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In digital advertising, keeping up with the most effective measurement tools is vital. With changing privacy regulations and technological advancements, traditional ad measurement methods are being disrupted. One significant shift reshaping digital marketing is the resurgence of the Marketing Mix Model (MMM). Not just any MMM, but the Marketing Mix Model SaaS solution, which is proving to be far superior. Let’s explore why.
The digital ad ecosystem has undergone major transformations. For example, Apple’s restrictions on advertiser tracking highlight how deterministic user-level measurement is becoming increasingly difficult. As user-level data becomes scarcer, companies that fail to adapt risk falling behind. The MMM SaaS solution offers an alternative by thriving on natural variations in aggregate data, delivering reliable insights without relying on individual user data.
MMMs excel at working with aggregate data, but challenges arise when marketing strategies vary significantly across channels, particularly with highly personalised digital campaigns. Recent findings from the Harvard Business Review demonstrate that calibrating MMMs using ad experiments can significantly enhance accuracy, improving return-on-ad-spend estimates by up to 25% across various industries.
Niche digital ad campaigns, especially those targeting custom audiences in markets like the U.S., often require substantial calibration adjustments—sometimes as high as 56%. For companies focusing on specific channels or niche markets, a SaaS solution that facilitates frequent recalibration is essential for maintaining accuracy.
As user-level ad measurement becomes more constrained, ad experimentation is evolving. Techniques such as geo-based ad experiments, which target specific geographic regions, are effective for generating data to calibrate MMMs. These methods, already offered by major platforms like Google and Meta, are widely adopted by leading advertisers for their proven success.
The future of MMM lies in its calibration using ad experiments. SaaS solutions offer tools to compare MMM outputs with experimental results, select the best models, and integrate experiment data directly into the MMM. While this might seem complex, most SaaS platforms feature user-friendly interfaces that simplify the process.
A Marketing Mix Model SaaS solution enables frequent recalibration. Factors such as ad spend and the number of digital channels determine how often experiments should be conducted. For instance, a company spending over €1,000,000 per month on ads might need to run five experiments per channel if they are advertising on one or two channels. Frequent calibration ensures that marketing decisions remain accurate and data-driven.
Digital ad measurement is undergoing a seismic shift. With user-level data measurement becoming increasingly challenging, the MMM SaaS solution has emerged as the gold standard. By integrating MMM with experimental calibration, companies can remain agile, data-driven, and ahead of the curve in their marketing strategies.
As the Harvard Business Review suggests, this approach is reliable and effective until new technologies, such as differential privacy, become mainstream. During this transformative period for digital advertising, adopting tools like a Marketing Mix Model SaaS solution isn’t just advantageous—it’s essential.
