Will performance marketers benefit from MMM?

Performance marketing (which is a funny term - shouldn’t all marketing be performance marketing?) usually means: Pay-Per-Click (PPC), Search Engine Advertising (SEA), Search Engine Optimization (SEO), Social Media Advertising, Affiliates, Email marketing, Influencer Marketing, and Display Advertising.

Performance marketers have always been very good at measurement and analytics and for that reason it’s good to shed light onto the topic of Marketing Mix Modeling (MMM) and performance marketing and whether these two can go hand in hand in the future.

A couple of years ago, the response to whether performance marketing would benefit from MMM was "Absolutely not," but things have evolved from both the industry and vendor perspectives. Let's explore these changes.

Changes in the industry and vendors

Many other web browsers, i.e., Firefox, have been blocking third-party cookies for years now, but that hasn’t created any significant changes in the industry, as Google Chrome is the main browser with their close to 70% market share.

Due to the fact that only now Google is planning to phase out third-party cookies from January 2024 for 1% of users, and finally for all users during Q3/2024, marketers are waking up to this reality that their measurement tactics focusing on click-based attribution (MTA, for example) will be useless in this environment.

Simultaneously, Marketing Mix Modeling, or MMM, is rising like a Phoenix bird from the ashes, as vendors who have believed in and invested in this methodology have moved from one-time projects to continuously updating MMM offerings and developing dashboards to view the results. Concurrently, tech giants like Google and Meta have been developing their own MMM feeds to provide even richer data for modeling, and companies are increasingly using data connectors like Supermetrics, Funnel, and Fivetran to gather their media data.

All these changes have driven MMM from a decades-old approach, trusted by only a few, where ROI was served on a media group level, to a place where daily updating models let you optimize, for example, your Google Search investments. This has proved that sometimes the change starting from a privacy topic will take us to a better place. Let us explain why.

Don’t measure the goal scorer, but the contribution of the team

The main problem with click-based attribution, especially the last-click measurement, is that it tries to credit the conversion to a certain medium, like Google or Meta, and the most likely result is that both mediums have been part of this “goal scoring”. The problem appears when you go to each ad platform's analytics, where they both might give themselves credit for the conversion or the “goal”.

Multi-Touch Attribution, or MTA, generally has a good idea to attribute the conversion to each player “on the field”, but due to changes in third-party cookies, this approach will die because you can't see the path from one digital medium to another.

MMM will do basically the same thing that MTA was doing, attributing the goal to each player, but it also has two other superpowers that didn't exist in the MTA era: baseline and response curves.

• The baseline will answer your question: Would this conversion have happened anyway, or was it due to my media investment?
• Response curves will answer your question: Am I under-investing in this medium, or have I reached a saturation point?

This gives performance marketers the confidence to have the tough discussions with CFOs about the right media investments, as it will be backed by their own data and the elements of baseline (brand-driven sales) and response curves (media saturation).

More granular results for performance marketing

To truly benefit performance marketers, it's not enough to have elements like Baseline and Response curves from MMM and update the results on a daily basis if the results are too high level. The MMM results also need to be on a level that matches how the mediums are planned.

Since performance marketing is often planned with campaign, campaign objective, bidding strategy, or even ad group/set in mind, this is precisely where the earlier mentioned granular data has taken premium MMM providers, like Sellforte, to.

Use cases for performance marketing

1. All performance media ROIs / incremental uplifts in one place comparable to each other

MMM is a great tool for understanding how well your ads are performing on different platforms like Google, Meta, and TikTok, but also for integrating emails, affiliates, programmatic, and so on. It helps you see the return on investment (ROI) for each type of ad you run. This means you can find out which ads are making you more money and which ones aren't as effective. MMM makes it easier to decide where to spend your ad money to get the best results. By using this method, companies see the big picture of their ad performance, helping them to spend smarter and increase their profits from advertising.

2. Annual / Quarterly planning

MMM is really useful for planning for the year or the next few months. It examines past events to aid in making better future plans. This involves reviewing previous outcomes, such as which sales strategies were successful and which were not. With this information, companies can set more effective goals and decide where to allocate their money and efforts. On the Sellforte platform, you can create scenarios, select the best one, and view the optimal media split for any given season.

3. In-channel planning: Campaign objectives

When planning, for example, Facebook ads for the next 12 weeks, MMM can be really helpful. Consequently, you can decide how to split your money between different campaign objectives on Facebook. You can see the results split by:

• Re-targeting
• Re-marketing
• Prospecting
• Brand Awareness
• Lookalike
• …

4. Google search optimization

Similarly to Facebook, you can also optimize for example Google Search spend in Sellforte and see the results split by:

• Target cost per action (CPA)
• Target return on ad spend (ROAS)
• Maximize conversions
• Maximize conversion value
• Enhanced cost per click (ECPC)
• …

TL;DR / Key takeaways

1. Last-click attribution is dead by design and MTA will stop working due to the lack of third-party cookies in Q3/2024
2. More granular data and daily model updates has helped MMM providers to develop to become more relevant for everyday decision making
3. MMM needs to provide the results on a level that the media is planned to be able to be relevant for the media buyer
4. MMM superpowers are baseline and response curves
5. Sellforte provides lot of use cases to optimize performance marketing

Curious to learn more? Book a demo.