Optimize your Marketing Mix and Improve your ROI and Cost Efficiency
Mi.MMM is an automated modern marketing mix modelling solution for marketing managers, CMOs and other executives with marketing and revenue responsibility to understand and measure the incremental value of marketing and to optimize the budget allocation in order to significantly improve their marketing ROI.
Get clear recommendations for a more effective budget allocation across online and offline channels
Despite having reports and analytics in every possible tool, marketing and revenue executives still don’t get actionable insights and answers to the key questions.
Find the best allocation of marketing budget among all channels to achieve best possible revenue.
Clear recommendations where you should increase / descrease spend and by how much.
Typical opportunity is 8-15% of cost savings.
Identify true incremental effect on revenue (or profit or app installs or new customer acquisition…).
Mi.MMM can be calibrated using marketing tests and experiments for ground truth.
Identify and measure long term effect of brand building or other non-performance marketing activities.
Model various scenarios “what would happen if I change investment in channel X by Y” using intuitive UI.
Try different constraints such as “Investment into TV can increase by 20% maximum over last year” and find best budget allocation given those constraints.
Incorporate expected changes in media prices.
Online media data, offline media data, external signals like weather etc – all can be ingested automatically.
Market and competitor level data.
Daily updated model results and insights available to you through UI and integration to leading BI tools (Tableau, PowerBI, Google Data Studio,…).
Results and signals from Mi.MMM can be integrated with your bidding platforms and tools.
No need for user-level data or cookies-based data.
Privacy-first solution.
Marketing Mix Modelling and Attribution Modelling have the same basic goal: measure the business impact of marketing channels and find out how to allocate marketing budget between channels in order to achieve the best possible results. The key difference between the two methods is that MMM analyzes effectiveness of marketing budget distribution from a top-down perspective, while attribution models are calculated at the level of a specific user (bottom-up approach).
© 2023 by Marketingintelligence.io
Get in touch with Marketingintelligence team
Get in touch with Marketingintelligence team
Get in touch with Marketingintelligence team
Get in touch with Marketingintelligence team
Do you want to get more information about MMM or how/whether it could help your ROI & measurement issues? Lets us know – we can either answer your additional questions, share more materials with you or have a quick call if you prefer.
One of the new and exciting features of GA4 is called “predictive metrics” – with these you can learn about your customers and their shopping behavior. These are added automatically to your data and are based on Google’s machine learning expertise.
GA4 currently includes the following predictive metrics:
Google BigQuery is a cost-effective and highly-scalable cloud data warehouse optimized for high performance on very large data sets. With GA4 you can export all your event-level data to BigQuery for additional analytics or data science initiatives.
Example use cases might be
Example use cases might be
You will need to have a Google Cloud account set up and maintained for this purpose. That is why we are here and can solve all the infrastructure for you and with you!
Since 1/2022 GA4 has made a data-driven attribution model available to all users – unlike in Universal Analytics where the DDA model was only available to GA360 customers.
DDA is an algorithmic attribution model that quantifies the value of each touchpoint in the user journey (such as campaign click) and it does so by smart modelling behind the scenes without human bias that is always present in rule-based models.
The DDA model in GA4 is also better than the one on GA360 as it takes into account up to 50 touchpoints in the user journey vs. the GA360 only took 4 touchpoints.
It is also possible to set the default attribution model to DDA (or another model) from the old Last Non-Direct Click. And then all your reports and data exported to BigQuery will use the newly selected model as default.
DDA in Google Analytics 4 excludes almost all direct visits from receiving credit – you may or may not like that, but as it’s been always the case with attribution we recommend validating any model using marketing experiments.
GA4 is built for a world where more and more users opt out of cookie consent and other methods for data collection.
Google uses machine learning modeling to fill in the data gaps – some of these are already in the current GA4 others will be deployed in the future.
For example, modelling conversions allows GA4 to properly attribute conversions without user identification – this is crucial for optimized advertising campaigns and automated bidding. This covers situations such as some browsers limiting the time window for first-party cookies, conversions for unconsented users, Apple’s App Tracking Transparency (ATT) impacts, cross-device user behaviour, and others.