Marketing Mix Modeling - A Modern Case Study

MMM’s main job, as I see it, is to help us drive business results by unpacking the data from different channels for us. As a long time performance marketer with a closet passion for all things marketing measurement, earning a certificate in Advanced Attribution was exciting! I wanted to get to know this tool as well as I could, and was eager to have it in my back pocket and see what it brought up for the brands I work with!

I’ve worked with Marketing Mix Models (MMM’s), incrementality testing, multi-touch attribution, scale testing, and other advanced forms of marketing measurement in the past but this certification program brought it all together. With this new level of understanding and toolset, I completed my first project - an Advanced Attribution audit.

The audit was conducted for a burger brand in the food & beverage category within CPG.  

Some of the key questions from the brand we were looking to answer as part of the audit included: 

1. Need a better understanding of media’s contribution to sales - and in particular Retail Media Network performance
2. Validate/verify existing measurement partner results - many of which don’t seem reasonable
3. Identify and recommend a go-forward attribution framework for the business

 

The analytics plan developed to address these questions entailed:

• Data Harmonization - Collection of historical data on sales, media and events…reviewed and processed the data as per modeling needs.
• Preliminary Analysis - Ran trend analysis, correlations, and a basic MMM to understand the fit of individual variables driving sales. Further reviewed the retail store categories and created hypothesis to determine the number of MMMs to run.
• Media Mix Modeling - Ran 1000’s of iterations and 100’s of tranches for different retailer grouping to come up with the best-fit models explaining the drivers of sales across what ended up being 4 primary retail sales channels. Three of which were stand alone, major chains and one was an aggregate of specialty retailers.
• Triangulation - Using the MMM decomps, did a triangulation exercise to understand the impact of media and the value it brings in driving retail sales. 

 

The audit entailed following a structured process to help ensure successful insights and outcomes. This included:

• A business understanding meeting - so we know the brand, media, and distribution strategy - as well as any other market of category dynamics.
• Setting objectives and success criteria for the audit - we want to define what success looks like upfront and ensure delivery against that.
• Collecting media and sales data - this is one of the most critical steps and needs to be done right. Garbage in/garbage out…..bad or incomplete data can undermine the entire project.
• Conducting data QA and application of taxonomy to get the data “model ready”
• Running multiple MMMs to find best fit for purpose - MANY model iterations are run and tweaked to achieve the best V1.0 fit - ideally in the 70% range with a first iteration.
• Taking outputs from marketing mix model decomps and loading the data into the Triangulation tool. This is where the magic happens and we get the views that yield actionable insights.
• Assessing iROAS by media channel, by sales channel
• Formulating mix optimization recommendations 

The Marketing Accounting Framework was oriented around incremental retail sales volume ($$$) driven by media. This was distilled into incremental ROAS (iROAS) by media channel/platform. This was a 4 P&L structure - meaning 4 different models were built - one for each of the 4 retail store groupings. This was the best approach from a model feasibility perspective (grouping smaller speciality retailers) and the business understanding that the other 3 retailers were different/unique enough to warrant their own model and “P&L”.

Going through the data and MMM outputs was super interesting and insightful in and of itself.

 

Some of the key insights that popped from the analysis:

1. Media drives incremental impact/revenue - nearly 10% of sales on an incremental basis with a ROAS of almost $4.00.
2. Different types of media impact sales differently depending on the sales channel and/or retail network specifically.
3. Retail media network performance varied quite a bit by network - RMN A) had a ROAS of 3.9, RMN B) had a ROAS of 2.9 and RMN C) had a ROAS of 0.3
4. Upper-funnel and lower-funnel media tactics performed better than mid-funnel tactics. Upper funnel tactics had an average ROAS of 4.85 while lower funnel tactics had an average ROAS of 2.6.

Overall, the insights suggested there was a growth opportunity within the existing budget based on making some investment shifts to higher-performing (based on incremental sales contribution) media tactics. There also appeared to be an opportunity to drive additional growth through increasing investment levels. The question is can we do this confidently based on working from version one of the marketing mix models? The answer is - probably not.

The model outputs created some initial recommendations, but also a number of hypotheses that would need to be tested. These tests will serve to validate and/or further refine the models and in the shorter term can be used to update iROAS numbers to ensure a high degree of confidence before looking to scale many of the recommended investment shifts. As we continue down the path of test, learn, grow we will continue to fine-tune the models to improve their fit and outputs. Along the way, we will continue to find questions, form hypotheses, and test them in market.

It’s important to understand that practicing data-driven decision-making in and ongoing iterative process. At no point does it become “set it and forget it” because the media, consumer, and business dynamics are constantly evolving. As long as this is the case we need to think of Advanced Attribution techniques like Marketing Mix Modeling to be evolutionary and never static.