Mining Retail Transaction Data for Targeting Customers with Headroom - A Case Study

  • Madhu Shashanka
  • Michael Giering
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)


We outline a method to model customer behavior from retail transaction data. In particular, we focus on the problem of recommending relevant products to consumers. Addressing this problem of filling holes in the baskets of consumers is a fundamental aspect for the success of targeted promotion programs. Another important aspect is the identification of customers who are most likely to spend significantly and whose potential spending ability is not being fully realized. We discuss how to identify such customers with headroom and describe how relevant product categories can be recommended. The data consisted of individual transactions collected over a span of 16 months from a leading retail chain. The method is based on Singular Value Decomposition and can generate significant value for retailers.


Singular Value Decomposition Recommender System Product Category Shopping Behavior Retail Chain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Madhu Shashanka
  • Michael Giering

There are no affiliations available

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