Unified Personalization Across 480 Stores and DTC
Illustrative reference architecture. This is a composite engagement template built to show how Mopshy structures enterprise work. It is not a real Mopshy client, and every figure, KPI, and quotation on this page is illustrative rather than measured client data.
Real-time personalization, AI-assisted merchandising, and store-associate clienteling — powered by a unified customer graph across in-store and online.
+9.4%
Same-store comp lift
+22%
Loyalty repeat rate
+14%
AOV in personalized sessions
The bottleneck
Loyalty program had 12M members but no unified profile. Merchandisers set assortments on quarterly cadence; associates had no visibility into a customer's DTC history at point of sale.
The system
- Customer 360 graph unifying POS, DTC, loyalty, service, and returns
- Real-time recommendation service across web, app, email, and in-store kiosk
- Assortment AI ranking SKUs per store-cluster and season
- Associate clienteling app with next-best-action for VIP tiers
Outcomes measured, not promised
12M
Unified customer profiles
<80ms
Recommendation latency p95
+38%
Email revenue per send
94
NPS on clienteling app
What it cost, what it returned
Investment
$5.6M program (year one)
Payback
8 months
Annualized value
$74M attributable revenue lift
Measured via holdout testing across 40 matched-pair store clusters.
How it is built
- CDP-backed customer graph with real-time identity resolution
- Feature service powering recommendations across channels
- Assortment optimization models integrated with planogram tools
- Associate app with offline-first sync in-store
Representative engagement. This case study describes a composite of Mopshy AI's enterprise engagement patterns, delivery methodology, and observed outcome ranges. Company names, individual quotes, and specific figures are illustrative and used to communicate the enterprise pattern rather than to describe a single identified client.
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