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ChoiceStream offers pay for performance marketing services for retailers to deliver personalized display advertising and product recommendations. Retailers use ChoiceStream to present relevant products to consumers with the intention of increasing clickthrough and sales conversion.



ChoiceStream uses shopping data from retailers to generate personalized product recommendations for what consumers are likely to be in-market for next. Depending on the situation or context, ChoiceStream applies different models and analysis techniques to the data to create the personalized advertising and recommendations. These techniques include, but are not limited to:

For example, in the case where much is known about a consumer’s interests, ChoiceStream might use CF to determine which products to recommend. However, when little is known about a consumer, ChoiceStream would not use CF as it could produce off-base suggestions (e.g., If you liked this book, you might also like this hair dryer…) Instead, ChoiceStream would use multiple correlation tables and cohort analysis in this case.

ChoiceStream’s technology is based on the principle that effective personalization in real-world situations requires the use of many different techniques depending on the context. ChoiceStream technology analyzes each situation and automatically deploys the ‘best-fit’ technique to provide consumers with personally relevant display advertising and product recommendations.

Solution Details

ChoiceStream offers two performance marketing solutions for retailers: RealRelevance Advertising and RealRelevance Recommendations. ChoiceStream RealRelevance Advertising is a display ad format and landing page combination that enables retailers to automatically generate personalized advertising for each consumer and run them with a single tag across their media buy. It uses consumer shopping data to predict what consumers will be in market for next and combines personalized product recommendations with individually targeted messages and offers.

ChoiceStream RealRelevance Recommendations offers automated, personalized merchandising and marketing based on consumers' purchase and browsing behavior and in-market intentions.


  • Moshe Ben Akiva, Edmund K. Turner Professor of Engineering Systems at the Department of Civil and Environmental Engineering at MIT
  • David Breashears, Filmmaker, Mountaineer, Business Leadership Consultant
  • Esther Dyson, Editor at Large, CNET Networks
  • Lauren Reiss Frank, Director of Marketing & Investor Relations, Value Insight Partners
  • Miles Gilburne, Managing member, ZG ventures Former SVP Corporate Development, America Online, Inc.
  • Pattie Maes, Interim Head of Program in Media Arts & Sciences, MIT Media Lab
  • Ken Novack, Special Advisor, General Catalyst Partners Senior Counsel, Mintz Levin
  • Rose Polidoro, Marketing & Promotions Executive Former EVP Promotions, New Line Cinema
  • Geoff Ralston, Former Chief Product Officer, Yahoo!
  • David Weiden, Partner, Khosla Ventures Former SVP Marketing & Business Development, TellMe Networks
  • Richard J. Zeckhauser, Frank Plumpton Ramsey Professor of Political Economy, Harvard University JFK School of Government

See also


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