Web Analytic Matt

Web Analytics as a Marketing Tool: Scorecards
Matt Hopkins posted in Web Analytics, Visitor Segmentation on June 16th, 2007

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We should all know that to do web analytics for web analytics’ sake is not a good thing, all those numbers, a few words, but without meaning they are pretty useless.

As a web analytics consultant I like to find ways in which web analytics data can be used in new and interesting ways to assist in making better business decisions.

This is where I introduce the concept of scorecards, banks and lending institutions use a method called credit scoring to identify people who they consider to be eligible for receiving credit. The way this scoring works is to take a number of factors about each person, such as their gender, age, employment status and where they live. They then apply a value to each factor, so if the person is below 25 years old then they get a score of 1 for age. If they are employed and have a salary of over 30K pounds then they may get a score of 15 for employment status. Add all these scores together and you get a scorecard per person.

Each person’s scorecard then has an overall score which determines how eligible they are for credit and how much credit. Now, currently in web analytics we have the concept of visitor segmentation which takes different visitor behaviours as differentiating factors but I’m not aware of a product that allows scoring of these factors in a similar way to scorecards.

So, what is the benefit of scoring website visitors and what should we score them on?
In my opinion the main thing website visitors do is view pages, or on web 2.0 sites they may trigger events. So let’s say that we apply a score to certain pages or events. If we use the example of an e-commerce site where books are sold, a search result page could have a score of 1, a book description page may have a score of 2 and the sign up page for the latest books email newsletter can have a score of 5.

Now when we take a site visit that includes 2 searches, a view of a book description page and a sign up for the newsletter the total score for this visit is 9. If a visit consists of only 4 searches then the total score is 4.

In this example the same number of page views were present but because we score each of the page views, we know that the initial visit was worth more than the latter visit. This also means that we can group visits together by their score and find out commonalities between high scoring visits and also low scoring visits.

Also there is no reason why we can’t use this technique to filter out website spiders when using log file analysis, because spiders will want to visit every page so they will have outrageously high scores.

Whatever you decide to use this technique for, you will need to decide which pages to score, what the scores will be for each page and the thresholds for visit/visitor scores you are interested in.