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With third-party cookies becoming so prevalent, it’s time to rethink how we measure media performance. His Nick Zanetis of creative software developer VidMob wrote:
Historically, there have been two main ways to measure the effectiveness of marketing on sales. Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA). Although these methodologies measure similar metrics, the underlying approaches and objectives are quite different. MTA is a detailed “bottom-up” approach that measures all exposures and assigns credit to each touchpoint on the conversion path. MMM, on the other hand, is a “top-down” approach, aimed at evaluating the right overall media mix to maximize revenue.
Multi-touch attribution relies on the ability to track individual exposures, interactions and sales. Much of that measurement is now done using third-party his cookies and app tracking. Cookie depreciation could therefore lead to a shift in investment from MTA to his MMM. This shift is a good time to re-evaluate both methodological approaches and think about a key piece of the long-ignored puzzle: creative.
Different, but equally creatively blind
MTAs seek to understand consumer paths to conversion by tracking all exposures of an individual across various channels. Tracking exposure across web pages and apps relies (at least historically) on cookies and cross-app tracking. Credit is then assigned to each of these touchpoints, the amount of which is determined by the attribution model used. Depending on the framework used, often depending on the order of exposure, some touchpoints may receive more credit and others less.
MMM primarily leverages impression streams across publishers and channels to determine impact on metrics such as return on ad spend. This is done using multiple linear regression to determine which variables are related to a particular indicator and to what extent. Unlike MTA, MMM does not rely on tracking audience exposure and does not try to understand the ideal consumer path, but instead helps identify appropriate media mixes.
Advertisers are expected to invest more in MMM measurements as a result of signal loss. MMM has the advantage that he does not rely on cookie data, but it also has its own challenges in that the model is not perfect. Historically, creative was the most important input accounting for 70% of performance, yet it was the variable that was not considered in the model.
Instead, MMMs tend to rely most heavily on media variables such as spending, channels, and targets, and may also consider external factors such as seasonality, macroeconomic conditions, and weather. The lack of creative variables can lead to false attribution of performance to other factors, misleading budget allocations and media mix decisions.
Fit Adherence Score Variable to Existing MMM
As Google follows Apple’s lead and intends to limit cross-app tracking on Android over the next two years, marketers could be under even more pressure to adopt different solutions to mitigate signal loss. There is a nature. Cookie depreciation reduces the effectiveness of hypertargeting, further highlighting the importance of measuring creative impact on performance. Accordingly, introducing creative variables into sales/ROI modeling frameworks may become more popular. The challenge is understanding the most important creative elements for your marketing model, from key messages to emotional levers to sounds and images.
Third-party cookies and app tracking make MTA difficult, but there are ways to improve MMM to include creative impact on performance using specified creative variables.
However, multilinear regression cannot include too many creative variables with too much granularity. Instead, we recommend scoring assets against specific adherence criteria (approximately 4-5, maximum) and introducing them into the model as variables. These variables should be clear, easy to measure, and based on insight into what works and what doesn’t. In most cases, these criteria are likely to align with platform best practices, but as more analysis is performed on advertiser creatives, these criteria may be aligned with brand-specific best practices. have the opportunity to adjust. Introducing creative variables via scoring is hoped to help provide a better read of effect without attributing performance to the wrong variable.
Consider unified marketing measurement (UMM) techniques
Another option is to create a hybrid model. UMM is a blend of top-down/bottom-up approaches of MMM and MTA respectively. UMM allows marketers to get a more granular view of a particular channel, sometimes down to the creative level, to determine the best path for consumers to convert.
UMM also provides visibility into high-level budget allocations across properties and channels. This is a more comprehensive approach to understanding marketing effectiveness. The granularity of the data also opens the door for conducting creative analytics to understand which creative attributes are potential drivers of results. These attributes can be specific key messages, themes, objectives, brand positioning, colors, etc., and the insights gained can help optimize your creative strategy.
As we enter the new year, it’s a good time for brands to consider rethinking their approach to measurement. Not only do many brands need to work with in-house analysts, agencies and technology partners to participate in new measurement approaches, they may actually have useful data and reports. , many tech companies can share log-level reports with brands that want to move to more detailed modeling. Most importantly, now is the time to be open-minded. With the change in strategy, the motto to follow is “test and learn”. Updating our approach to measurement is no exception.
Nick Zanetis is Director of Product Research and Innovation at VidMob, a creative software development company.
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