Facebook Ad Optimization: Optimal Targeting and Comparison

Posted by bkloss | facebook | Wednesday 6 May 2009 4:25 pm

Here’s the third installment of my series on Facebook ad demographic optimization.  In the previous posts, we defined the problem of Facebook advertisers misspecifying demographics .  In the second post, we examined the results of Facebook ad clusters derived from text mining . This instalment picks up where we left off by showing a method for determining the optimal demographics within a homogenious ad cluster.  Once the overall distribution of responses has been charted for each demographic, we explore a way to determine if a single ad shares the same response rate distributions.  Then and only then, can we determine if an ad has been misspecified.

Optimal Targeting

Once clusters of similar ads are identified, the relationship between the probability to click and user demographics can be explored.  Certain levels of the demographic predictors would exhibit a higher click probability than others; men are more likely than women to click on get six pack abs.  Candidate variables for a click model would include, but not be limited to: location, age, sex, education, relationship and interested in.  If probability to click by demographic can be modeled for each ad group, Facebook would be able to identify the target range for an ad group based on the number of clicks per day the advertiser wanted to receive.  If the advertiser’s budget were small, Facebook could serve the ad to the most targeted range.  If the budget were large, they would have to increase the range size serving ads to less and less targeted user groups.

Logistic regression using a selection method can be employed to determine the significance and contribution of each demographic to predict the probability of a click.  The interested in variable may need to be dropped because of insufficient frequency counts (small number of LGBT responders) at different levels of the other demographic predictors to prevent quasi-separation.  Other variables such as keywords and workplaces have too many response levels to be considered in the model without binning or clustering the data.  Additionally, location will need to be binned by region or clustered using census data to reduce the number of predictor levels.  Age will need to be plotted by the logit to determine if it should be entered as a continuous, quadratic or cubit predictor.

Below is the equation for the proposed logistic model:

This model can be used to deterimine the most important facebook demographics


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?


Are YOU a Predictably Irrational Analyst?

Posted by bkloss | Analytics,YouTube | Saturday 21 February 2009 2:31 am

Let’s begin the conversation with a little attention test.  Please count the number of passes the white team makes in the video below.



Creating a Cartesian Product in SAS

Posted by bkloss | SAS | Friday 13 February 2009 6:49 am

Here’s the first of several question/answer posts.

Feel free to write me with your SAS question and I’ll provide the answer in a post.


Suppose I have two SAS datasets, set A contains 5 variables having 3000 unique records and set B contains just 1 variable having 500 unique records (just a bunch of ID numbers). Suppose I would like to create a new dataset where each of the records from set A would be duplicated so that the first record would be repeated 500 times where each of those would be paired with a unique ID from set B. The goal is to have the new dataset with 6 variables (the five from A and the one from B) containing a total of 1,500,000 records produced in this way.

Any help or suggestions you can provide is greatly appreciated.


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