Mastering the Accuracy, Validation, and Calibration of Marketing Mix Modeling

Marketing Mix Modeling (MMM) is a marketing measurement framework that estimates the impact of external factors (recession, inflation, festivities, natural disasters, and so on), and offline and online marketing initiatives on a brand's overall ROI without using user-level customer data.

While MMM is an incredibly useful tool for marketers to measure the outcomes of their strategies and plan budget allocation across different channels, the question regarding the accuracy of an MMM Model always remains.

What if the data provided by the mix model is inaccurate?

Flawed outcomes from a marketing mix model will result in misleading strategies and a huge marketing budget that you cannot justify to the board members.

You need to validate and calibrate your MMM models to avoid such scenarios and ensure accuracy.

Ensuring Accuracy in MMM

The quality of outputs you acquire from a marketing mix model is directly related to the effectiveness of your marketing campaigns. Hence, accuracy matters in MMM.

The accuracy of your mix model depends on the following factors:

• Quality of data - MMM model requires high quality data inputs. It capitalizes on datasets like sales data (regular and seasonal), spending data, organic marketing data, and so on.

• Incorporating external factors - External factors like recession, shifting consumer behavior, and inflation affect the performance of marketing campaigns. Using these datasets in optimizing your MMM modeling will provide more accurate results.

• Granularity of data - By "granularity," we mean detailed. For example, a brand sold 500 items to its Canada-based customers is generic. But if there is a more comprehensive sales breakdown from each city of Canada, the input becomes much more granular, and the accuracy level of your mix model increases.

• Statistical methods - Marketing mix models apply statistical methods to estimate the impact of marketing campaigns. Depending on the statistical approach it implements, the outcome varies.

Perry Cao, a digital and ecommerce strategist, describes a reliable framework to maintain accuracy of MMM models.

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• Consistent validation - Since the business landscape keeps changing, the accuracy of a marketing mix model framework also comes down to how often it is validated and optimized.

More on validation and calibration of MMM modeling in the following sections.

Validation: The Benchmark for MMM Reliability

A marketing mix model deals with multiple variables, and how they would change over time is beyond our control. While media mix models aim to find the return associated with every marketing investment in an organization, handling multiple external and internal factors makes this process complex.

Different models use different data points and external factors, and the outcomes differ. To identify one reliable outcome, marketers validate the MMM models.

There are three methods to validate a marketing mix model; when needed, you can undertake all these tests simultaneously.

1) Conversion lift tests

Conversion lift tests refer to the tests marketers conduct on different active campaigns to validate the effectiveness of the companies by reducing or increasing marketing spending, switching off an existing channel or adding a new one, and so on. Lift tests aim to validate their decision to relocate budgets to a specific marketing channel.

Michael Kaminsky, Founder of Recast, explains how to perform MMM lift tests correctly.

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Conversion lift tests provide more ground information to a mix model and assist the model in settling down based on the feasible parameters that are aligned with the results derived from the lift test.

However, the challenge with lift tests is that it is tricky to figure out which lift test to choose from: two or more lift tests for calibrating your marketing mix modeling.

Most MMMs assume that marketing performance doesn't change over time, but with lift tests, getting two consistent results from one channel is very common as the performance of marketing channels keeps changing. As a result, it is difficult to identify which lift test to select.

2) Hold-out forecasting

The goal of the hold-out forecasting approach is to test a model's capability of data prediction based on unknown data. Suppose you remove a specific part of the dataset before sharing a wide range of data input and start training the mix model with the remaining data for a certain time period (suppose one month)

Once the training is complete, you will ask the model to predict the performance of a campaign for the next month and then compare it with the actual data (data you removed before) to assess the prediction accuracy level.

You may argue, "why hold out the datasets initially when I can chop off some data in the middle of the training session?"

While possible, we don't recommend removing any data that the model has already seen, as it would not be a proper quality check of the model's capability.

3) Dynamic spending variations

This approach to marketing mix models is about changing budgets for different marketing channels and observing how the mix model's recommendations vary.

For example, say in January, the mix model considers Instagram the most profitable channel and radio ads the least profitable channel. The marketer cuts down a significant percentage of the budget from radio ads and allocates more budget to Instagram.

The goal of dynamic spending variation is to note if these changes improve overall ROI the next month and if the model continues to predict the same factors in the next month as well.

Calibration: Fine-Tuning MMM to Reflect Reality

There are two ways to get an output from any marketing mix model - you either tell the model what to do, or you just add all the inputs and let the model figure out the rest on its own.

Calibrating an MMM model is the first option.

Dimple Dinesh, Marketing Science Partner at Meta talks about this MMM calibration guide published by Warc in her LinkedIn post.

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While it is not impossible to generate accurate outputs from a mix model by just integrating it with required data sources, the level of accuracy is not guaranteed in that situation. By calibrating the model, provide additional information like business scenarios and long-term/short-term goals that an MMM model cannot figure out by analyzing data alone.

Start calibration by conducting lift and hold-out tests to identify the model's prediction capabilities and accuracy. But to calibrate the model, you must accurately measure how different external and internal factors are related. This is known as "ground truth"; without ground truth, you cannot measure a model's prediction accuracy level.

The two popular methods to calibrate a marketing mix model are - Randomized Control Trial (RCT) and GeoLift

Randomized Control Trial (RCT)

RCT identifies a random user group for an advertisement and compares the conversion rate of that group with a control group that didn't come across the same ad. While RCT is a reliable mix model calibration method, to successfully execute it you'll need access to a large user group from your existing audiences. Only Meta's Lift Study and Google's Conversion Lift provide access to such large audience groups.

GeoLift

GeoLift, also known as Geo experiments, focuses on estimating the performance of an ad campaign within a specific geographical location. But this method is not as reliable as RCT because it assumes that the impact of an ad campaign is uniform across a geographical location, but in reality, is a far-fetched dream.

Additionally, GeoLift is more suitable for large enterprises. Small businesses either operate in a very small area or, even though they expand across borders, it is difficult for them to collect high-quality, granular data from multiple cities.

Conclusion

Businesses implement marketing mix modeling to cut down on marketing channels that don't add any value to the overall ROI of a business and focus on profitable channels to distribute budgets strategically.

However, to receive optimum outputs from a marketing mix model, marketers must ensure that the model is providing accurate outcomes. That's precisely why marketing models require frequent validation and calibration.

Automated marketing mix modeling solutions like Lifesight builds and refine MMM models within minutes using real-time data integrations.

Want to reassess your current marketing practices with a faster, more accurate, data-driven, and customized MMM platform?

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FAQs

1) What is the value of calibrating MMM with lift experiments?

Calibrating the MMM (Marketing Mix Modeling) with lift experiments helps validate and improve the model's accuracy. By comparing the predicted results from MMM to the actual lift observed in experiments, adjustments can be made to fine-tune the model's parameters and enhance its predictive capabilities.

2) How do you validate MMM?

MMM validation is done by calibrating the model with lift experiments. This involves comparing the predicted results of the model with the actual lift observed in experiments. By doing so, the model's accuracy is assessed, and adjustments made to improve its parameters if necessary.

3) How accurate are MMM models?

The accuracy of MMM models can vary depending on various factors such as data quality, model complexity, and the level of detail considered. However, when implemented correctly with appropriate data and methodology, MMM models provides reasonably accurate insights into the effectiveness of marketing activities and their impact on sales.