the new challenges to Media Mix Modeling

Among many themes discussed in the 2009 Digital Outlook report by Razorfish, there is a strand linked to media and content fragmentation, the complex and non-linear consumer experience, interaction among multiple media and multiple campaigns – all of these lead to one of the biggest analytics challenge: the failure of traditional Media Mix Modeling (MMM) in searching of a better Attribution Analytics.

The very first article of the research and measurement section is on MMM. It has some of the clearest discussion of why MMM failed to handle today’s marketing challenge, despite of its decades of success.  But I believe it can be made clearer. One reason is its failure to handle media and campaign interaction, which I think it is not the modeling failure but rather a failure for the purpose of attribution ( I have discussed this extensively in my post: Attribution, what you want may not be what you need).  The interaction between traditional media and digital media however, is of a different nature and it has to do with mixing of both push and pull media.  Push media influence pull media in a way that render many of the modeling assumptions problematic.  

Here’s its summary paragraph:

“ Marketing mix models have served us well for the last several decades. However, the media landscape has changed. The models will have to change and adapt. Until this happens, models that incorporate digital media will need an extra layer of scrutiny. But simultaneously, the advertisers and media companies need to push forward and help bring the time-honored practice of media mix modeling into the digital era.”

The report limit its discussion to MMM, the macro attribution problem.  It did not give a fair discussion of the general attribution problem - no discussion of the recent developments in attribution analytics ( called by many names such as Engagement Mapping, Conversion Attribution, Multicampaign Attribution etc.).  

For those who interested in the attribution analytics challenges, my prior post on the three generations of attribution analytics provide an indepth overview of the field. 

Other related posts: micro and macro attribution and the relationship between attribution and  optimization.

Eight trends to watch: 2009 Digital Outlook from Razorfish

1. Advertisers will turn to “measurability” and “differentiation” in the recession

2. Search will not be immune to the impact of the economy

3. Social Influence Marketing™ will go mainstream

4. Online ad networks will contract; open ad exchanges will expand

     with Google’s new interest-based targeting, thing looking to change even more rapidly.

5. This year, mobile will get smarter

6. Research and measurement will enter the digital age

     This is an issue dear to my heart and I have been writing about the importance of Attribution Analytics,  Micro and Macro Attribution many times in recent months; directly from the report:

    ”Due to increased complexity in marketing, established research and measurement conventions are more challenged than ever. For this reason, 2009 will be a year for research reinvention. Current media mix models are falling down; they are based on older research models that assume media channels are by and large independent of one another. As media consumption changes among consumers, and marketers include more digital and disparate channels in the mix, it is more important than ever to develop new media mix models that recognize the intricacies of channel interaction.“

7. “Portable” and “beyond-the-browser” opportunities will create new touchpoints for brands and content owners

8. Going digital will help TV modernize

Read the Razorfish report for details.

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.