Media Mix Modeling comes in when performance data has proven to be a valuable resource for decision-making, but obtaining insights has become more complex.
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Challenges arise from the restrictions of SKAdNetwork, including a 72-hour window and privacy thresholds, impacting a comprehensive understanding of user conversions (anticipated enhancements with SKAN 4.0 adoption across channels).
Marketers are compelled to navigate these limitations (even in 2024) by making informed estimations for key iOS KPIs, shaping the need for precise decisions in orchestrating effective Media Mix Modeling strategies.
Media Mix Modeling (MMM), also known as marketing mix modeling, is an analytical approach empowering marketers to evaluate the impact of their marketing endeavors, revealing how diverse elements contribute to their primary objective – often geared towards enhancing conversions.
The insights coming from MMM enable marketers to fine-tune their campaigns, considering many factors, from consumer trends to external influencers, in order to make an optimal campaign that drives both engagement and sales.
MMM leverages aggregate data, enabling the evaluation of a broad spectrum of channels implementing both traditional and digital platforms. Furthermore, MMM facilitates the use of external influencers, such as seasonality and promotions, in the analysis, providing marketers with a comprehensive understanding to refine and elevate their strategies effectively.
Regarding app marketing, Media Mix Modeling offers a comprehensive, top-down view of various business facets, aiming to grasp the contribution of each aspect to overall business outcomes.
On the other hand, Multi-Touch Attribution (MTA) takes a meticulous bottom-up approach within the app marketing landscape, striving to precisely measure every consumer interaction with media and attribute each exposure to specific outcomes.
A media mix marketing strategy provides enhanced flexibility and visibility compared to traditional marketing approaches. Integrating various marketing channels into a cohesive media mix enables your business to tap into new markets, enhance brand recognition, and boost the return on investment (ROI) of your marketing efforts.
Moreover, SKAdNetwork’s limitations may result in data gaps and incomplete insights, especially for specific KPIs (Cost Per Event, LTV, e.g.) or segments (retention, user quality). Media Mix Modeling can bridge these gaps by leveraging additional data sources and statistical modeling techniques.
MMM allows marketers to attribute post-install events or KPIs to marketing channels beyond what SKAdNetwork can track. By analyzing historical data and integrating consenting user analysis, estimations can provide insights into specific channels’ effectiveness, even without precise SKAdNetwork measurements.
By incorporating estimations for untracked or underreported channels, MMM can provide a more accurate assessment of the overall performance and help marketers evaluate the effectiveness of their media mix decisions.
Media Mix Modeling employs statistical analysis, specifically multi-linear regression, to ascertain the correlation between the dependent variable, such as sales or engagements, and the independent variables, such as advertising expenditure across various channels.
The media mix can be calibrated and validated using available SKAdNetwork data as a reference point. By comparing the estimations derived from MMM with the actual SKAdNetwork measurements, marketers can fine-tune and validate the model’s accuracy, ensuring that the estimations align closely with the observed data.
Estimates will vary greatly depending on each case, and calculations should be tailored to specific scenarios or in-app events. That’s where expertise meets data, and all variables should be considered. Estimations should factor in lengths of free trials, cohort analysis with different timeframes, audience segmentation, etc.
Media Mix Modeling (MMM) offers several advantages and disadvantages for marketers in paid app marketing. Understanding the pros and cons listed below can help marketers make informed decisions about implementing MMM in their strategies.
The four steps of an effective, standard MMM procedure are as follows:
Below is a general scenario that we use. Specific cases may require specific adjustments to the example below. The idea here is to incorporate the “CAMPAIGN A” consenting user conversion rate (CR%) into “CAMPAIGN A” SKAN numbers.
In cases where the privacy threshold is not passed:
In cases where the privacy threshold is passed, but we want a CPA for a longer cohort period (e.g., 14 days):
Note: In cases where there is insufficient consenting user data, we consider organic users as well.
Marketers should approach Media Mix Modeling (MMM) and estimations as iterative processes, constantly updating and refining their models as new data becomes available. This approach allows them to adapt to changing market dynamics and consumer behavior, ensuring their media strategies remain optimized over time.
With the inclusion of SKAN in a marketer’s reality, it has become essential to acknowledge its limitations and the need for data. In order to overcome these limitations, marketers should employ educated estimations to supplement the available data in 2024.
Need help measuring the true impact of a campaign? Our team of experts will help you measure, manage, and analyze marketing performance data to understand the effectiveness and improve ROI.
Originally published on July 7, 2023. Updated on October 23, 2023.