230%
Redemption increase
60M
Households analyzed
400
Retargeting campaigns
23%→4.5%
Generic offers
The challenge
UInlocking consumer data for better offer redemptions
A leading specialty retailer wanted to personalize pricing and promotions across its 14 business units. Massive volumes of siloed data – spanning customers, products, demographics, transactions, and more – led to disconnected campaigns and low redemption rates, limiting opportunities for incremental foot traffic and sales.
Key challenges
Siloed data across business units
Ineffective personalization strategies
Low visibility of localized assortment needs
Uncoordinated promotions hampered redemption
The solution
Creating a 360-degree customer view for retargeting campaigns
Customer markers
50 behavioral attributes
Purchase pattern tracking
Life stage and segment focus
Localized offers
ML-driven personalization
Store-level product mapping
Retargeting via customer intelligence
Implementation approach
1
Unified data ecosystem
60M household profiles
Cross-functional collaboration
Centralized data sources
2
AI-powered targeting
Dynamic ML models
Redemption pattern analytics
Targeting refinement
3
Continuous optimization
Data-driven tuning
Collaborative strategy
Iterative model updates
The impact
230% redemption growth with localized targeting strategy
Revenue growth
230%
Redemption boost
Greater offer success
Increased foot traffic
Immediate sales impact
Increased ROI
23.2%→4.5%
Generic offer reduction
Optimized campaign relevance
Reduced promotional waste
Strengthened customer loyalty
Campaign scale
400
Retargeting launches
14 aligned business units
Unified targeting framework
Deepened cross-sell potential
Looking ahead
Predictive analytics
Forecasting trends with precision
Dynamic customer profiles
Adapting to evolving preferences
Omni-channel expansion
Bridging digital and physical touchpoints
