“Almost everything we do is a recommendation.” That’s the essential design philosophy articulated by then-Netflix engineering director Xavier Amatriain five years ago, where personalizing and customizing choice is the coin of the realm. “I was at eBay last week,” he said at the time, “and they told me that 90% of what people buy there comes from search. We’re the opposite. Recommendation is huge, and our search feature is what people do when we’re not able to show them what to watch.”
How Marketers Can Get More Value from Their Recommendation Engines
Recommendation engines not only generate useful data for analyzing customer desires; they can be harnessed to make tactical and strategic recommendations for marketers. The “Netflixization of analytics” will increasingly shape how serious marketers make up their minds. Whether managing sales funnel conversions or customer journey touch-points, marketers will spend less time searching for answers than weighing recommended alternatives. In an “almost everything we do is a recommendation” analytics environment, suggested alternatives could range from segmentation advice to possible hypotheses for A/B and multivariate experimentation. Marketers planning mobile promotional campaigns, for example, would spend less time data mining than exploring Amazon/Netflix-type suggestions, such as: Prospects who clicked on this advertisement also responded to that advertisement; 2-for-the-price-of-1 seasonal promotions attract more repeat purchases from gift buyers than 50% discounts, and so forth. At larger firms with broader product and service offers, enterprise marketing recommendations might be framed as, Brand managers like you seeking to appeal to X customers used Y campaigns and Marketers who launched these kinds of promotions also used those kinds of advertisements. Thoughtfully managed, recommendations can prove far more valuable to marketers than for the customers they ostensibly serve.