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Zafu Helps Fashion Etailers Harness Big Data

Personalized recommendations enhance the customer experience and increase engagement, conversion rates and customer loyalty. Recommendations and personalization features are among the top investment priorities in 2013 for 62% of retailers, according to a study conducted by Forrester Research and Shop.org, titled: The State Of Retailing Online 2013: Marketing & Merchandising.

In response to the growing market for e-Commerce customization, Zafu created a product recommendation and personalization platform designed to help retailers understand shoppers’ unique styles, tastes and body measurements. The Zafu solution focuses exclusively on the challenges unique to eTailers in the fashion apparel space.

The Zafu technology encourages fashion apparel shoppers to spend more time on e-Commerce sites by enticing them to answer simple questions about the offerings they prefer. The experience and data captured then feeds the Zafu platform algorithm. Shoppers’ unique preferences are matched against the Zafu database and the eTailer’s offerings, and tailored recommendations are delivered to each shopper in real time.

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The platform stores and uses customers’ opinion data to analyze and re-rank products for each eTailer. This process increases the relevance of products that site visitors see, and helps eTailers leverage the value of Big Data to become more customer-centric.

Launched in 2012, the Zafu platform already has matched 13 million shoppers to the clothing styles they prefer, according to the company. At Lane Bryant, the personalization technology has had a positive impact “from sales conversion to shopper satisfaction,” stated George Hanson, VP of e-Commerce for Lane Bryant. The Zafu solution “has helped reinforce Lane Bryant as the preferred online shopping destination for the plus size consumer.”

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