For an MMM to be able to inform your overall marketing strategy, it will need your media spend data. You should be able to identify the highest-performing channels, seeing which deliver the best user acquisition rates. Monitoring daily ad spend is a must for mobile marketers. While a major selling point of this model is that it’s flexible in the data points you can plug into it, we recommend including the following in your mobile app marketing’s MMM. For mobile apps, however, not all of these aspects are as relevant (e.g., price of an app). Traditionally, a marketing mix model was built around the 4Ps: Promotion, Price, Place, and Product. You can see it combines all marketing efforts to show the generated ROI compared to the weekly cost. Not bad, right? What’s even more impressive, is that an MMM can answer more business-specific questions, such as, “If I change X factor, how will my revenue be affected?”īelow, we’ve included an example graph created by an MMM, displaying the revenue contribution based on a certain mix of individual marketing channels. What is the impact of external factors on the total revenue?.How to best optimize marketing touchpoints by campaign, audience, geography, timing, and publisher to maximize ROI?.What’s the role of earned, owned, and paid media?.How do channels and geography impact my marketing effectiveness?.What is the optimal mix of channels to reach a certain user segment?.Where should my ad spend go in the near future?.Does the way a campaign is executed on a channel impact its performance?.What’s the optimal spend level for each channel to maximize KPIs?.What’s the ROI of each of my marketing channels?.How many conversions did each media channel drive?.With MMM, marketers can answer questions like: 10 questions app marketers can answer with MMM Today’s digital media mix modeling can handle plenty of data input, but it’s up to the marketing team to determine which variables to use, which will in turn ultimately influence the model’s usefulness. The ratio above is the base of an MMM framework, flexible in its ability to include other components. The previous campaign results and insights. The amount of money spent on each channel.ģ. Thanks to advancements using data analytics and machine learning, MMM is having a renaissance, which we hint at in our article: Media mix modeling: The comeback kid of advertising analysis.Ģ. This is one key place where the privacy-friendly media mix modeling comes in.Īt its core, MMM is looking at how your marketing budget was spent and the result of your spend and using that to inform your future marketing endeavors. Similarly, Google is seeking to limit the sharing of user data on Android to third parties, reducing reliance on cross-app identifier data in an effort to strengthen user privacy in its Google Privacy Sandbox on Android. With the introduction of Apple’s App Tracking Transparency (ATT) framework in 2021, marketers can no longer access user-level data on iOS unless a user opts into tracking (see iOS 14.5+ Back to basics guide). In fact, in late 2022, Meta announced it had seen an 80% increase in MMM adoption compared to the previous year. Having made its initial splash in the 1950s, Media mix modeling (MMM) is not new, but its relevance has recently skyrocketed in light of the new era of user privacy in digital marketing. “Forecasting via media mix modeling will return” was one of Our top 5 mobile app marketing predictions for 2023.
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