The retailer used Amperity to separate almost 400,000 records that had been wrongly merged and unify 800,000 records belonging to individual customers. In total, more than one million previously inaccurate profiles were resolved.

The retailer reduced data latency by 86%, moving from a seven-day delay to fresh data every day. Before Amperity, some customer information lagged 7-10 days before teams could analyze and use it.

The brand combined historical and ongoing transactions from online and offline channels with marketing, store, product, and privacy data. This created a more complete customer view refreshed daily.

More accurate identities gave the retailer a stronger basis for choosing relevant offers and messages. The brand could use more than 100 customized attributes on every profile for analytics, direct mail, and marketing personalization.

Wrongly merged records can combine the behaviors and attributes of different people, weakening the accuracy of customer profiles. The retailer separated almost 400,000 such records and corrected lifestyle classifications for 25% of customers.

AI-driven personalization depends on accurate, current context about each customer. This retailer established that foundation through identity resolution and daily profile refreshes, although the source does not document a specific AI deployment or AI-attributed result.