Building Profiles for Anonymous Website Visitors

One of the biggest challenges faced by marketers is to convert anonymous visitors into customers. Converting these visitors would require that these frequent anonymous visitors are identified by a unique key along with their behavioral pattern to be able to personalize content or gather more information about this visitor. Based on this information or insight, the marketers need to adapt strategies on their website to secure contact information.


Plumb5 has an automated profiler for anonymous visitors which overlays explicit data and implicit data to build visitor profiles.

Understanding how Plumb5 profiles anonymous visitors using implicit and explicit data:

Plumb5 breaks all visitor information into explicit and implicit data which is modeled on the lines of attention profiling.

Implicit data applies to data of the visitor captured without the visitor revealing it or otherwise, data captured using web analytic cookies. Implicit data comprises of the geo-location of the visitor, their page behavior, browser’s device information and their source.

Example of Implicit Visitor Info: 
This unique visitor from San Diego, mostly browsing from his mobile, has visited skin products on most occasions.

Explicit Data is data gathered from the information revealed by the visitor. Explicit data comprises of anything you ask the customer to fill or answer. From a win-back form which collects the visitors email to responses gathered using in-page polls or questions, are all explicit information of the visitor.

Example of Explicit Visitor Info: 
This unique visitor is a male, has not used our skin product before, and he likes to purchase if there is a COD option.

The data that the visitor is a male and the information of COD purchase is gathered from the customer either during registration or an in-page poll.

Here is a small example on how it works:

All the data tracked or captured is tagged to the cookie data to arrive at the profile for each visitor.

For example, lets assume that if the visitor is on a specific product page, the visitor is giving attention to that product.

Using the scoring method, you could directly add this visitor to the attention segment or use step-profiling to confirm that the user belongs to this segment.

But just because someone gives attention to something, doesn’t mean they really like it. The thing about implicit data is that companies are guessing because you haven’t actually said it. So the script might tag that the unique customer id showing an inclination towards this product, but with a score of 25% positive – meaning they’re not too confident. Now lets say the same visitor comes to the same product page again, then the visitor id inclination score would add up to 50%, which otherwise is based on the scores set by the marketing manager.

Segmentation is highly dependent on how the marketing manager would want to group these visitors, which could be dependent on the kind of campaigns and the kind of responses he generates from these users.

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