Marketing Mix Modeling Is Coming Back

Over the last few years, advertising has seen explosive growth in performance media due to its effectiveness and ability to be easily measured.

However, privacy policy initiatives and shifts in consumer behavior have started to impact the effectiveness of performance marketing, forcing CMOs to shift their thinking on brand advertising.

We're seeing a change in how brands balance brand and performance marketing. At the eTail Palm Springs conference, Estee Lauder shared how they're balancing the two strategies and measuring the success of their initiatives.

Why is it important?

Marketers need help to figure out how best to measure their brand campaigns and balance the long-term investment against the short-term revenue from performance marketing. Estee Lauder, has committed to building brand equity and is turning to marketing mix modeling (MMM) to understand the impact of its marketing efforts.

Marketing mix modeling (MMM) is a statistical analysis of your sales and marketing data to estimate the impact of your marketing tactics and forecast future opportunities.

Since it's a statistical analysis, marketing mix modeling can help identify the impact of difficult-to-measure channels like TV, sponsorships, and other significant brand activations. The model relies on your first-party data, allowing you to measure results even as platforms see signal loss.

The analysis comes with its own set of challenges. The analysis requires a large amount of data to create a model. This usually means daily breakdowns of data across all marketing channels. Capturing this data and regularly keeping it updated can require a tremendous amount of work and diligence.

What should you do?

If you spend more than $5M annually across your marketing channels, you should consider marketing mix modeling as part of your measurement framework.

It should be one of many tools in your toolbox, so consider it across your entire measurement suite. Brands that regularly invest in difficult-to-measure tactics like OOH, TV, sponsorships, catalogs, and events could benefit from regular marketing mix modeling.

The best place to get started with MMM is through a pre-built tool. We recommend exploring Rockerbox, Northbeam, Recast, and Leavened.

As your marketing mix grows and your data becomes more complex, you'll optimally start building custom models and analysis. At this stage, you'll need a team to manage your model and identify additional data that may impact your marketing performance.

TL;DR

Measuring the impact of advertising has become more challenging as consumer behavior shifts and privacy initiatives grow. These changes have renewed interest in marketing mix modeling (MMM).

Marketing mix modeling uses statistical analysis of sales and marketing data to gauge the impact of various marketing tactics, including harder-to-measure channels like TV and sponsorships.

Despite requiring significant data and maintenance, MMM offers valuable insights for brands spending over $5M annually on marketing. For those looking to adopt marketing mix modeling, tools like Rockerbox, Northbeam, Recast, and Leavened can provide a starting point before possibly moving to custom models managed by a dedicated team.

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