Analytics Strategist

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.

February 23, 2009

attribution is the hottest topic these days …

Why?  there are a few signs:

If web analytics is your profession or your passion, considering these:

    Avinash Kaushik has several posts in recent month:  Measuring Value of “Upper Funnel” Keywords,   Measure Latent Conversions & Visitor Behavior  and this one from almost a year ago: Conversion / ROI Attribution.

    Eric Peterson  has been busy with webinars and presentations, all over attribution problem.  He use a new metric as a foundation for attribution analysis: “Appropriate Attribution Ratio”.  

We all know that all the major web analytics vendors are working hard trying to figure out the right attribution modeling tool to offer.  My recent meeting with a major web analytics vendor also convinced me; it is all about attribution data service and attribution modeling.

Attribution is also the most talked about topic in SEM/SEO today. I can’t think of a search conference does not have sessions focus on attribution; and every SEM tool makers are tooting its solution for atrribution management

From ad-servers: Atlas’s Engagement Mapping and DoubleClick’s Exposure to Convertion Report.

Another variation of name is Multiple Attribution Protocol.

Forrester Research, who lead research in perhaps everything online marketing, has three researches on attribution within the last six months: Attribution by Emily Riley, Search and Attribution by Evan Andrews and Multicampaign attribution by John Lovett et al.  

It is a hot topic and an area not short of differing approaches.  Anyone who is seriously interested should also read Barry Parshall’s fighting post on the right and wrong approaches for attribution modeling.      


February 18, 2009

Rater and Rating puzzle

Filed under: Datarology, Web Analytics — Tags: , , — Huayin Wang @ 10:56 pm

I will not tell you how many times I thought about this puzzle, it is more than a couple of times for sure.  Here’s the setup:

Given a focus topic area, a group of people with diversified background and levels of related aptitude/skill, allowing pairwise conversations so that people can evaluate each other’s level of expertise on the topic but with NO group sharing.  Suppose you can collect a rating matrix where each row corresponds to a rater and each column a ratee, with cell containing the rating.

Q:  What patterns do you expect to find?  Patterns for expert, and patterns for novice?

January 27, 2009

Recommendation Algorithm and Personalization

Filed under: Datarology, Web Analytics — Tags: , , — Huayin Wang @ 6:29 pm

Recommendation algorithm is at the heart of personalization of contents!

Why? The answer lies in the growing importance and availability of data and speed of changes. 

This of course is breaking a tradition where human knowledge and insights drive designs and decisions directly – in case of personalization, many of them are incresingly mix human manual process with data and algorithm driven process and in many other cases, it can be completely data/algorithm driven.

We should really not be too surprised about this if we stop and ask, where the human knowledge and insights come from? It based on data, many different kinds of data.  When the relevant data are sufficiently available and the learning process is well understood, put human effort in between the otherwise automatable processes can only add inefficiency. 

I have just run into this interesting post about  music recommendation, a field rich with many different ways of doing personalization/recommendation.  Here it is: Four Approaches to music recommendations.

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 …

December 30, 2008

The annoying misuse of web analytics

Filed under: Business, Web Analytics — Tags: — Huayin Wang @ 10:04 pm

I really dislike people using Web Analytics as a synonym for web analytic tools and softwares working with web traffic data.  Software is not discipline; wake up people!

The confusing usage comes from the fact, based on my wild guess, that there are some people for whom there is no analytics other than Web Analytics.  Well, get educated!

For those who have trouble with how I felt, please read the following paragraphes:

“Analytics solutions typically fall short in understanding post-impression data …”

“the common limitation of analytics is that it lacks the data insights of offline behavior …”

The misuse of the word “Web Analytics” is corrupting the commnication – it may make sense in the little corner of some people and only within their little corner.

Web Analytics is not refering to a set of software/tools (which includes Google Analytics), it refers to a subdiscipline of data analytics.  Please, stop labeling “Best Web Analytics” when just you really mean just a comparison of web analytics tools.

May 16, 2006

google search trends

Filed under: Advertising, Datarology, Web Analytics — Tags: — Huayin Wang @ 9:35 pm

Google's tool for viewing trends of search: http://www.google.com/trends

You can do comparison too. Here's what I tried:

republican, democrat (and democrat, republican)

regression model,decision tree,neural networks,support vector machine

response model, risk model, predictive model

intelligence,wisdom,smart,knowledge

harvard, yale, stanford

china,india (within US, Germany, United Kingdom)

buddism,taoism,hinduism,judaism,christian,islam

data mining, statistical analysis, analytics

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