Understanding what determines a company's success is more complex and challenging than ever. Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA) are reaching their limits to answer the challenging questions that marketing managers face.
As a result, more and more companies are focusing on a highly advanced marketing analytics approach that combines the strengths of MMM and MTA and addresses their weaknesses: Unified Marketing Measurement (UMM).
Marketing Mix Modeling (MMA) and Multi-Touch Attribution (MTA) reach their limits
Before the age of digitalization and the resulting multiple possibilities of user journey tracking, Marketing Mix Modeling (MMM) was the predominant method for evaluating marketing effectiveness. MMM is based on macro-level data (e.g. aggregated TV data or print ad spendings, but also information such as weather data or the unemployment rate). The method is still used today to measure the effectiveness of offline campaigns and to derive budget distributions on a strategic level.
Multi-Touch Attribution (MTA), on the other hand, is based on micro-level user data, which is measured by user tracking - naturally in compliance with GDPS. MTA helps advertisers to better understand the effectiveness of individual marketing touchpoints along the customer journey on an individual level and to optimize them accordingly.
Marketing mix models and multi-touch attribution basically have the same goal: to accurately measure marketing effectiveness by assigning a specific value to each element in the marketing mix. MMM and MTA still have their right to exist today. However, they do not completely reflect the current marketing reality, because they do not link the interdependencies between the online and offline worlds. This is problematic because cross-channel campaigns include both digital and offline touchpoints and the mutual influence cannot be denied. UMM takes up precisely this challenge as we reveal in this video:
What is Unified Marketing Measurement (UMM)?
Unified Marketing Measurement means bringing together two of the most important components of marketing analysis. This means Media or Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA). Incidentally, UMM is also often referred to as Unified Marketing Impact Analytics (UMIA). The goal of UMM is not to apply MMM and MTA simultaneously. Rather, the aim is to create a uniform model for evaluating marketing effectiveness. Better still it is about giving marketers a holistic view with consistent data to get even closer to the so-called marketing truth. In concrete terms, this means that with UMM, the aggregated data from the Marketing Mix Modeling must be merged with the user-level data from the Multi-Touch Attribution.
Forrester Research describes the UMM approach as "a combination of statistical techniques that assign business value to each element of the marketing mix on the strategic and tactical levels".
In addition, "the integration of online and offline data to measure short and long-term goals offers advertisers both allocation and planning capabilities in one tool".
Why is UMM important for marketing managers?
If a company wants to be successful in the long term, it must be able to comprehensively track how each individual campaign on each individual channel or medium has contributed to a conversion. In addition, insight at the user level is fundamental to providing users with the best experience. The importance of UMM for marketing managers is also underlined by the following: The tech giants Google, Facebook & Co are getting more and more intransparent and especially claim user level data for themselves. Furthermore, certain marketing activities cannot be measured on user level e.g. print or out-of-home campaigns. Therefore Marketing managers often have to rely on aggregated data.
What are the technical requirements for UMM?
Anyone who wants to use Unified Marketing Measurement should take a few things into account in advance. It is recommended to work with a provider that enables the integration of user level data and aggregated media performance data (e.g. TV GRPs).
It should also be mentioned that UMM is not suitable for every company. The effort required for such comprehensive data capture and integration on macro level does not always pay off. Also, companies do not always have the relevant historical data to perform UMM. Instead, machine learning-based multi-touch attribution, for example, may be the more appropriate solution to increase Return on Advertising Spend (ROAS).
To some degree UMM is of course also a hype topic. Many marketing analysts and data scientists have been working with it for a long time - but without using this specific term. Nevertheless, it should be emphasised that UMM is currently the latest and most advanced approach for evaluating performance marketing. It allows evidence-based budget allocation, measurement of cross-channel effects and detailed insights into the user journey.
Why Adtriba relies on UMM
In addition to customer lifetime value-based marketing attribution, Adtriba's clients are currently focused on creating synergies between the online and offline worlds in online and performance marketing. As a provider of marketing attribution with the help of machine learning, we naturally meet this requirement with our UMM solution.
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