Recommendations from Watson Analytics

Although we won't be using Watson Analytics as an online recommendation engine, it does make practical sense for us to use Watson Analytics as the tool to analyze user behavior data and then provide recommendations based upon Watson's ability to identify predictors with the highest prediction strength.

Predictive strength measures how well a predictor variable accurately predicts a target. You use predictive strength to compare the various predictors within your data. Predictive strength is typically presented as a percentage. A predictor with a predictive strength of 100% perfectly predicts a target. A predictor can be a single input or a combination of inputs. We'll see this work later in this chapter.

The fundamental statistical test that determines predictive strength depends on the measurement level of the target. For categorical targets, predictive strength is the proportion of correct classifications. For continuous targets, predictive strength is 1—relative error.

As an example of user recommendation, everyone should know about the www.amazon.com website, and most are likely to also be aware that when Amazon recommends a product on its site, it is not a making a random, or chance, recommendation.

In an article found in Fortune, the following explains:

At root, the retail giant's recommendation system is based on a number of simple elements: what a user has bought in the past, which items they have in their virtual shopping cart, items they've rated and liked, and what other customers have viewed and purchased. Amazon calls this homegrown math item-to-item collaborative filtering, and it's used this algorithm to heavily customize the browsing experience for returning customers. A gadget enthusiast may find Amazon web pages heavy on device suggestions, while a new mother could see those same pages offering up baby products (Amazon's recommendation secret; Mangalindan, 2012).

In this chapter, we will use a file of logged online user behaviors in an effort to identify opportunities for an upstart online retail store to kick-start a program aiming to provide unique individual online shopping experiences.

The premise of this new store is to use Watson Analytics to evaluate available user behavior data, looking at data (either volunteered by the user or determined automatically by the website) to create a customized user experience for each user each time they visit the store's website, in an effort to increase sales.

Information such as images and/or advertisements that were clicked on, previously completed and abandoned purchases, as well as user demographical statistics, will hopefully be used as sources to guide the creation of focused marketing campaigns, coupon offers, and even the look and feel and navigation style of the website, for each shopper.

Table of Contents for Recommendations from Watson Analytics

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Recommendations from Watson Analytics