Analytics Strategist

May 17, 2012

Attribution Model vs Attribution Modeling

Attribution is a difficult topic, growing into a mess of tangled threads.

I hope this post, and subsequent ones, will help to untangle the messy threads.  I like to start with simple stuff, be meticulous with the use of words and concepts and be patient; after all, haste makes waste.

When an advertiser records a conversion or purchase, some times there are multiple campaigns in the touch point history of the conversion; how do we decide what campaign(s) responsible for the conversion and how should the conversion be credited to each of these campaigns?  This is the attribution problem; a practical issue first raised in digital advertising but in itself a general analytical challenges.  It is applicable to many marketing/advertising contexts, cross channels or within a particular channel for example.

Micro vs Macro

Notice that Attribution is a “micro” level problem: it dealt with each individual conversion event.  In contrast, Marketing Mix Model (or Media Mix Model) deals with “macro” level problem: crediting conversion volume to each channel/campaigns in aggregate.  There are similarity between the two when viewed from the business side; they are quite different analytic problems, different in all major aspects of the analytic process: from data to methodology to application.

Attribution Model vs Attribution Modeling

Advertisers implement business rule(s) to handle this “attribution”, or credit distribution, process.  These rules are generally called “attribution rule” or “attribution model”; examples of it are Last Click Model, First Click Model, Fractional Attribution Model etc..  Rules and models are interchangeable in this regard, they serve as instruction set for the execution of attribution process.

There are no shortage of attribution rules or models being discussed. Anyone can come up with a new one, as long as it does partitioning credit .  The challenge is finding the right one, to choose from too many of them.  In other words, it is the lack of justification for the approach, process and methodology of Attribution Rules/Models that is a problem.

Now comes the Attribution Modeling – a statistical model-based approach to quantify the impact of each campaigns on each individual’s conversion behavior.  It is a data-driven algorithmic approach; it is hot and cool, with an aura of objectivity around.  It is often treated as the secret sauce to unlock attribution and optimization and covered with a proprietary blackbox.

Let me slow down a bit.  I have discussed two important concepts here: Attribution Model and Attribution Modeling. The former refers to the attribution rules; the later refers to the process of generating/justifying the rules. I understand that everyone do not agree with my use of the words, or the distinction between the two; but I think this is a critical distinction, for untangling the threads in attribution discussion.

Domain Expert vs Data Scientist

There are generally two camps when it comes to the generation/justification of attribution model.  The first is the “domain experts” and the second the “data scientists”.  Domain experts take issues with attribution models by pointing out the good and bad, arguing on behalf of common sense, experience and intuition; it is qualitative, insightful and at times interesting but generally pessimistic and fall short when it comes to build a rigorous data-driven solutions. The general principle for justifying attribution is one of two: the influence and the fairness.  The influence principle attributes credit based on the influence of the campaign on conversion, whereas the fairness is often stated generally.

The fairness principle is not a concern for the data scientists camp; in fact, it is all about modeling/quantifying the impact or influence. After all, if you can do the attribution based on precise measurements of influences of each touch points, what other principle do you need? Of course, the problem is often about the promise and less about the principle.  In contrast to the domain experts, the data scientists approach is quantitative, rigorous and data driven. You can argue with the choice of a specific modeling methodology, but the resulting model itself does not require common sense or past experience to justify.

Principle of Attribution:  Influence, Fairness, Optimality

A third principle in picking the right attribution model is optimality, for lack of better word.  Do right attribution models lead to optimal campaign management?  Some argue yes.  Does the reverse statement true? Can optimality be a principle in choosing or justifying attribution model?  These are some of things I will discuss and debate about in my next writeup.

Thanks for reading!

Advertisements

Leave a Comment »

No comments yet.

RSS feed for comments on this post. TrackBack URI

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

Create a free website or blog at WordPress.com.

%d bloggers like this: