I believe the MTA modeling problem is solved with the approach I discussed in the Unusually Blunt Dialogue on Attribution. I have since received some questions about the approach, or the agenda; some related to the contents and others about formatting. Today, I am going to try a simple recap, to address those questions.
First of all, the formatting issue. The format in WP is hard to read. A friend of mine (thank you, Steve!) is kind enough to put the content into MS-Word. Anyone interested in reading the dialogues in a better format, can download it here: the attribution dialogue.
Below are Q&A for other questions:
Q: Is attribution problem solved?
A: Hardly. Attribution problem consists of many challenges: data, model/modeling, behavioral insight, reporting, and finally optimization.
Q: When you started, you were aiming to reach a consensus on Attribution Model and Modeling. Have we reached the consensus? Is this attribution modeling problem solved?
A: Consensus is never easy to build and may never be achieved. I believe I have covered enough ground to build consensus on this issue, so we can move on to other businesses. I believe the MTA modeling problem is solved, but I am open to someone who can convince me otherwise.
Q: Is there any remaining issues not covered in your agenda?
A: Yes. One example of the left out issues is the search – display interaction; we handles part of it, but not completely.
Q: What do you mean?
A: There are two types of interactions: the interaction effect at behavioral level, which is covered in the conversion model, and the interaction effect on media exposure. The latter type of interaction is not capturable by conversion models.
Q: This is quite dense … do we need another methodology to model the likelihood of exposure?
A: I do not think individual level modeling is the right approach – lack of data is not the only challenge …
Q: Ok, if this is so, how can we say attribution modeling is solved?
A: I consider this to be outside the main attribution modeling. This trailing piece may need a different handle – a “re-attribution” methodology?
(more to come)
Analytics Strategist 
You make a good point that modeling the propensity to see ads is a critical part to correct measurement of perfomance of ads, In that it is a critical step in causal inference and separating targeting bias from actual effect. To that end none of the commercial solutions in the “attribution” space are correct or adequate in solving the problem (in fact they detract from focus on the real issue).
However you are incorrect that this is not solvable .. It requires data unification at an enterprise level, but this is achievable – in fact we are providing this solution today to our clients.
Comment by mattpanthony — April 6, 2013 @ 6:01 pm
Thanks for stopping by. i focus on attribution model/modeling only, in this discussion. If you say you do that, I’d like to know how you do it, openly, in details.
Comment by Huayin Wang — April 7, 2013 @ 4:07 pm