Download understanding the difference between attribution and media mix

ICROSSING PERSPECTIVE:
UNDERSTANDING THE DIFFERENCE BETWEEN
ATTRIBUTION AND MEDIA MIX MODELING
By Benjamin Hinson-Ekong, Associate Director, Digital Media Strategy, iCrossing
INTRODUCTION
Across the media landscape, one challenge that faces agencies, vendors
and clients alike is a way to holistically assess marketing performance
and allocate media spend across channels in an efficient manner that
will ultimately maximize ROMI (return on marketing investment). Channelcentric media agencies place a strong emphasis on optimizing tactics
within channels: paid search, display, paid social, email and broadcast
media (TV and radio). While it is smart to maximize the knowledge of specialists, it is equally, if not
more important, to assess the best marketing mix on both a macro and micro basis – in essence to
see the big picture.
To date there are two standard practices that address both sides of holistic marketing mix assessment: Digital Attribution Modeling
and Media Mix Modeling. These approaches complement each other with assessment and optimization.
MEDIA MIX MODELING
Media Mix Modeling (MMM) can be thought of as the big brother to Attribution Modeling. Traditionally, MMM is a top-down approach
used to assess how to best allocate spend between channels on a macro level.
Why shouldn’t marketers determine how to distribute their media budget by running simple ROI formulas, or using cost per action
measures (CPA) or just relying on their own opinions? The answer is that the marketing efforts of each channel are not homogeneous.
Clearly there are several different ad formats, delivery mediums and creative types used across multiple channels. Offline factors can
also influence performance metrics. Media Mix Modeling creates response curves for each type of marketing spend and then uses
that information to determine the most efficient marketing mix. MMM’s key strength lies with its ability to analyze offline variables that
UNDERSTANDING THE DIFFERENCE BETWEEN ATTRIBUTION AND MEDIA MIX MODELING
MARCH 2015
affect media performance: market elasticity, marginal profit, point of diminishing returns, seasonality and how other media impacts
consumer behavior.
When done properly, MMM takes historic data into account, and is usually performed once a year (although some companies run it
on a quarterly basis). It can help marketers determine ROI from each channel, and predict expected returns from future media spend.
In essence, MMM assesses the cause and effect of changes to the marketing mix. Traditionally, MMM has been the best fit for brands
that focus more on offline channels, but those lines are blurring in today’s digital age as companies across the board integrate their
offline and online media channels.
With that said, MMM does have its limitations, namely:
+ The variables that inform the model can change after the modeling data is collected and analyzed, making the model obsolete
for the long term (e.g. media budgets may change after the model source data is compiled).
+ MMM does not measure long-term brand equity, hence it is limited to short to medium-term ROMI forecasting.
+ MMM may not be the best tool to speculate investment levels for new products.
ATTRIBUTION MODELING
If MMM focuses on a top-down approach through channel allocations, then Attribution Modeling (specifically digital attribution)
focuses on a bottoms-up approach that involves more granular planning and optimization on a more frequent basis. A true attribution
model goes beyond a basic path report that is available on many generic platforms (e.g. Google Analytics, DoubleClick, Omniture
Site Catalyst, Webtrends, etc.). It will offer more robust information such as:
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UNDERSTANDING THE DIFFERENCE BETWEEN ATTRIBUTION AND MEDIA MIX MODELING
MARCH 2015
+ Identify the most influential conversion points in a consumer’s digital journey.
+ Utilize an advanced algorithmic approach to evaluate performance and automatically update results.
+ Understand with precision how media assets performed along the conversion path.
+ Enable intelligent optimization recommendations, such as how should marketers shift budget by tactic, ad group (SEM),
publisher (display), placement (display), keyword (SEM), etc.
+ Allow for scenario planning and forecasting (e.g. what the expected KPI return will be if budget is shifted between channels).
+ Directionally assess the impact of digital efforts.
+ Allow marketers to integrate learnings and recommendations with DSPs (Demand Side Platforms) and DMPs (Data
Management Platforms).
Having a properly vetted digital attribution system in place enables marketers to understand how their channels, tactics and spend
are performing, and allows for planning optimizations to be made on a real-time basis.
Attribution Modeling also has its potential limitations based on the solution/provider chosen, including:
+ Offline partnerships may offer limited match rates, where offline data is matched to an internal data pool, depending on the
particular vertical.
+ Certain pathing approaches may add bias to results. Last touch and first touch models are considered inaccurate by many
experts today as they are subjective and do not take into account the multiple interactions consumers have with a brand.
+ In many cases, results may not account for missing critical offline influences, such as in-person referrals.
+ Weighting assumptions (if the methodology is subjective and/or not holistic) may bias the results.
SUMMARY
Despite limitations, Media Mix and Attribution Modeling can add great value to an organization’s holistic marketing strategy. Both
approaches allow for efficient macro and micro optimizations. They can help marketers make strategic decisions about budget
allocations by providing deeper insights into customer behavior.
In the next post in this series we will take a deeper dive into digital attribution, and review how to choose a potential attribution
partner.
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