Marketing Mix Modeling: A Future-Proofed Approach to Marketing Measurement
Marketers must be prepared to keep up with developments in innovation, consumer behavior, and regulations because the marketing industry is constantly evolving. One big difference on the horizon is the transition to a cookie-free world, as well as the increasing significance of privacy regulations. This shift has completely altered how we think about marketing measurement, making marketers more prepared than ever. Marketing Mix Modeling is one approach that can assist brands in future-proofing their measurement (MMM).
Marketing Mix Modeling is not a new concept; it dates back to the 1960s. The rise of programmatic advertising and cookie-based user tracking, on the other hand, temporarily shifted focus away from MMM. While user tracking provides rich data sets, marketing companies continue to struggle to establish appropriate attribution rules and frequently rely on erroneous last-click approaches to evaluate impact. Advertisers are revisiting MMM as a way to disaggregate performance from user-level tracking as third-party cookies become obsolete and modeling techniques advance.
The Advantages and Limitations of Marketing Mix Modeling
So, exactly what is MMM? It is a statistical analysis that employs regression techniques to assess the impact of independent variables such as marketing activity and seasonality on the dependent variable, sales. MMM has three major advantages: cross-channel measurement, budget planning and forecasting tools, and a future-proof measurement source. It also has some drawbacks, such as less granular insights and slower delivery.
The foundation for a successful MMM implementation is clean data inputs. Historical data must be gathered, and campaign taxonomies must be translated to the appropriate dimensions. After receiving the final historical data, the modeling team runs sophisticated programs that incorporate added modeling techniques, such as ad stocks, to determine how long the impact of advertising lasts. When the final model is delivered, the analysis of marketing impact begins.
If the model indicates that a channel is outperforming its share of media spend, spend is typically increased to capture more value. Conversely, spend is reduced for underperforming channels. Any investment change should be implemented gradually, as results from other measurement methods may differ from what clients have seen. Experimentation is recommended in these cases to validate MMM learnings.
Using Multiple Measurement Methodologies to Future-Proof Marketing Strategies
MMM differs from other types of measurement, such as platform attribution or lift testing. Platform insights metrics provide real-time reporting, allowing media buyers to optimize campaigns based on the most recent performance at any level of granularity, filling the gaps left by MMM in terms of recency and detailed reporting. Incrementality or lift testing is best suited to answering questions about how clients can be certain that the conversions seen in their real-time reporting sources are truly a reflection of marketing efforts.
Given the distinct value that different measurement methodologies provide, many brands employ them all. The best way to ensure long-term success in the changing digital marketing ecosystem is to create a feedback loop between the tools and ensure that the organization is aligned on how the results will be implemented.
Marketing Mix Modeling is a future-proof approach to marketing measurement that can assist brands in answering critical questions about the impact of their marketing activity. MMM provides cross-channel measurement, tools for budget planning and forecasting, and a future-proof measurement source by using regression techniques to measure the impact of independent variables on sales. While it has some limitations, MMM can be used in conjunction with other measurement tools to create a feedback loop between the tools, ensuring long-term success. Marketers who adopt a future-proofed approach to measurement will be better equipped to adapt and succeed as the world of marketing evolves.
How does Marketing Mix Modeling differ from other measurement approaches?
Marketing mix modeling differs from other types of marketing measurement approaches, such as platform attribution and lift testing. Platform attribution provides real-time reporting and optimization based on recent performance, while lift testing measures the incremental impact of marketing efforts. MMM, on the other hand, provides cross-channel measurement, budget planning and forecasting tools, and a future-proof measurement source. While it has some limitations, MMM can be used in conjunction with other measurement tools to create a feedback loop between the tools, ensuring long-term success.
Why is Marketing Mix Modeling becoming more important in a cookie-free world?
As third-party cookies become obsolete, marketing companies are revisiting MMM as a way to disaggregate performance from user-level tracking. While user tracking provides rich data sets, marketers often struggle to establish appropriate attribution rules and rely on last-click approaches to evaluate impact. MMM can provide a future-proof measurement source that doesn’t rely on user-level tracking, allowing companies to optimize their marketing mix across different channels and timeframes.
What are some best practices for implementing Marketing Mix Modeling?
To implement MMM successfully, brands should start with clean data inputs and gather historical data for the appropriate dimensions. The modeling team can then use added techniques such as ad stocks to determine how long the impact of advertising lasts. When the final model is delivered, the analysis of marketing impact begins. Any investment change should be implemented gradually, as results from other measurement methods may differ from what clients have seen. Experimentation is recommended in these cases to validate MMM learnings. The best way to ensure long-term success is to create a feedback loop between different measurement tools and align the organization on how the results will be implemented.