December 31, 2008

Your web browsing clickprints

Each individual may have a "clickprint" -- a unique pattern of web surfing behavior based on actions such as the number of pages viewed per session, the number of minutes spent on each web page, the time or day of the week the page is visited, and so on. This is a finding by Wharton operations and information management professor Balaji Padmanabhan in a working paper -- co-authored with Catherine Yang, a professor at the Graduate School of Management at the University of California, Davis -- titled, "Clickprints on the Web: Are There Signatures in Web Browsing Data?".
Using Clickstream patterns, an e-commerce company can distinguish between two individuals with nearly 100% accuracy, sometimes with as few as three Internet sessions, and potentially use that information to deter fraud. The number of sessions needed to identify an individual rises with the number of unique users a site has because there are more people to differentiate.
Important benefits of Clickprint analysis: 
1) An e-commerce company could use clickprints to recognize that a person is using a stolen credit card based on differences in browsing behavior from the card's true owner.
2) Clickprint analysis can help online merchants customize content and recommendations much earlier in a user session than they might otherwise be able to do (since they will not have to require a sign-on before implementing strategies to better serve this customer). Implemented appropriately, such customized online storefronts have increased customer satisfaction.
Important limitations and challenges for using clickprints to profile customers:
It is unclear whether clickprints can be applied on a massive scale such as 100 million unique users; effective fraud detection may require methods that work at such a scale.
Companies will have to discover unique characteristics to their customers, and sometime distinguishing characteristics may well not exist.
Online companies will have to experiment to see what specific browsing behavior(s) need to be tracked to build unique profiles.

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