Analytics Strategist

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.


  1. Hi Huayin.

    Great posts on attribution. I wanted to re-state what I think you are saying to make sure I am following it and also want to ask for some clarification.

    The macro attribution case:
    The goal is to answer questions like – how would sales volume change if the level of spending in media were to change? For example, a marketing executive might ask what would happen if he pulled out all the money currently spent on buying a spot on TV channel XYZ during football telecasts? Or, what would happen if he re-distributed this money such that 50% remained in this media and the rest was used to buy a print ad in a local weekly?

    Questions like this can be answered by:
    1. modeling the relationship between historical spend in these media and sales, over time and
    2. using numerical estimates of these relationships in a scenario-testing process.

    Typically, the independant (spend by media type, other factors) and dependant (sales) attribute data are aggregated over time intervals (weekly, monthly, quarterly, etc.) and a statistical relationship is estimated.
    No individual level data is required.
    The target of such an analysis might be the entire book of business or a part thereof but there is no concept of a solicted population and responders and non-responders.
    The model here is a causal model but it does not connect an action from the marketer towards an individual to the reaction from that individual; rather, it connects actions from the marketer aimed at the marketplace as a whole to reactions from the marketplace as a whole.

    The micro attribution case:
    The goal here is to answer questions like – how would response to the Back-to-school promotional event change if more money was spent on emails and less spent on banner ads? The question is conceptually the same but only as it relates to a specific event.

    One possible way to go about answering the question –
    the BTS promotional event garnered 1000 sales. $5000 were spent on the email campaign; 10K emails were delivered and 700 sales resulted from these 10K emails. $20K were spent on banner ads and 300 sales resulted from this media. In this hypothetical case, the email media was approx. 10x as effective as banner ads on a response per $ spent basis and 70% of sales came through the email channel.
    To me, this is more about tracking and using the last click methodology to attribution.

    Another way to look at this –
    gather individual level data on
    recieved email (yes/no)
    clicked through to order page (yes/no)
    visited page(s) with banner ad (yes/no)
    clicked through (yes/no)
    received email and visited page with banner ad
    received email and visited page with banner ad and clicked through from email
    received email, did not visit page with banner and clicked through to order page from main .com page
    purchased (yes/no) etc.
    and build a statistical model, a behavioral model. From a model like this, one can estimate the contribution of different factors on sale.

    Is the latter closer to what you are advocating?

    Satindra Chakravorty.

    Comment by Satindra Chakravorty — March 3, 2009 @ 6:27 pm

  2. Satindra, thank you for reading my blog. Your comments help me to understand how to make my thought clearer, both for myself and for presentation purpose. I think the question you asked are relevant to other readers as well, so I put my response in my other posts. Please read “attribution problem is data analytics problem” as well as “the three insights on attribution analytics”.

    Comment by huayin — March 4, 2009 @ 7:03 pm

  3. […] has not touch the attribution problem straight on.  It failed to make the distinction between micro and macro attribution problems; there is also no discussion of the relationship between attribution and  optimization […]

    Pingback by Media Mix Modeling, the new challenges « Analytics Strategist — March 16, 2009 @ 4:35 am

  4. […] post: Micro and Macro Attribution Possibly related posts: (automatically generated)attribution problem is a data analytics […]

    Pingback by The three generations of (micro) attribution analytics « Analytics Strategist — March 25, 2009 @ 2:32 pm

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