Micro-Segmentation For
Personalized Marketing

Challenges and Industry

A European luxury goods mall was doing well with attracting new customers, but struggling with realizing the most value out of existing ones. The mall had implemented a loyalty program 5 years prior, but was not seeing substantial gains from it. The primary goal for GFAIVE’s client was to boost sales by increasing the average purchase.

The challenge for GFAIVE was to not only provide the mall owners with a better understanding of their existing customers (their likes, dislikes and churn likelihood), but also more accurate predictions regarding customers' future behavior. The goal was to build a tailored customer intelligence system that would allow the mall to provide memorable experiences to its customers, increase satisfaction and loyalty and be able to run more effective marketing campaigns in partnership with the retailers.



To achieve the client's goal of increasing sales by increasing customer loyalty, frequency of visits and average purchase, GFAIVE built a predictive customer intelligence tool by merging 3 solutions:

1. To increase customer loyalty and frequency of visits GFAIVE merged all of existing customer data to create accurate customer segments that with the help of machine learning algorithms could recognize hidden correlations and factors that influenced customer behavior.

2. To increase the average purchase GFAIVE built a predictive recommendation system that offered the “next best purchase” for each customer by using microsegmentation and customer preferences for that specific segment. The results of this part of the solution could be implemented through personalized email recommendations, or through pop-up notifications in mobile applications.

3. To further increase the average purchase GFAIVE built heat maps of customer paths in malls that identified hotspots and bottlenecks. The solution then analyzed cross-store visits and provided recommendations on how to optimize the positioning of each store to maximize customer convenience and purchases during their visit.



The luxury goods mall obtained a predictive customer intelligence tool that was integrated into its existing customer management systems. It provided insights about each customer segment, which helped maximize marketing efficiency and led to a 13% increase in sales through accurate targeting and personalization.

The tool also helped identify customer footfall hotspots and bottlenecks which allowed the retailer to improve layout changes, resulting in a 21% increase in positive customer feedback and a significant boost in sales.

Technologies Used

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