Visitor Conversion with Bayesian Discriminant and Hadoop

Visitor Conversion with Bayesian Discriminant and Hadoop

From the post:

You have lots of visitors on your eCommerce web site and obviously you would like most of them to convert. By conversion, I mean buying your product or service. It could also mean the visitor taking an action, which potentially could financially benefit the business e.g., opening an account or signing up for email new letter. In this post, I will cover some predictive data mining techniques that may facilitate higher conversion rate.

Wouldn’t it be nice if for any ongoing session, you could predict the odds of the visitor converting during the session, based on the visitor’s behavior during the session.

Armed with such information, you could take different kinds of actions to enhance the chances of conversion. You could entice the visitor with a discount offer. Or you could engage the visitor in a live chat to answer any product related questions.

There are simple predictive analytic techniques to predict the probability of of a visitor converting. When the predicted probability crosses a predefined threshold, the visitor could be considered to have high potential of converting.

I would ask the question of “conversion” more broadly.

That is how can we dynamically change the model of subject identity in a topic map to match a user’s expectations? What user behavior and how would we track it to reach such an end?

Reasoning that users are more interested in and more likely to support topic maps that reinforce their world views. And selling someone topic map output that they find agreeable is easier than output they find disagreeable.

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