" Tell me the products that your customers buy together and I will boost your sales on the shop-floor. This is the promise that was kept at the retail group behind ‘beauty and care’ brands like Di and Planet Parfum. "
Data science eases cross-selling on the shop-floor
Orchestrating online and in-store sales is a major challenge for most retailers. Within the broader context of its digital transformation, Distriplus took a closer look at its customer buying patterns to detect cross-selling opportunities. Basically, the retailer applied data science techniques to identity products that are most often bought together. From there, it was able to define a series of ‘most frequent shopping baskets’. These insights, translated into practical tips, are now used to train staff on the shop-floor. Cross-selling while improving customer service.
Distriplus entrusted delaware with the mission to show the potential of data science in the retail sector. The proof of concept built by delaware focused on the definition of “most frequent shopping baskets’. Based on volumes of raw customer data, data scientists identified a series of products that have a high probability of being bought in combination. These insights were then passed on to the staff during trainings. First results in some Di pilot stores already show a significant increase in sales, only after a few days.
More than 3%
This is the sales increase, after only a week, that was seen in the Di pilot store where the POC was started.
Immediate impact
The ‘frequent shopping baskets’ insights , translated into sales tips for staff on the shop-floor, generated extra sales revenues after just a few days.
Data science made easy
Though the transfer from raw transaction data to shopping patterns requires very specific skills, the results of the exercise were made very understandable and actionable for the employees
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