Retail AI optimization: a Talbot West case study

We helped a major big-box retailer improve its targeted marketing capabilities and increase customer engagement through Cognitive Hive AI.

WORK WITH TALBOT WEST

Executive summary

A big-box retailer needed to personalize customer outreach and improve engagement. We implemented Cognitive Hive AI (CHAI) to integrate siloed data and extract insights from fusing discrete data sources. We were able to more accurately predict customer preferences and optimize promotions compared to baseline.

LET'S TALK DATA FUSION

PROBLEM

The retailer's data was siloed across loyalty programs, demographics, and online behaviors. Marketing campaigns were generic and didn't engage customers or adapt to changing trends.

SOLUTION

CHAI integrated diverse data streams, predicted customer preferences, and enabled real-time targeted promotions through modular AI components tailored to unify, analyze, and act on customer data.

RESULT

The retailer saw a 30% increase in ad click-through rates, a 10% rise in sales, better inventory management, and faster, data-driven adjustments to marketing campaigns for improved customer engagement.

Background

The retailer managed loyalty program data, demographic insights, and online engagement metrics, but the data was siloed. Legacy systems prevented actionable insights, leading to generic campaigns and missed opportunities for precise targeting. The retailer needed a unified, adaptable system to improve customer targeting and react quickly to changing behaviors.

Project highlights

CHAI fused siloed data and extracted valuable insights from it.

Ad click-through rates increased by 30%.

Sales increased by 10%.

The marketing team spent less time optimizing ad spend and were able to invest in other areas.

Objectives

Integrate customer data from multiple sources into one system.
Use AI to predict customer preferences and behavior.
Deliver targeted, real-time promotions to specific customer segments.

CHAI modules and their roles

CHAI is a modular framework for AI implementation. In CHAI, discrete modules perform separate tasks and all cooperate together to a common end. Here are the modules we deployed in this CHAI instance:

Data collection and aggregation module:
This module combined diverse data streams, including loyalty program purchases, publicly available demographic data, and engagement metrics from emails, in-store visits, and online activity. It created a unified repository of customer behavior and preferences, forming the foundation for advanced analytics.
Predictive analytics module:
Using machine learning, this module identified patterns in customer behavior, such as buying trends, seasonal preferences, and responses to promotions. It forecasted product demand and segmented customers into micro-groups for precise targeting.
Ad and promotions cross-referencing module:
This module matched customer segments with tailored promotions and dynamically updated ad placements. Real-time adjustments ensured customers received offers most relevant to their interests, improving the impact of marketing efforts.
Market segmentation analysis module:
Focused on demographic and geographic analysis, this module refined strategies for targeting specific customer groups. It enabled personalized outreach based on factors like age, location, and buying habits, enhancing the relevance of marketing messages.

Results

Higher ad engagement: Personalized promotions aligned with customer interests, increasing click-through rates by 30%.
Boosted sales: Targeted campaigns drove a 10% rise in total sales.
Efficient inventory planning: Demand forecasts reduced stockouts and overstock issues.
Adaptive marketing: Teams used real-time insights to refine strategies quickly.

Challenges and solutions

Data silos: CHAI unified fragmented data streams, providing a complete view of customer behavior.
Scalability: Modular design supported growth for seamless integration of additional data sources.
Data security: Agentic AI safeguarded sensitive information for compliance and secure access control.

Lessons learned

Unified data systems improve customer understanding.
Modular AI adapts easily to changing business needs.
Human oversight enhances accuracy and trust in AI outputs.

Future implications

The system is scalable for future enhancements, such as IoT data integration or real-time in-store tracking. These additions could provide even more precise insights and enable the retailer to maintain a competitive edge.

Conclusion

By using CHAI, the retailer turned fragmented data into a cohesive, actionable system. The modular AI framework enabled personalized marketing, better targeting, and adaptive strategies, driving improved sales and customer engagement.