Targeted customer approach through intelligent data analysis
The Challenge
Our client is a full-range food wholesaler (kitchen appliances and food). With its own logistics, it supplies restaurants, canteens, cafeterias, and cafés. The aim of the project was to develop a strategic segmentation of the customer base in order to obtain more data and thus knowledge about the interests, similarities, and differences of food wholesale customers, enabling a targeted approach. The starting point was that only basic master data on the customer base was available in the DWH. Hyand was commissioned to identify and analyze more profitable groups and thus support the customer's sales department in acquiring new customers.
Our solution
To implement the project, we successfully worked through the following steps using data science methods:
- Enrichment of customer data through the use of external services (social media channels and map services)
- Segmentation of the customer base using clustering algorithms to identify similar groups
- Use of a shopping basket analysis to recognize product combinations purchased together
Through the analytical evaluations of the customer clusters, we were able to uncover revealing correlations on behalf of our client and build up the client's know-how on the subject of big data and data science.
The result
The derivation of interpretable customer segments enabled us to address customers precisely through sales and marketing activities. This enabled us to gain more profitable groups for better acquisition of new customers and to recommend product bundles to increase sales. By establishing a trusted advisor, we were able to advise existing customers on how to optimize their product range, thereby increasing satisfaction and loyalty in the long term.
The most important facts in brief
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