Why MER and MMM?
Deterministic attribution (the ability to track the online behavior of a specific user or source) was the dominant attribution methodology for most of digital advertising history. However, app tracking transparency, GDPR, and Google’s “3rd party cookie killer” have almost made deterministic attribution obsolete.
As a result, brands and agencies have resurfaced some older, higher-level attribution methodologies to reclaim ownership of advertising ROI: Marketing Efficiency Ratio and Marketing Mix Modeling.
Marketing Efficiency Ratio
What is MER?
Marketing efficiency ratio measures the high-level success of your marketing campaigns. It takes total sales revenue divided by total marketing spending over a specific period of time. Unlike ROAS (return on ad spend), MER isn’t meant to guide advertising decisions at the ad or campaign level. Instead, MER helps you understand how efficient you need to be in your marketing in order to achieve your target profitability.
Why is MER important now?
Deterministic measurement methodologies severely underrepresent advertising impact because advertisers are no longer able to collect user data. Furthermore, workarounds like UTM tracking overweigh last-click attribution at the cost of leveraging more holistic measurement tactics like first-touch or multi-touch attribution (or any view-through attribution).
How do you calculate MER
To calculate MER, divide your total sales revenue by marketing spend across all channels. MER = Total Revenue/Total Ad Spend.
How can you apply MER to your business?
MER becomes a high-level tool to understand how to weigh the impact of paid advertising on revenue. How much revenue do you generate for every advertising dollar spent. You can go further by analyzing marginal acquisition marketing efficiency, which shows you the relative performance of each additional ad dollar. The ultimate goal is to answer the question: when does my next dollar of advertising stop making me money?
Issues with MER
The biggest issue is that this is a reversion to holistic top-level directional analysis vs. deterministic, granular analysis. It’s also hard to account for seasonality, trends, growth stages, market saturation (cue MMM). It’s also hard to weigh the impact of various channels (ex: search, Meta, TikTok etc.)
Media Mix Modeling
What is MMM?
MMMs use aggregate historical time series data to model sales outcomes over time, as a function of advertising variables, other marketing variables, and control variables like weather, seasonality, and market competition. Metrics such as return on advertising spend (ROAS) and optimized advertising budget allocations are derived from these models, based on the assumption that these models provide valid causal results.
Marketing Mix Modeling (MMM) predicts business outcomes through a statistical analysis using multivariate regressions, with marketing tactics and spend as variables. The regressions provide contributions of each variable to outcomes, which are then used to predict what conversions and sales would be with different inputs or marketing mix.
Why is MMM important now?
MMM is the best methodology to determine budget distribution by channel. While it has plenty of pitfalls and inaccuracies, it truly is the best analysis to determine what channels make the greatest impact on revenue and how to distribute budget to maximize ROI.
How do you calculate MMM?
Establish a relationship between the marketing activities variables and sales variables in quantitative linear or non-linear equations. This is done by portraying the impact of marketing activities on sales volume, the volume generated for each unit of effort (effectiveness), sales generated divided by cost (efficiency), and return on investment.
How can you apply MMM to your business?
High-level analysis across the entire media portfolio — great for delivering strategic long-term planning insights into your non-addressable and addressable media — but not ideal for tactical or ongoing insights.
Issues with MMM
MMM estimates marketing impact on historical business outcomes based on probability and can be subject to the correlation vs. causation dilemma. For forward-looking projections, MMM relies on a number of assumptions for non-marketing factors as well as the assumption that channel-level media mix, cost, and response do not diverge from the historic data that is the basis for the demand model.
Also, 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).
Finally, A typical MMM model has three years of national weekly data. Which is sometimes significantly more than a brand is able to provide accurately.
The Big Takeaway?
Advertising works. The only issue is the measurement tools digital advertisers have historically used to track performance and attribution are increasingly ineffective, thus shrouding digital advertising in murky analytics. However, even though the measurement tools are ineffective, it doesn’t mean the impact of advertising has gone away.
The futurist Alvin Toffler once wrote: “The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.” MER and MMM models are the most accurate ways to track advertising performance during this time and must be quickly implemented to ensure advertising spend continues to be a scalable, efficient method for generating customers. Brand and agencies must quickly learn and implement these new models in order to adjust to the market conditions we’re dealing with today.