Analytics Strategist

March 4, 2009

attribution: what you want may not be what you need

.. or should I say, what you need may not be what you want?

Attribution problem, particularly the macro attribution problem, is traditionally asking for a way to partition the success metrics to each marketing efforts, in the form of percentages, so that the relative contributions can be measured. The hope is that these percentages can be used, aside from figuring out how to distribute bonus, to guide the optimal budgetting decision.  However, the promise of using attribution as an optimization methodology is flawed. Attribution is basically an additive model of business process where the success can be partitioned as if they are mutually independent. It is problematic when the actually relationship between marketing efforts and their success metric is non-linear – due to either the presence of interaction (in the form of synergy or cannibalization) effects or the intrinsic quantitative relationship.

When the data-driven relationship/model is shown to be non-additive and non-linear, it may not be intuitively clear of how to use it for attribution, i.e. coming up with the percentages.  On the other hand, the non-linear non-additive model should just be what you need for operational optimization, such as budget optimization decision.  This is because true optimization follows the equalization principle of marginal returns, rather than the averages. Attribution percentage is not as necessary for optimization precisely because it is based on the average. It still useful in many cases when the averages and marginals are highly correlated.

This is the basic idea of this post, and the reasons I was asking everyone to come back and read. For fundamentally, you need to optimize your operation; not just a set of percentages for distributing credits.  

The other point I want to make is that the percentages that every attribution is trying to get at, or every attribution rules is trying to produce, only make senses with some types of causal interpretation.  If the data show that one factor/effort has no real influence on success/conversion, then it is conceptually not justifiable to attribution anything to it. Again, the right interpretation of an attribution is based on the affirmation of a causal relationship. This is another reason for why the statistical modeling is fundamentally important.

I am not saying that the conversion model I mentioned is the typical “conversion model” we used in the DM context; it does not has to be exclusively predictive modeling. The special type of conversion model that we are building is a causal model based on empirical data. Much of the predictive modeling techniques do apply, but still there are some differences.  For example, if it is for prediction purpose, proxy variables are as valid as any other variables. It may not be automatically acceptable when building a model that require causal interpretation. 

Correct attribution rules, should be based on sound conversion model. Its implementation in web analytics tools can facilitate the reporting process for insight generation and monitoring purposes. It will have a similar role as what it does now. What I am arguing about it that it should be build based on sound data-driven conversion model, not simply intuitions. My point goes a little further in that I am also arguing the use of conversion models (be it linear or non-linear, additive or non-additive), but not the attribution percentages.

In sum, conversion model will provide what you needed, which is the ability to optimize your operation, but may not be what you wanted with attribution; those percentages that we all like to see and talk about are ultimately less critical than what we thought.  

Please come back and read the next post on a deep dive example of conversion modeling approach to attribution

Comments?

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March 2, 2009

the first 3 insights on attribution analytics

Looking at micro attribution from a conversion modeling framework, there are a few insights we can contribute right away without getting into the details.

1)  The sampling bias

If your attribution analysis used only data from convertors, then you have an issue with sampling bias.

As a first order question for any modeling project, understanding the data sample, therefore the potential sampling bias, is crucial.  How is this relevant to the attribution problem?

Considering a hypothetical, but commonly asked type of, question:

What is the right attributions to banner and search given that I know the conversion path data:
    Banner -> conversion:   20%
    Banner -> search -> conversion: 40%
    Search -> conversion: 25%
    Search -> Banner -> conversion:  15%

Well, what could be with the question?  A standard setup for a conversion model is to use conversion as the dependent variable for the model with banner and search as predictors. The problem here is, we only have convertor cases but no non-convertor cases.  We simply can’t perform a model at all.  We need more data such as the number of click on banner but did not convert.

The sampling bias issue is actually deeper than this.  We want to know if the coverage of banner and search are “biased” for the data we are using, an example is when banner were national while search were regional. We also need to ask, if the future campaigns will be run in ways similar to what happened before – the requirement of modeling setup mimicking the applying context.

2) Encoding sequential pattern

The data for micro attribution is naturally in the form of collection of events/transactions:
User1:
    banner_id time1.1
    search_id time1.2
    search_id time1.3
    conversion time1.4
User2:
    banner_id time2.1
User3:
    search_id time3.1
    conversion time3.2

Some may think that this form of data makes predictive modeling infeasible. This is not the cases.  There are many predictive modeling are done with transaction/event type of data: fraud detection, survival model, to name a couple.  In fact, there are sophisticated practice in mining and modeling sequential patterns that are way beyond what being thought about in common attribution problem discussion. The simple message is:  this is an area that is well researched and practices and there have been great amount of knowledge and expertise related to this already.  

3) Separating model development from implementation processes

Again, the common sense from the predictive modeling world can shed some light on how our web analytics industry should approach attribution problem.  All WA vendors are trying to figure out this crucial question: how should we provide data/tool service to help clients solve their attribution problem. Should we provide data, should we provide attribution rules, or should provide flexible tools so that clients can specify their own attribution rules. 

The modeling perspective says that there is no generic conversion model that is right for all clients, very much like in Direct Marketing we all know there is no one right response model for all clients – even for clients in the same industry. Discover Card will have a different response model than American Express, partly because of the differences in their targeted population, their services, and partly because of the availably of data.  Web Analytics vendors should provide data sufficient for client to build their own conversion models, but not building ONE standard model for clients (of course, they can provide separate modeling services, which is a different story). Web Analytics vendors should also provide tools so that clients’ modeling can be specified/implemented once it’s been developed.  Given the parametric nature in conversion models, none of the tools from current major Web Analytics vendors seem sufficient for this task.

That is all for today. Please come back to read the next post: conversion model – not what you want but what you need.

February 26, 2009

micro attribution analytics is conversion modeling

If you are surprised by the title statement, you are in the majority.  

This is actually a very strong statement and I did not make it lightly. It is saying that micro attribution is an area of data analytics that can be defined and studied with rigorous statistical methodologies. In short, it is more of a science than art or common sense.  Micro attribution problem is more like a response modeling or risk modeling problem than the problem of finding out a fair rule for distributing year-end bonus.  

Does this sound the same as how others describe attribution problem and solutions? 

It is certainly different from those who think the solution to attribution problem is about tracking.  Tracking is important because it provides you the data, but in itself they do not tell you what factors or customer experience have more or less influence on conversion.

It is also different from many who think about “last click”, “first click” etc. when they speak about attribution models.  Those are not data analytics models or statistical models that I was referring to.  One is about intuition-based smart rule; the other is about data-driven behavioral modeling.  The smart rule vs. modeling debate was long over in Direct Marketing, but it is just beginning in web analytics and online, right here in the micro attribution problem.   

It is also different from the many who think this is all about metrics (because of the claim that there is no right solution to attribution :).  It is not about averaging the attribution of first-click and last click. It is not about using engagement metrics as a proxy either. 

It is definitely not the same as those who think we need to wisdom-of-the-crowd type of solution.  The percentage of you who think early keywords should get 15% attribution for “assist” maybe right, but it has no bearing to me.  I do not believe that there is an average truth in any of these, for reasons that I do not believe one retailer’s offer-X response model shouldn’t be used for a loyalty campaign of a telecom company.

It is categorically different from those who hold that there is no right answer to the attribution problem. I agree that there is no perfect model that has no model prediction errors, but that is not a refutation for statistical modeling.  Statistics is founded on imprecision in data and never afraid of counter examples.

It is an approach of simplification, not of complication and certainly not a proposal to bring in psychology, media-logy or astrology into the picture.  In that regard, it could be a spoiler for the fun party we had so far.

Still, it is really just a claim at this point.  Please come back to read the next post: (TBD)

February 25, 2009

micro and macro attribution

What is attribution analytics? I was thinking of quoting a definition from Wikipedia but could not find it. So here’s my version of it:

Attribution analytics is a set of data analytics techniques used to determine the proportional contribution of marketing campaigns to conversions.  

There are two general types of attribution problem:  micro attribution focus on attribution of individual level conversion to marketing campaign touch points; macro attribution focus on attribution of aggregated level sale/conversion to marketing campaigns activities. Both types of attribution are trying to answer the question of how much credit each marketing campaigns should get; the data analytics behind them are in fact very different.

Marketing Mix Modeling (MMM) is one type of macro attribution analytics.  

The data used for macro attribution are commonly time-series data:  sale volume over time and marketing campaign spend data over the same time periods (may also include a few periods prior to that).  MMM is typically statistical/econometric model that use marketing spend data, and others, to predict the sale volume. The estimated relationship will then applied back to the data to figure out the proportional contribution of each marketing campaigns to total sale (or total incremental sale). MMM is a well developed analytics technique that has been around and practiced for decades. Its pros and cons are also well understood.

Macro attribution is mostly used for the purpose of evaluating the effectiveness of marketing/advertising channels, budget allocation and optimization.

Micro attribution on the other hand, uses individual level event data.  When individual is exposed to multiple marketing events (touch points), which events should get credit and how much? The explosive growth of individual level event data and cross channel matching methodology/technology is perhaps the major factor behind the rapidly growing interest in micro attribution. There has been a great deal of misunderstanding regarding to what the right analytics framework for this, and what are appropriate analytics tools for it. Some think that it is all about coming up with smart business rules and protocols while others believe that more rigorous statistical modeling is needed.

Micro attribution is mainly used for tactical campaign optimization.  However, when multiple channels/media events are involved (such as email, banner, ppc and seo etc..) its implication on macro level budget allocation is inevitable. Hence the organizational complications come with it as well. 

Individual event level data is clearly a better source for attribution – even if the interest is only at the aggregate level.  Multichannel marketers have been wrestling with attribution problem for a long time.  Many of the innovation are in the data capturing area, such as custom 1-800 numbers and custom URL etc.. Those are important measurement processes, designed to capture micro event level data to avoid having to do the less precision and less effective macro attribution.  However, in most cases only the last touch point is recorded – because it often happens at the same time as conversion event.  It is a situation that fit neither the micro nor the macro attribution problem above.  From the micro attribution perspective, because it has only one (last) touch point, it is simply a dummy LAST touch point attribution rule.  From the macro attribution perspective, because we know which touch point a sale/conversion was attributed, there is no need for any modeling at all.  Hence the popularity of the use of these types of attribution measurement devices.

The calling for using micro attribution is due to its optimization potential for business operation.  If attribution for the purpose of attribution, i.e. to satisfy political or procedural reporting need, then the last touch point attribution is a perfect solution.  However, it fails mainly because of its inability to capture and understand today’s consumer experience – that is multi-channel, multi-touch in nature.  One touch point analytics paradigm is outdated for understanding today’s consumers. The future is multi-touch, the future is micro attribution.  Hence it is extremely important to understand the right analytics framework for micro attribution.

Please come back to read the next post: micro attribution analytics is conversion modeling.

February 24, 2009

attribution problem is a data analytics problem

Is there anyone out there as frustrated as me with the many different terms and concepts around “attribution”?  For those who haven’t thought about this yet, here’s a sample of the terms related to the discussion:  

attribution management, attribution protocol, attribution problem, multiple touch point attribution, online marketing attribution, multiple attribution protocol, attribution modeling, marketing mix modeling, last-touch attribution, equal-attribution, impression attribution, attribution theory, online-offline attribution, attribution rules …

In this and a few follow up posts I will discuss a few topics that I hope will bring some clarity to this.  

Let me be upfront with my main point: attribution problem is a data analytics problem. I know that few people would argue with me on this, but I think few people have taken this seriously all its implications.  

Since it is fundamentally a data analytics problem, we should start with data.  What is the underline business question requiring an attribution analytical solution? What kinds of data we have, or we need to have, to answers attribution question?  How to translate the business questions into a data analytics questions that match the type of data we have.  What questions are not answerable given the limitation of data, or available analytics tools?  How rigorous is the proposed data analytics strategy:  a heuristic, a rule of thumb, a well-specified model? Are we over or under in our use of data?  Are we over design the analytics and making it more complex than necessary? What are all the limitations and disclaimers associated with an approach?

My sense is that we have not taken a serious look of the attribution problem from a data analytics perspective yet. We know the business problems, but most of us are not expert in data analytics methodology. 

Any comment?

Please come back to read my next post on micro and macro attribution.

December 31, 2008

Attribution Models

In marketing, particularly in search engine marketing, there has been a growing interest in attribution models.  It is perhaps no coincidence that the same period saw a tighten-up budget and increasing demand for accountability – afterall, attribution is the process of how success are credited to its source(s) – a highly contentious field in marketing. 

This is one of the many reasons I expect attribution modeling to atrract even more attention in 2009 – with SEM and multi-channel marketing at the center of it all. 

There are many uses of “attribution”: in arts and academia it refers to crediting the original authors; in performance attribution – a large area covering investment, marketing etc – it refers to crediting results (or partitioning the results) to its sources or its causes; still it has a place in psychology where the attribution processes of behaivor is the focus of study.

Attribution in marketing/advertising world is the process of attributing the success (usually sales or other metrics) to the marketing/advertising activities.  Since most of the time different activities result in different customer touch points, it reduce to crediting sucess to different touch points.  From this perspective, multi-protocol attribution, engagement mapping, marketing mix modeling, even customized 800 numbers are all attribution approach and techniques.

In the follow up post, I will discuss in details the other aspects of attribution modeling; why attribution modeling is important, what are the different type of attribution challenges, how to do it, and finally what are the limitations of the whole attribution modeling approach …

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