Footfall is up, yet basket size shrank and returns climbed
Question
Store traffic is higher than last year and conversion is okay, but the average basket is smaller and returns have crept up—especially after promo weekends. Shelves look full overall, yet we still get peak-hour stockouts in a few aisles. Dashboards say we’re doing fine, but the P&L feels softer. Why is this happening?
Answer ✅
Footfall wins can hide mix, pricing, and availability drift. If more sales come from promos, if size/color availability is uneven at peak hours, or if returns rise on a few categories, the totals look good while quality of revenue weakens. SSM-Audit adds a stability band beside the KPIs you already track so you can see whether growth is repeatable or promo/stockout-driven—before it dents margin and loyalty.
What the bands would have shown 📊
• Average basket size sliding from A+ to A0 / A- (promo-heavy baskets)
• Full-price sell-through degrading to A- (deeper markdown reliance)
• Peak-hour stockouts (top SKUs) worsening to A- / A– in specific aisles
• Return rate by category tilting A0 → A-, concentrated post-promo
• Upsell/cross-sell attach slipping to A- (staff load or planogram issues)
What to do now 🛠️
- Split by lane: promo vs full-price, peak vs off-peak, top 50 SKUs vs long tail.
- Stabilize availability: pre-allocate fast movers for peak windows; micro-replenish during lull periods.
- Tighten promo design: fewer “wide” discounts; focus on entry SKUs with low return propensity.
- Lift basket quality: refresh end-caps/attach scripts on 3 best cross-sells by store cluster.
- Return reduction loop: fix top defect reasons (fit, quality, info); adjust sizing depth in affected clusters.
- Report a quality panel: basket band, full-price band, stockout band, return band—review weekly.
How SSM-Audit helps (practicalities) 🌟
- No additional infrastructure: runs beside your POS, inventory, and BI exports.
- Numbers unchanged: the KPIs you trust stay the same; stability is a read-only overlay.
- Easy to use: spreadsheet/BI friendly; one lightweight weekly review per store cluster.
- Universal language: A++ / A+ / A0 / A- / A– aligns merchandising, ops, and finance fast.
CLI 💻 — try our mini Calculator to identify the drift
(Mini CLI Download Page)
Feed your CSV and see bands and drift at a glance (numbers unchanged).
# Average basket size (value/units)
ssm_audit_mini_calc retail.csv --kpi "Average Basket Size" \
--out bands_basket.csv --plot_kpi "Average Basket Size" --build_id r16
# Full-price sell-through
ssm_audit_mini_calc retail.csv --kpi "Full-Price Sell-Through" \
--out bands_fullprice.csv --plot_kpi "Full-Price Sell-Through" --build_id r16
# Peak-hour stockouts (top SKUs)
ssm_audit_mini_calc retail.csv --kpi "Peak-Hour Stockouts" \
--out bands_stockouts.csv --plot_kpi "Peak-Hour Stockouts" --build_id r16
# Return rate by category
ssm_audit_mini_calc retail.csv --kpi "Return Rate" \
--out bands_returns.csv --plot_kpi "Return Rate" --build_id r16
# Cross-sell attach rate
ssm_audit_mini_calc retail.csv --kpi "Cross-Sell Attach Rate" \
--out bands_attach.csv --plot_kpi "Cross-Sell Attach Rate" --build_id r16
Outputs you will get:
- CSVs with stability bands for each timestamp (e.g.,
bands_basket.csv). - Drift charts per KPI (
--plot_kpi) showing where mix, pricing, and availability slip. - Optional alerts if you enable thresholds in your setup.
Technical notes
Representation: x = (m, a) with a in (-1, +1)
Collapse parity: phi((m,a)) = m
Order-invariant pooling:
U = sum(w_i * atanh(a_i))
W = sum(w_i)
a_out = tanh( U / max(W, eps_w) )
Typical bands (example):
A++: a >= 0.75
A+: 0.50 - 0.75
A0: 0.25 - 0.50
A-: 0.10 - 0.25
A--: a < 0.10
Navigation
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Page disclaimer
Illustrative scenario for research and education. Observation-only; do not use for critical decisions without independent validation.
SHUNYAYA 