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


Facebook Ad Optimization

Posted by bkloss | facebook | Sunday 3 May 2009 4:56 pm

The Setup…

There’s been quite a bit of hubbub about Facebook as of late:  unexpected privacy updates and quick rollbacks, homepage redesign riots, the list goes on.  This series of posts presents a method to address a much more serious question.  How does Facebook ascend to be the crown prince of the advertising world?  In my opinion, the way forward is to leverage the mass amount of data they collect about their users to become a behavioral targeting GOD.  The monetization team at Facebook is chock full of really smart, talented guys working on this very thing.  I know- I’ve spoken with 3 of them over the last few months, yet with all the data about their users and a really talented team it’s clear that I’m not being served compelling advertising.  Here’s a few short examples…

Mission trips Here’s a little ditty asking me to join a Christian Mission trip.  I haven’t specified a religion as part of my demographic profile.  Don’t you think this ad may enjoy more success when served to an expressly Christian viewer?


Musing on Twitter monetization strategies…

Posted by bkloss | Business Models | Sunday 8 February 2009 6:25 am
The last few years have proved this bird can fly .  But can Twitter
dodge arrows?  Only time will tell.

I’ve been on Twitter for a few years now but it’s never managed to capture my full attention.  What has been taking up my attention is reading the seemingly never ending stream of recent posts where everyone knows the  best way to monetize Twitter .  One idea that’s been bandied about is the potential to leverage their mass amount of personal data as a monetization strategy through targeted adserving or third party partnerships. Yes, sites like Twitter and Facebook probably have more data about us than our own mothers but their ability to turn this data into dollars is hampered by three factors: