Conference Presentation

Optimizing a Recommendation Engine for Customer Outcomes with Sparse Data

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Every service organization is in the business of making recommendations. Whether it is your knowledge management system that makes a suggestion on what help article to read, your service sales rep crafting a customized pitch for a client, or your strategy team determining how to bundle services to help customers achieve an outcome, we all need a little help in making recommendations. Companies such as Amazon.com, Ebay, and Google have demonstrated that data driven recommendation engines can not only scale well, but also perform as well as most human subject matter experts. But, how can a services organization with limited data go from a subjective recommendation process to a data driven process? We provide practical steps on how to build data-informed and data-connected recommendation engines in presence of sparse consumption data.

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Publish Date: October 20, 2014

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