Analytics Strategist

March 5, 2009

The three generations of (micro) attribution analytics

For marketing and advertising, attribution problem normally starts at the macro level: we have total sales/conversions and marketing spends.  Marketing Mix Modeling (MMM) is the commonly used analytics tool providing a solution using time series data of these macro metrics.  

The MMM solution has many limitations that are intrinsically linked to the nature of the (macro level) data that’s been used.  Micro attribution analytics, when the micro-level touch points and conversion tracking is available, provides a better attribution solution.  Although sadly, MMM is more often practice even when the data for micro-attribution is available; this is primarily due to the lack of development and understanding of the micro attribution analytics, particularly the model-based approach.

There has been three types, or better yet, three generations of micro analytics over the years: the tracking-based solution, the order-based solution and the model-based solution.

The tracking-based solution has been popular in the multi-channel marketing world.  The main challenge here is to figure out through which channel a sale or conversion event happens. The book Multichannel Marketing – Metrics and Methods for On and Offline Success by Akin Arikan is an excellent source of information for the most often used methodologies – covering customized URL, unique 1-800 numbers and many other cross-channel tracking techniques.  Tracking normally implemented at the channel-level, not individual event levels.  Without the tracking solution, the sales numbers by channels are inferred through MMM or other analytics. With proper tracking, the numbers are directly observed.

Tracking solution essentially a single attribution approach to a multi-touch attribution problem. It does not deal with the customer level multi-touch experience.  This single-touch attribution approach leads natrually to the last-touch point rule when viewed from a multi-touch attribution perspective.  Another drawback of it is that it is simply a data-based solution without much analytics sophistication behind it – it provides relationship numbers without a strong argument for causal interpretation.  

 The order-based solution explicitly recognizes the multi-touch nature of individual consumer experience for brands and products. With the availability of micro-level touch point and conversion data, order-based attribution generally seeks attribution rules in the form of a weighting scheme based on the order of events. For example, when all weights are zero except the last touch point, it simply reduced to the LAST touch point attribution.  There has been many such rules been discussed; with constant debate about the virtual and drawbacks of each and every one of the rules.  There are also derived metrics based on these low-level order-based rules, such as the appropriate attribution ratio (Eric Peterson).

Despite the many advantages of order-based multi-touch attribution approach, there are still methodological limitations. One of the limitations is that, as many already know, there is no weighting scheme that is generally applicable, or appropriate for all business under all circumstances. There is no point of arguing which rule is the best without the specifics of the business and data context.  The proper rule should be different depending on the context; however, there is no provision or general methodology for the rule should be developed.  

Another limitation of the order-based weighting scheme is: for any given rule, the weight of an event is determined solely based on the order of event and not the type of event.  For example, one rule may specify the first click getting 20% attribution – when it maybe more appropriate to give the first click 40% attribution if it is a “search” and 10% if it is a “banner click through”.

Intrinsic to its intuition-based rule development process is that it does not have a rigorous methodology to support any causal interpretation which is central for right attribution and operation optimization.

Here comes the third generation of attribution analytics: the model-based attribution.  It promises to deliver a sound modeling process for rule development, and provides the analytical rigor for finding relationships that can have causal interpretation.  

More details to come.  Please come back to read the next post: a deep dive example of model-based attribution.

Related post: Micro and Macro Attribution

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4 Comments »

  1. Hi Huayin.

    This post is a really good summary of the evolution of analytics in the arena of micro attribution, for those like me, with little prior exposure to this discipline.

    In my opinion, the tracking-based attribution solution seems an inappropriate one, on the face of it. The one advantage it does have over macro attribution is that response can be measured (tracked) unlike with macro attribution where the analyst has to rely on proxies for response such as circulation data for print, share or rating data for TV, etc. Yet, it is both an imperfect and imprecise solution.

    What you describe as an order-based solution is to a modeled approach what a smart-selection strategy is to a regression model, in the world of direct marketing response modeling. While it is a multi-factorial approach, it is still somewhat arbitrary. Therefore, it remains both an imperfect and still imprecise (maybe less so) solution.

    The model-based approach, as you point out, is methodologically more rigorous and attempts to empirically establish causality. However, it has to be pointed out that it can only do this with observable (measurable) factors. Therefore, the phrase “causal interpretation” should be used somewhat loosely. I wonder if conclusions drawn from a statistical model (based on routinely captured data) should not be “checked” against qualitative (surveyed) assessments of conversion which can capture typically non-measurable factors. Furthermore, there isn’t a one-model solution applicable across markets/industries. In my opinion, this makes the model-based solution also an imperfect one.

    However, in the real world of business analytics, a perfect solution is often elusive and unnecessary. More rigorous data-driven solutions certainly improve the precision of our estimates which in turn results in more effective business strategies.

    On this note, I look forward to your next “deep dive” post.

    Satindra Chakravorty.

    Comment by Satindra Chakravorty — March 6, 2009 @ 3:14 am

  2. Hi Satindra,

    Really appreciate your thoughtful comments, as always. You made a good analogy between the state of analytics in direct marketing and online marketing attribution; my reading experience constantly remind me of how true that is!

    Your cautionary comments on the “causal interpretation” are golden to me and all practitioners in the field. I should emphasize, however, that the model I was referring to is not simply the GLM type of response model we used in direct marketing. In fact, I do not think it is appropriate to say it belongs to predictive modeling at all.

    Thank you again for the thought provoking comments!

    - Huayin

    Comment by huayin — March 6, 2009 @ 4:02 am

  3. I should also add that, the 3rd generation of attribution analytics is NOT a replacement of earlier generation. We still need tracking, perhaps more sophisticated tracking because otherwise we do not have the data needed; and some more refined order-based rules are playing a new role as fields/patterns components for modeling.

    Comment by huayin — March 6, 2009 @ 6:31 pm

  4. [...] those who interested in the attribution analytics challenges, my prior post on the three generations of attribution analytics has some in depth overview of the field. Possibly related posts: (automatically generated)Eight [...]

    Pingback by Media Mix Modeling, the new challenges « Analytics Strategist — March 16, 2009 @ 4:36 am


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