Personalization Technology Brief: Traditional Approaches Contrasted with ChoiceStream’s Universal

Personalization Technology Brief Traditional Approaches Contrasted with ChoiceStream's Universal Recommender
After years of research and real-world experience serving over a billion recommendations for some of the world's largest consumer brands, ChoiceStream knows that no single model or technique produces effective recommendation results in all contexts requiring personalization. Rather than leaving it to customers to choose from the dizzying array of approaches to personalization, ChoiceStream has brought its experience to bear in its Universal Recommender - a technology platform that seamlessly blends best-of-breed personalization algorithms to deliver high-performing recommendations for the full range of real-world deployment scenarios.

Personalization Technology Brief
Traditional Approaches Contrasted with ChoiceStream's Universal Recommender
Goals of Personalization The goal of personalization in aiding customer decision making is simple: to measurably increase the financial bottom line. This goal is typically accomplished through the delivery of recommendations from a catalog of items - from media titles, to news, apparel, general retail merchandise, and more. Relevant recommendations make it easier for users to find items of interest, and this discovery leads to higher consumption and greater user satisfaction.
Recommendation functionality can be grouped into two main categories:
. Probabilistic personalization systems, which base recommendations on statistical models; . Rules-based systems, which generate recommendations from manually maintained rule-sets based on individual or institutional knowledge of customer segments and preferences.
Traditional Rules-Based Marketing
A rules-based approach to recommendations has intuitive appeal for those who know their customers well. It builds on one of the most fundamental tenets of modern marketing-segment the target market into groups of customers who have uniform preferences and they will respond similarly to offerings (products, price, promotions, and placements). Once you have your segments profiled, you can use deterministic rules to market offerings to the segments most likely to choose them and avoid offerings these segments are unlikely to choose.
However, the marketer's notion of a segment is really born of a logistical necessity-customer segmentation schemes are created because it is typically impossible to tailor offerings to every individual customer in a market. The compromise forces marketers to make the age-old tradeoff of how many segments to create and along what lines to define them. The greater the number of segments, the more homogenous the members, but the closer the marketer comes to trying to market to every individual customer. The smaller the number of segments, the easier it is to manage and track them, but their membership is less homogenous, and the segments less meaningful-the segmentation scheme looks more like the market as a whole, with many buyers whose preferences don't align.
ChoiceStream, Inc. CONFIDENTIAL 2
While a rules-based approach to recommendations makes intuitive sense in the context of traditional segment-based marketing, the approach has essentially the same limitations as segment-based marketing itself. Creation of effective rules relies on an explicit, pre-existing understanding of ever-changing customer behaviors and preferences, and how they relate to the available offerings. In other words, it relies on some notion of market segmentation. Just like a segmentation scheme that has too many segments, a rule set that is designed to precisely target groups of similar customers is complex and unruly to manage. Conversely, a rule-set that is simple to manage is a blunt marketing instrument, treating the members of the target market as though they are identical to one another.
Figure 1 shows a simple hypothetical example of these concepts and the difficult compromise marketers must make when limited by a rules-based approach to recommendations. A bookseller defines an initial segmentation scheme based on its knowledge of the market for books. It includes two segments: male and female customers. The corresponding recommendation rule is "show the female-oriented products to women and the male-oriented products to the men." In th... [download for more]