How to use MMM to increase your Marketing ROI

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marketing analytics

Why am I writing this post?

I’m Gabriele Franco, the founder of Hybrida Marketing, a growth marketing agency that last year alone managed 12 million dollars of advertising spent for their clients

In order to optimize our work, we developed an internal technology that is now called Cassandra.

Marketing is changing

I’ve been working in marketing since 2016 and in April 2021 we faced an enormous change in our field.

We faced a new “restriction on cookie policy” that ruined the accuracy of almost all the tracking systems we were using.

Why is that?

Let me explain what happened:

Google Analytics, Google Ads, Facebook ads, TikTok ads, and Bing,… all use a measurement system called “Multitouch Attribution”

Multitouch attribution is a system that uses third-party cookies to measure the user behavior on our site and discover from which source or channel each user came from.

This system has been incredibly useful for almost 10 years, but recently it’s facing big inaccuracies.

Tracking systems fail to attribute correctly up to 30% of transactions due to the restriction of the cookie policy of April 2021.

This leads the advertising platforms to raise marginal costs (CPO, CPL, CPA) and reduce the average contribution margin.

Other than that, all marketers that have been making decisions off on data, now feel stuck with inaccurate data that doesn’t describe reality.

Introducing a solution:

In April 2021, all our clients faced a stop sign for growth because we could not continue making decisions on inaccurate data.

We started searching for a measurement solution that did not use cookie technology.

We found out about Marketing Mix Modeling (MMM).

“MMM are statistical techniques applied on historical data to understand the marginal contribution that each single factor has with respect to our sales”

MMM are basically tools that help you study your investment efficiency, and discover new opportunities.

How can they be applied in an organization?

Studying this market we tested all the possible applications of MMM and selected the 3 most important ones.

1. Solving the attribution problem

Marketing budget planning is a complex activity based on the economic returns of each past investment.

We are now facing big problems in attributing digital investments and we never knew a method to measure offline investment attributions.

MMMs are able to discover the contribution of all media investments including offline ones.

You can see through a dashboard how many conversions each channel has contributed.

In the image, we can see on the X-axis all the channels in which we invest.

We have a blu bar that describes the spend share, so the % of how much we spent on each channel

In light blue, we have the % of transactions attributed to that channel

In pink we have the ROI estimation on that investment.

2. How can I predict next month’s sales?

What happens to my sales if I double down on my investments this month?

What happens if I lower my investments by 35%?

If you are in marketing you have asked yourself this question at least once.

The answer to that question is more complex than you think:

  • You should consider tens of factors that influence your sales, process them all together in your mind, and be able to give a prediction.

It is simply impossible for a normal human being to process all these factors simultaneously.

Marketing Mix Modeling help you do that by considering the following factors:

  1. Competitors: Promotion, discounts, advertising investments
  2. Offline Media: Investments in pr, influencers, TV, OOH, Radio
  3. Digital media: Investments in Meta Ads, Google ads, Tiktok Ads, Outbrain,…
  4. Price variation and Promotion: Measure Price elasticity of demand
  5. Seasonality: Discover how much the demand for your product changes for each month in a year.
  6. External Factors: Weather temperature, inflation, Oil Prices, and other factors that could influence the end consumer behavior

3. How can I maximize my multichannel Marketing ROI?

Much similar to a financial portfolio, your returns are based on how you allocate and distribute your investments.

When you have 6 possible channels on which to spend, and more than 15 campaign types:

how do you decide what’s the right amount of budget for every campaign type?

Certainly, there are rules, I tried them, but they require much work, and most of the time are not based on science but on best practices.

Marketing Mix Modeling, instead of using best practices, learn from all your historical data what are the diminishing returns of each channel in which you invested in the past.

It uses then this information, to tell you how to distribute your budget to maximize your ROI

MMM is basically a tool for the financial operations

In the example image, we can clearly see on the left the “Initial Spend” (how much we are spending on each channel now) and “Optimized spend” (how much we should spend in the next two weeks to maximize sales).

It suggest us to increase tv_spend, search_spend & print_spend and to lower ooh_spend & facebook_spend.

This new distribution, according to Cassandra, would lead to an increase of 8.85% in revenue, as we can see in the top right.

Not only the model is able to suggest us how to optimize our allocation but it’s able to predict how many incremental revenue we would get following this course of action.

Are MMM expensive?

If someone knows MMM already, he knows that MMM are usually really expensive.

In fact they are promoted by big consulting firms like Mckinsey, EY, Nielsen and to give you an idea this is the average listing price for this service:

  • 50.000$
  • 3-6 months for delivery
  • Output: one model and one suggestion.

Now, it’s incredibly expensive for a mid size company to invest 50k to receive only one suggestion that can realistically be applied only once.

That’s why we’ve developed Cassandra:

We created a solution for all the companies that invest more than 50000 $ / month on advertising to have a customized Marketing Mix Model that is constantly updated on new data at a fraction of that price

Case Study: How to increase transactions by 13% thanks to a MMM

I love case studies because they can help demonstrate practically how a solution works.

We’ve received a new client that sold “Shippings for e-commerce businesses”.

Its monthly advertising budget was rough: 52.000€/month

We had 3 years of historical data.

Our goal: generate growth through a data-driven budget allocation.

We tried using the google analytics attribution system but we couldn’t learn much.

Most of the transactions we were tracking came from the direct channel.

There was a clear problem with our google analytics, and we couldn’t use it.

Non è stato fornito nessun testo alternativo per questa immagine

We developed a Marketing Mix Model that suggested lowering the budget on Facebook ads and allocating it to google search and Google video.

This action, according to the model would generate 5.5% incremental sales compared to the previous period.

We executed it for two weeks and we noticed a 5.75% increase in the number of transactions.

We then refreshed the model on the new data we collected in the two weeks and received back a new allocation suggestion:

“Lower a little more Facebook ads and reallocate the budget on google discovery”

The prediction of incremental sales, in this case, was 6.6% but after execution, we generated over a 7.6% increase in the number of transactions.

Summary Case Study:

In 1 one month and one week, just modifying the budget distribution we’ve been able to obtain:

  • +13.36% in the number of transactions
  • +42.21% in profit
  • +22% in ROAS
  • -7% CAC (thanks to a geolift experiment)

How can I implement this solution in my organization?

In Cassandra, we have developed a solution composed of 3 steps:

Step 1: In 2 weeks you can receive a personalized MMM for your company with a money-back guarantee offer if you don’t like the outcome.

Step 2: In 2 weeks we create a data infrastructure able to centralize all your data daily and refresh the model every two weeks.

Generate a dashboard for your company: SEE THE EXAMPLE

Step 3: 2 hours of monthly consultancy to transform model insights into experiments that will drive better ROI over time.

If you want to learn more and verify if this solution could bring value to your company I invite you to click on the following link and book a 30 minutes call with me to see if and how we can help you

Click Here To Book a call with Gabe

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