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Convenience · Unmanned storesMeasured casesAudience · HQ merchandising · Store operations

A hypothesis, a shelf change, and a second measurement

Six days and six days. Snacks rose, ice cream fell. Both results are here.

From problem to result

Problem
The back zone drew few visits, and the centre aisle held no clear category focus
Applied and measured
Sales figures alone could not tell us which zone was underperforming, the back of the store or the centre aisle. We measured visits and dwell time by zone for six days with saai insight, formed a hypothesis from that, changed the shelves, and measured another six days under the same conditions.

saai insight

Measured result
Ice cream moved to the back zone, snacks and biscuits to the centre aisle, then we measured again under the same conditions
Field-verified

35% up

Centre aisle · biscuit sales

Index (before = 100)BeforeAfter
  • Visitors+4.3%
  • Snack sales+12%
  • Biscuit sales+35%
  • Ice cream sales-42%

Six days before and six days after, matched for day-of-week composition. Colour marks direction of change only.

Field-verified

12% up (181% up on the dedicated fixture)

Centre aisle · snack sales

Field-verified

42% down

Back zone · ice cream sales

Field-verified

Low three thousands (4.3% up on the previous period)

Visitors

Evidence and scope

05 Re-measureMeasured casesAudience · HQ merchandising · Store operations

Adoption stage

  1. Pilot
  2. Deployed
  3. Scaled

How this was verified

Verified by comparing the client's POS sales data with saai insight measurements over the same period. Source documents and store names are not disclosed.

A real improvement case at a store of a major Korean convenience chain. The store name and absolute figures are banded and reconstructed to protect the client; the rates of change are as measured.

See the decision criteria used in this case on the technology page.

See the Agentic AI technology page

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