Facebook Ad Optimization: Ad Clustering

Posted by bkloss | facebook | Monday 4 May 2009 2:10 pm

This is the second post of my Facebook ad serving optimization article.  If you haven’t read my first post in the series , check it out here for a fleshed out version of the problem statement. To recap, we are looking at the problem of identifying Facebook ads that are misspecified, then giving marketers a gentle nudge to help them get the highest CTR.  This post employs text mining to discover similar ad groups.  This would provide Facebook with a basis of comparison to determine if an ad is not optimally targeted.

Ad Text Clustering

The first step to correcting over targeting would entail identifying similar ad groups.  Once these groups are identified, Facebook can then amass demographic and response data into a large data set for later analysis.

Ads are composed of text and pictures describing a product or service.  To evaluate the worth of ad clustering based on textual attributes, a sample of 47 distinct Facebook ads were copied from search result pages.  SAS Enterprise Miner (EM) was used to create and describe ad clusters.  Below is an outline of the text clustering process flow:

This is an upper level overview of the text mining process

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