Customer story · Women's apparel and accessories retailer

A national apparel retailer recognizes shoppers its CRM missed

The retailer already had a customer file and high-performing physical stores, but almost none of the shoppers detected in stores appeared in its active CRM. Mall and plaza locations also made GPS-based audiences unreliable.

CAMPAIGNWho was reached?An exposed audienceOBSERVED RESPONSEWho came in?Observed visits, not causal liftTHE NEXT QUESTIONWhat changedbecause of it?Incremental impact needsa suitable study design.THREE DIFFERENT QUESTIONS. KEEP THE ANSWERS DISTINCT.
Conceptual illustration—not customer creative or product UI.
Less than 1%

overlap between in-store shoppers and active CRM customers

Less than $8

return-to-store cost per visit, after one month of testing

Over 5:1

ecommerce ROAS, measured on new-to-file customer purchases

Return-to-store cost under $8 per visit and ecommerce ROAS over 5:1 on new-to-file customer purchases, after one month of testing.

The challenge

The retailer's stores converted better than its website. But once a shopper left the store, the retailer lost them:

The first gap was no way to reach in-store shoppers again. Without tracking, the retailer could not drive shoppers back to a store or to its online shop.

A second challenge was gPS data full of false positives. Most stores sit in malls and plazas next to other retailers. Past GPS-based tools could not tell walk-ins from walk-bys.

The remaining issue was no proof marketing worked. With bad data, the retailer could not allocate budget or know whether its programs were working.

The solution

Detect: counting who actually walked in. Adentro (formerly Zenreach) used the retailer's own store WiFi hardware, not GPS. Logic based on signal strength and dwell time separated shoppers inside from people passing by. Walk-in counters at the doors validated the counts.

Identify the right guests: finding the shoppers the CRM missed. Adentro compared in-store shoppers with the active customers in the retailer's CRM. The overlap was less than 1%. The in-store audience was almost entirely people the retailer could not otherwise reach.

Deliver the campaign: two channels, two destinations. Retargeting reached in-store shoppers not already in the active file. Social campaigns drove them back to physical stores. Programmatic display drove them to the ecommerce site. Each shopper could buy in the channel they preferred.

Read the outcome: store and online in one view. Walk-Through Rate™ measured cost per store visit from social. Ecommerce ROAS was measured on new-to-file customer purchases. With both views, the retailer shifted budget based on individual store performance.

Why the combination mattered

Existing store WiFi hardware supplied passive presence signals, Foot Traffic Analytics distinguished entrances from passersby, Customer Data exposed the low CRM overlap, and Retargeting ran store-return and ecommerce campaigns. Walk-Through Rate™ and ecommerce conversion reporting measured those two destinations separately.

The results

  • Less than 1% overlap between in-store shoppers and active CRM customers
  • Less than $8 return-to-store cost per visit, after one month of testing
  • Over 5:1 ecommerce ROAS, measured on new-to-file customer purchases

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