup counter is supply-led: there is no evidence in the data that local customers want to collect online orders, and 600 sq ft at the entrance is the highest-impulse space in the store, where beverages and snacks are already growing 5%. Converting it could fund a channel that mainly cannibalises existing sales. Test it small at the side of the store (100–150 sq ft), not at the entrance.
Better first moves:
- Defend the top-up basket. Introduce small packs and a simple WhatsApp/phone order line for customers within 2 km, measured by staples units per 1,000 footfall and repeat-order rate.
- Fix evening availability and staffing between 6 and 10 p.m., measured by fresh fill rate and evening conversion.
- Use the lease decision as a separate analysis: at ₹1.27 crore monthly sales (19,800 × ₹640), a 9% rent rise must be covered by incremental sales or margin, so calculate the sales per sq ft needed to keep occupancy cost at its current percentage.
3. State two risks in the owner's plan and one guardrail for each.
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Risk 1: cutting 300 SKUs can worsen the availability problem that is pushing customers online if the cuts remove low-volume but basket-completing items. Guardrail: delist only SKUs outside the top 80% of sales that have a clear substitute on shelf, and review after one quarter using sales per sq ft and share of basket.
Risk 2: the pickup counter adds labour, parcel handling and possible liability without a proven margin. Guardrail: cap the trial at 150 sq ft and three months, with a pre-agreed stop rule - for example, stop if incremental footfall does not cover the labour cost or if impulse sales in the affected area fall more than 5%.
A third risk is the rent increase; guardrail: do not sign the renewal until the occupancy-cost ratio and break-even sales are calculated.