Ongoing Marketing Mix Model

Marketing Mix Model Introduction

Marketing Measurement Overview has a high-level overview of the Marketing Mix Model (MMM) and how does it compare to other approaches such as traditional attribution and incrementality test.

The Maxma Advantage

Most Marketing Mix Modeling solutions provide only high-level insights once or twice a year. Maxma MMM is different — it delivers ongoing, tactic-level incrementality measurement, allowing you to continuously track and optimize the true impact of your marketing efforts across channels and tactics.

Continuous, Granular Incrementality Measurement:

• Incrementality from Maxma Marketing Mix Model is measured at the week x channel x tactic level.
• Retrospective measurement: Provides a trendline of incrementality for each channel and tactic over time, rather than just a single point estimate.
• Ongoing updates: Delivers weekly updates of incrementality across channels and tactics for the most recent week on an ongoing basis.

*Note: The exact level of granularity depends on factors such as marketing spend per channel and tactic. Maxma automatically determines the most feasible granularity based on the available data. The frequency can also be customized if a different cadence is preferred.

Key Benefits:

• Always Up-to-Date:
  • Fast growing business cannot rely on outdated insights developed from data that is a year old.
  • Onging MMM provides insights that are always up-to-date with the latest data.
• Fast Feedback Loop:
  • Test new ideas and see incrementality performance as quickly as one week later.
  • A strong alternative to tracking-based attribution (without privacy or tracking limitations).
• Seasonality Insights:
  • Businesses with strong seasonal patterns benefit from weekly measurement, revealing how efficiency fluctuates over time.
  • Optimize not just across channels and tactics, but also across seasonality and timing.

How Maxma Marketing Mix Model Works

Maxma MMM is powered by a proprietary algorithm that integrates the latest advancements from open-source tools, academic and industry research, and our own innovations.

Leveraging Large-Scale Geo-Location Data

As mentioned in Marketing Measurement Overview, Marketing Mix Model measures incrementality by modeling fluctuations in marketing activities relative to conversions without the need of tracking individual users. Maxma takes this a step further by using high-frequency, large-scale geo-location data to provide granular, timely, and robust incrementality measurements.

Marketing Mix Model Measures Incrementality based on Fluctuations:

• In its simpliest form, if conversions rise and fall in correlation with increases or decreases in marketing inputs, the channel or tactic is likely driving conversions.
• The magnitude of these conversion changes relative to marketing inputs determines incrementality.

Why MMM powered by large-scale geo-location data is more powerful: Imagine having daily data on conversions and marketing inputs for hundreds of geographical areas. Even within a short period, the fluctuations across geographical areas and days provide a rich dataset to estimate the true impact of marketing inputs on conversions. However, tracking these movements and drawing meaningful insights manually would be overwhelming for humans - but ideal for statistical and machine learning models to make robust estimation. Maxma MMM does exactly that.

• Compared to most other MMM solutions, which often rely on weekly, country-level data, Maxma’s approach uses daily, granular geo-level data, often 1,000x larger in data volume, enabling more robust and granular insights.
• Unlike tracking-based attribution, which follows individual users, Maxma's approach essentially analyzes audience cohorts across geographical areas, bypassing tracking restrictions.

We've also built the infrastructure to collect and process data at scale, addressing one of the key challenges that have prevented this rich data from being widely used in the industry. Check out the Maxma Data Platform for more details.

Attribution Based On the Complete Picture

The incomplete picture of traditional attribution:

• If you rely on platform-reported conversions, you’ll notice that different platforms compete for credit, often leading to duplicated conversion counts.
• Multi-touch attribution (MTA) tools from third-party vendors help slightly, but they still suffer from tracking limitations, as discussed in the Marketing Measurement Overview.

Why Marketing Mix Model Provides a More Complete View: The general advantages of Marketing Mix Model are:

• Considers the full picture: Marketing Mix Model accounts for all marketing activities as well as non-marketing factors that can impact conversion outcomes.
• Use complete and accurate aggregated data: If 10,000 impressions were delivered in California on Jan 1st, that data point is fixed and accurate, regardless of tracking issues.

However, most other MMM solutions using small data sources (e.g. weekly, country-level data) often have difficulty in isolating the impact of individual marketing activities.

How Maxma MMM Isolate the Impact of Individual Marketing Activities:

With large-scale geo-location data, we can observe thousands of different combinations of marketing inputs and the resulting variations in conversions. This variation provides a rich dataset for estimating the incremental impact of each marketing activity.

To extract meaningful insights from this large dataset, Maxma MMM applies advanced Bayesian statistical model to simulate the most likely incremental impact of individual marketing inputs on observed conversion outcomes. This approach accounts for uncertainty and fairly distributes conversion credit across both marketing inputs and non-marketing factors based on their true contribution.

While Bayesian modeling provides more robust estimations, it is computationally intensive and rarely used at scale in the industry. At Maxma, we are pushing these boundaries with our optimized data infrastructure to make large-scale Bayesian modeling feasible.

Adstock

Advertising effects are not always immediate—conversions can occur days or even weeks after exposure. In platform reporting, this is often handled using arbitrary lookback windows with a hard cut-off, which may not accurately capture the delayed impact.

Marketing Mix Modeling (MMM) addresses this using an approach called Adstock, which transforms raw marketing inputs to account for the gradual decay of marketing effectiveness over time. Maxma’s algorithm detects the best-fitting decay pattern from the data. In practice, we often observe that top-of-funnel channels (e.g., Youtube awareness campaigns) tend to have a longer, slower decay, while bottom-of-funnel channels (e.g., retargeting) show a shorter, steeper decay in their impact.

Summary Comparison

To summarize, here's how Maxma MMM compares to most other MMM solutions in the market and tracking-based attribution approaches (e.g. in-platform reporting, third-party multi-touch attribution tools) that are currently widely used:

Getting Started

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