Web Mining: Short/Long Term User Profile

User112It’s now nearly one year that I manage a project about targeted advertising for a Telco company in Switzerland. The particularity of our approach is the fusion of offline (CRM) and online (web) customer profiles. We build these extended customer profiles (ECP) on a shifting time window. These ECP are then mined to predict some event such as the click of customers on a given ad.

Several factors can influence the results obtained: the quality of the CRM data, the granularity of the web log aggregation, the data mining technique used, the size of the time window to build the ECP, etc. In this post, I will focus on the time window size. Without going into too much detail, there are two choices: short or long term time window. As example, short term can be from 1 to 7 days, while long term can be 7+ days. The choice of the time window size (short or long) is an important decision that will affect the results.

The choice depends on what we want to capture. If we want the recent interests of customers, then a short time window should be used. In this case, only the very recent web activities of costumers are taken into account. It will thus have a bigger variation over the time. In the case of a long time window, the overall customer interest is taken. In our project, we identify the customer at the house hold level (rather than at the person level). It is thus not possible to differentiate between the father and the son who are both using the same internet connection. Knowing this, getting the recent and volatile interest of the customer makes no sense and we have decided to use the long time window.


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Comments Icon4 comments found on “Web Mining: Short/Long Term User Profile

  1. Hi Sandro,

    which are the better key drivers for your click-targeting. The sociodemographic’s from the CRM database or the affinities in web usage?

    Regards, Christian

  2. @Christian: Thanks for your interest. In my models, there are always variables from both side (CRM and web) which are selected. I have tried model based only on CRM data (resp. web data) but accuracy is not as good as a combination of both kind of data. Regarding the number of variables selected in my decision tree, it is the same proportion as the input data (3/4 CRM data, 1/4 for web behavior).

  3. Good article! I agree that CRM is becoming more and more accessible to small businesses as a Web-based application, thanks to the fact that in-house IT professionals are no longer required. Now, applications are accessible online – all you need is a Web browser and internet connection. Our online application, intelecrm (www.intelestream.net/intelecrm), focuses on small and medium sized businesses and provides a unique flat fee pricing model for unlimited users. Anyone shopping for a powerful and affordable CRM application is encouraged to try it for free.

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