Determining the true relationship between control and exposed audiences is a problem that advertisers have faced for decades. We live in a time when unlimited amounts of media is being consumed. This is coming from a more diverse variety of sources than ever, so how can you know your advertising is effective?
The answer seemingly lies in the vast amounts of data now available to advertisers to analyze exactly where their ad was seen and the accompanying data on the exposed audience. Surely, with such large amounts of data we could easily determine what’s causing conversion and what’s not, but this vast amount of data poses another problem as it presents too large an amount to decipher the cause of true conversions. This is why causal attribution has risen to prominence as being the key in determining conversions.
Causal attribution refers to the reasons that people behave the way they do. It is a cornerstone of psychology and has also served as the bedrock for advertising, or more acutely why some ads work and some don’t.
We can think of how to perform truly effective causal attribution through the analogy of a medical trial. Imagine after years of hard work going through medical school and months of interning to long, sleepless nights in labs, and all your work manifests into a tiny single pill . You can finally hold in your hand a potential cure to diabetes. The clinical trial begins but the initial results are inconsistent and vary wildly, leaving you seriously doubting all those years of struggle. You think that some external factors are affecting the results, but how do you minimize the external effects on your group of control patients? How do you normalize all the factors governing a person’s life every day and at every moment? Ideally your test patients receiving the drug and control patients receiving the placebo would be identical clones of each other and do the exact same things everyday, but this simply cannot happen.
So how can we use causal attribution to solve this?
“In control vs. test studies, we aim to isolate a single variable in order to measure the incremental difference caused by this variable. In our case this is being exposed to an ad, or not. In order to isolate this variable, we attempt to normalize as many other variables as we possibly can.” said John McNicholas the Head of Product Management at TVadSync.
Effective causal attribution reveals itself to be more of a line of elimination as you work progressively towards the actual effect of the advertisement. In doing so we see the strength of true causal attribution lies not in the chosen variable itself necessarily but in the strength of the normalization of other variables.
So in the case of our diabetes trial we can change one variable for the patients (the drug), while keeping the rest of the behaviors as identical as possible, and in the case of measuring TV ad exposure, companies try and replicate a control audience to measure how effective their ad was in driving conversions. Some bias may affect how a control audience is created out of first party data, whereby a control audience is created that favorably skews the results.
Third party data vendors such as TVadSync use new forms of machine learning analysis to analyze a myriad of TV viewing behavioral indicators such as the propensity to watch a TV network, genre, actor or an extensive number of other content consumption variables. This allows the creation of exceptionally similar test and control audiences to accurately measure the effect of TV advertising.
TVadSync can directly detect what people are watching on their Smart TV with 1:1 accuracy, and in doing so solve the problem of determining causal attribution by allowing the ability to narrow down each variable while normalizing every other possible variable within TV viewing behavior. It is only through modern smart reaching TV data companies such as TVadSync that this true causal attribution can be understood, and in turn the effectiveness of TV advertising can be understood.
