Finding Interests of Visitors through Data Mining
In a previous post, I mentioned one of my current projects at FinScore. In this post, I will discuss another possibility of online targeting when customer data and web profiles are merged together (for customers of a given company).
First, I briefly define an interest group (IG) as an ensemble of visitors with the same interests. Each URL of a given website can be mapped into an interest. Examples of interests are Auto, Lifestyle, Entertainment, Sport, etc. A visitor can belong to zero, one or several interest groups at the same time.
Here is how the process works. First, a set of identified visitors (1) are tracked on the website. Since the pages they visit are recorded, one can deduce their IG. We build a model using as input the CRM data of these visitors and as output their IG (binary variables for each IG). In our case, this model is trained using a decision tree algorithm. All clients of the company, in fact the ones that have never been on the website, are scored using the obtained model. It is thus possible to infer their IG before they visit the website.
One possible use of such score is to decide which ad, content or product to show to a new visitor (that is already a client of the company). It is also possible to use this IG information with another channel such as mail, e-mail or phone in a 1-to-1 marketing context. For example, clients having a high score for the sport IG may be contacted for a sport product, etc.
So, what do you think of this approach? Have you tried similar approaches or completely different ones? I’m looking forward to reading your comments.
(1) “Identified” means that these visitors are also client of the company. We can thus merge CRM data with web behaviors, for example in a “log in” area.
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