In today’s complex marketing landscape, Chief Marketing Officers (CMOs) and digital marketers face numerous challenges. The necessity for an omni-channel approach, the reality of cookie deprecation rendering rule-based attribution unreliable, and varying privacy regulations across countries are just a few of these challenges. Moreover, CMOs are increasingly expected to demonstrate the profitability of their initiatives and optimize the outcomes of marketing budgets that are not slated for expansion. As advertising investments grow, so does the demand for effective measurement.
UMM is the solution most marketers are turning to, to face such challenges.
This four-part guide will explore why UMM is a game changer (Part 1), how generative AI enhances UMM (Part 2), how and when Synthetic Data can be used to improve UMM (Part 3), and the benefits of developing a bespoke UMM model (Part 4).
UMM: The Newest, AI-Augmented Iteration of Marketing Mix Modelling
Marketing Mix Modeling or MMM is a time-tested technique that uses econometric methods to assess and predict the impact of marketing activities on revenue.
MMM differs from Multi-Touch Attribution (MTA) in its statistical foundation, macro-level overview of the marketing mix (including online and offline datasets and external factors), and its focus on long-term strategic planning.
While MMM dates back to the 1950s, it is currently experiencing a revival thanks to advancements in AI and Machine Learning (ML). The advent of open-source tools is modernizing this measurement methodology. These developments have paved the way for the latest iteration: Unified Marketing Measurement.
UMM employs a combination of innovative and traditional methods. It triangulates MMM with MTA, which focuses on more granular, micro-level data, and incremental experiments — considered the gold standard for measuring marketing effectiveness. When implemented together, these three methods yield the most robust and reliable results, as they complement, calibrate, and validate each other’s analyses.
UMM allows marketers to measure the incremental sales generated by advertising campaigns and understand the baseline to evaluate brand awareness. A UMM model can predict future trends by analyzing past data, thereby helping optimize budgets and strategically plan to maximize ROI.
Embracing a Statistical Perspective
In the current technical and social landscape, it is unrealistic to aim for an exact count of which interactions or conversions can be attributed to specific touchpoints. Marketers must acknowledge and adapt to this reality.
The pursuit of precise attribution has led to the development of numerous complex models aimed at defining how to attribute conversions, yet these models still fail to account for all instances.
The ultimate goal is not exact attribution, but rather the ability to assess how various conditions, such as budget choices and external events, impact sales. This understanding can then be applied to and replicated in future scenarios.
It is important to shift from traditional MMM to modern UMM, from rule-based precise attribution to data-driven statistical attribution, and from analysis to heuristics, and a test-and-learn approach.
What You Can Achieve by Using a UMM Model
By utilizing a UMM Model, you can:
- Evaluate the impact of marketing activities on sales.
- Measure marketing ROI across channels, campaigns, segments, and geographies.
- Understand how a set of conditions impacted revenues.
- Infer causal relationships between marketing activities and revenue results.
- Measure incremental lifts on sales or other KPIs.
- Project what-if scenarios.
- Verify probable outcomes of different budget allocations given known or supposed external conditions.
- Improve ROI of the same budget.
- Evaluate the opportunity to explore new marketing channels or enter new markets.
In the next section, we will delve into the contribution of AI and Generative AI to UMM and how these technologies open up entirely new possibilities compared to traditional MMM.
