The Prediction Lover’s Handbook

Assessment tools for better-informing decisions have proliferated. Here’s an insider’s guide to prediction and recommendation techniques and technologies.

Predictive analytic technologies incorporate statistical, rule-based and/or social networking techniques that can either be used to recommend actions for buyers or to predict customer behavior for business objectives. Predictive applications seek to discover and apply patterns in data to predict the behavior of customers, products, services, market dynamics and other critical business transactions. Recommendation engines (often called “recommenders”) attempt to influence or predict what customers want, enjoy or need. They also often incorporate a company’s business objectives and available offerings into the recommendations. Prediction and recommendation applications use one or more techniques to improve the accuracy of their predictions. Our prediction: These techniques will continue to improve and gain broad market acceptance.

Biological responses analysis

What it is: A family of techniques for assessing biological and neurological responses to content or stimuli, including brain wave monitoring, galvanic skin response, eyeball tracking and so on. Used by: Developers of successful television series for children, including “Sesame Street” and “Blue’s Clues”; Boston-based Innerscope Research for assessing responses to advertising and television content. Strengths: Takes guesswork out of questions involving human response to cultural products. Weaknesses: Highly invasive. Difficult to understand reasons behind a particular biological response. Prospects: Will probably grow in popularity with increased understanding of neurological processes despite ethical concerns.

Cluster analysis

What it is: A variety of multivariate statistical techniques (factoring, pca, ica, k-means) that empirically form groups with common traits (clusters) that had not been previously defined. Used by: Platinum Blue Music Intelligence to group sound attributes that are attractive to particular customers; marketing departments in retail and consumer product companies to determine strategies for cross-selling products and to identify new customer segments and product needs. Strengths: Fast and scalable. Usable by companies lacking detailed consumer behavioral data. Weaknesses: Lacks detailed personalization possible with other techniques. Prospects: Useful for analyzing customer behaviors for cultural products, but less so for recommendation and real-time prediction.

Attributized Bayesian analysis

What it is: Attribute-based probability analysis attempts to understand why a customer behaves as he or she does by examining the attributes of the products a customer likes or dislikes. Attributes can be explicit (a movie’s rating or a book’s title) or implicit (descriptions such as “thrilling” or “funny”). Attributive analysis then predicts or finds other products with similar attributes, using Bayesian inference techniques.

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