Recommending appealing items
Three Fruit, by Charles Plaisted
Carefully chosen recommendations can be irresistible.

This post is a brief summary of my in-depth analysis of Peerius, published in a Patricia Seybold Group report in June 2012. The evaluation is based on my Recommendation Solution Evaluation Framework, which details 100+ criteria in 7 categories. It is available as a free download here.
London-based Peerius was founded in 2007 by Roger Brown, who had been CEO of Kay Media Technologies LTD, a media industry consultancy. Their Chief Scientist, Dr. Bruce D’Ambrosio was the founder of Cleverset, an early personalization. CleverSet was acquired by ATG , now Oracle, in early 2008. Andreas Weigend, former Chief Scientist of, is an advisor to Peerius.
Peerius provides personalization and recommendation solutions for roughly 90 retailers and 150 sites, with a strong client base in the apparel and accessories category. Three-quarters of its deployments are in Europe, one fifth in North America, and the remainder in Australia. Its European clients are in U.K., Germany, Denmark, France and the Netherlands. The Peerius Merchandising system is deployed in British and American English, German, Dutch, Danish and French.
Peerius offers personalization, recommendations, and related products which personalize product recommendations and content for visitors based on behavior and/or customer segment.
In the first three months of implementation, Peerius clients achieve an average increase in revenue of 10 percent overall, generated through recommendations. This is a period of A/B testing, so not all customers are shown Peerius recommendations. An overall revenue increase of 10 percent for these three months is an outstanding result. Average contribution to client revenue averages 30 percent; 75 percent of that contribution is from customers immediately purchasing the recommended item. Peerius reports annual growth in recommendations served is 24 percent, amounting to 1.4 billion recommendations in first quarter 2012.
I identified four key strength’s of Peerius’s offering:
1. Peerius uses a broad range of social data, including ratings, reviews andFacebook likes in its algorithms. Using this data, Peerius can recommend only products with a 5-star rating, or only products that have been reviewed. Peerius can also recommend only styles that are popular with the customer’s circle.
2. Peerius’s behavioral segmentation data can be exported to other applications, or posted to the customer profile, a unique feature and an important differentiator. This RFM-focused information helps retailers identify customers who have changed their purchase or visit patterns, and establish campaigns to increase their loyalty.
3. I’m impressed that Peerius’s reporting guides clients on what to do to improve results. For example, activity is grouped based on action to be taken: “High Converting, High Traffic – Preserve them;” High Converting, Low Traffic – Promote them;” Poor Converting, Peak traffic – Demote them.” We very rarely see reporting that directs users on what to do: apparently “what to do” is deemed too obvious to mention, or too complicated to explain.
4. Business people can control campaigns, space, placement and appearance of recommendations without technical assistance.

Published by Sue Aldrich

As a leading authority on worldwide customer requirements, practices, technologies, and governance for personalization, Sue researches the technologies and practices that help marketers get the most useful content in front of customers at the right moment: recommendations, search, discovery, targeted marketing, and web content management. Aldrich is an expert on optimizing the methods that help customers find what they need to make buying decisions and/or to solve problems. She helps clients develop personalization, marketing, discovery, and content management practices that will engage customers and improve results.

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