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?

I just had to include this ad for comic relief.  I don’t know if I can blame Facebook for this one.  If there’s a good demographic segment for the incinerator market, I know I’m not a part of it.  You almost had me with the comment about no mid cycle stirring.  Very close to the click, so close.

In the following series of posts, I lay out a possible path to optimal ad serving based on demographic targeting.  The gist of my method involves Facebook training its advertisers to specify demographics optimally.  As a result, Facebook can expect a higher click-through rate and increased ad serving revenue.  This first post will explore the business problem in more depth.

Problem Statement:

Facebook serves ads based on demographic targeting.  If an advertiser creates a target range that is too restrictive (over targeting), they may loose valuable clicks and impressions.  If advertisers create too broad a range (under targeting), Facebook risks serving irrelevant ads that will yield lower click through rates (CTR). This study employs a combination of text mining and statistical methods to identify and address the problem of over targeting for Facebook pay per click campaigns.
To that aim, two questions must be addressed:

•    How do we identify when an ad is over targeted?

•    What action can we take to help the advertiser improve demographic targeting for the ad group?

Have You Targeted too Much?

An operational definition of over targeting is required to investigate this question.  An ad is over targeted if the advertiser has failed to capture user segments that will produce the desired number of clicks while maintaining the highest Click Through Rate possible.  This could happen if an advertiser selects a demographic range that is within the optimal range but is too restrictive or if an advertiser selects a range that is outside the optimal range all together.  If advertisers were made aware of the optimal range for demographics, they could specify demographics to achieve their desired number of clicks in with the fewest impressions.

Ignoring real world constraints, Facebook could run a designed experiment serving each ad to all its users to determine the distributions of CTR by demographic.  This approach would come with a huge opportunity cost because Facebook would be displaying millions of ads for free.

A more realistic and cost effective approach would be to look at an aggregate set of demographics for responders to ‘similar’ ads from a comparable time period.  Response data for the combined set would be analyzed to determine the optimal ad demographics to maximize yield.  Once the optimal range were determined, Facebook can compare that range to the specified range of recently created similar ads to determine if they are over targeted.  Armed with this data, Facebook can recommend advertisers to modify their demographic specifications resulting in a higher performing campaign.  This series of posts addresses each step of the above approach in detail by applying analytical methods to real world and simulated data.

I hope you readers will jump into the discussion and propose ways to improve the initial approach.  In the next post I’ll describe the text mining process I used to cluster documents


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