Redemption increase
Households analyzed
Retargeting campaigns
Generic offers
The challenge
UInlocking consumer data for better offer redemptions
A specialty retailer wanted to personalize pricing and promotions across its multiple 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
Multiple 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
High 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
Redemption growth with localized targeting strategy
Revenue growth
Redemption boost
Optimized promotional outcomes
Boosted store visitation
Prompt revenue response
Increased ROI
Generic offer reduction
Refined audience relevance
Lowered offer redundancy
Greater customer stickiness
Campaign scale
Retargeting launches
Unified business functions
Consolidated targeting system
Improved cross-sell reach
Looking ahead
Predictive analytics
Forecasting trends with precision
Dynamic customer profiles
Adapting to evolving preferences
Omni-channel expansion
Bridging digital and physical touchpoints
