Accurately measuring the impact of various marketing efforts remains a significant challenge, even in 2024. As brands strive to understand the full picture of their marketing performance, the concept of triangulation (or calibration, diversified measurement, etc.) has emerged as a vital strategy, and is recommended by giants like Google, Meta and Snowflake. This approach involves combining multiple measurement methodologies—namely Marketing Mix Modeling (MMM), Multi-Touch Attribution (MTA), and experimental testing—to create a more comprehensive and reliable view of marketing effectiveness. In this article I am going to delve into the importance of triangulation, how it works, and why it is essential for modern marketers.
The Evolving Landscape of Marketing Measurement
Marketing measurement has undergone substantial changes over the past few decades. Historically (think 60s-90s), brands relied heavily on probabilistic measurement models such as MMM, which excelled in the pre-digital era's simpler media landscape. With the advent of digital marketing, deterministic data gave way for newer methodologies like MTA, which offered a more precise understanding of user-level interactions across various channels. However, as digital ecosystems have evolved and privacy regulations have tightened, it has become evident that no single measurement approach could provide a complete picture on its own.
Understanding MMM and MTA
Marketing Mix Modeling (MMM)
MMM is a statistical analysis technique that evaluates the impact of various marketing inputs on sales or other performance metrics over time. It incorporates historical data, including marketing spend, impressions, seasonality, and external factors, to estimate the incremental impact of each marketing activity. MMM is invaluable for long-term strategic planning and budgeting, offering insights into overall marketing effectiveness and helping allocate future marketing budgets.
Multi-Touch Attribution (MTA)
MTA, on the other hand, focuses on the user journey, attributing credit to multiple touchpoints that lead to a conversion. By analyzing user-level data, MTA helps marketers understand which channels and interactions contribute most effectively to conversions. This method is particularly useful for optimizing in-channel tactics and making real-time adjustments to marketing strategies.
The Case for Triangulation
While both MMM and MTA offer unique insights, they also have limitations. MMM tends to provide a high-level view, which may lack the granularity needed for tactical adjustments. MTA, although detailed, can suffer from gaps in data, especially with increasing privacy restrictions and the emergence of walled gardens. Triangulation addresses these limitations by leveraging the strengths of both approaches and incorporating experimental testing to validate and refine insights.
Holistic View
By combining MMM and MTA, marketers can gain both a broad strategic overview and detailed tactical insights. MMM informs long-term planning and budget allocation, while MTA provides granular, actionable data for day-to-day optimizations.
Data Validation and Calibration
Experimental testing, such as geo holdout tests or incrementality tests, plays a crucial role in triangulation. These tests help validate the findings from MMM and MTA, ensuring that the models accurately reflect real-world scenarios. For instance, if MMM suggests a high ROI from a particular channel but MTA shows a different picture, an incrementality test can provide clarity and help reconcile the differences.
Adaptive Strategy
The dynamic nature of digital marketing requires an adaptive approach. Triangulation allows marketers to continuously refine their strategies based on a robust combination of long-term trends, real-time data, and experimental results. This adaptability is crucial in responding to market changes, consumer behavior shifts, and evolving competitive landscapes.
Practical Implementation of Triangulation
Implementing a triangulated measurement strategy involves several key steps:
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Data Integration: Collect and integrate data from various sources, ensuring that both MMM and MTA models have access to comprehensive and accurate data sets. This includes digital and offline channels, first-party and third-party data, and deterministic and probabilistic data points.
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Model Development: Develop MMM and MTA models tailored to the specific needs of the business. This involves selecting appropriate metrics, defining conversion events, and setting up attribution windows that align with the customer journey.
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Experimental Design: Design and conduct experiments to validate and refine the models. This could include A/B testing, geo holdout tests, and incrementality studies to isolate the impact of specific marketing activities and validate model assumptions.
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Continuous Monitoring and Optimization: Regularly monitor the performance of both MMM and MTA models, using experimental results to adjust and improve the models over time. This iterative process ensures that the measurement strategy remains accurate and relevant.
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Collaboration and Communication: Foster collaboration between marketing, data science, and analytics teams to ensure that insights from triangulation are effectively communicated and acted upon. This cross-functional approach helps align marketing strategies with overall business objectives.
Conclusion
In an increasingly complex and fragmented marketing environment triangulation offers a powerful solution for achieving accurate and actionable insights. By combining the strengths of MMM, MTA, and experimental testing, brands can develop a comprehensive understanding of their marketing effectiveness, make informed strategic decisions, and drive continuous improvement in their marketing performance. Embracing triangulation is not just a best practice; it is an essential strategy for staying competitive and achieving sustained growth.
Want to keep reading? Follow Ashley McAlpin on LinkedIn for more on marketing effectiveness and check out Rockerbox to see how our MMM + MTA products can help you succeed.
