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With the impending end of cookie support, marketers stand before a major challenge: How to measure and optimize marketing activities when the old techniques such as user-based attribution (based on cookie-level data) are no longer reliable?
Getting insight into the true return on marketing investment has become more and more difficult due to cookie limitations which cause significant data gaps, resulting in inaccurate calculation of channel ROI. This leads to bad business decisions. So how to get answers to the questions such as:
Getting insight into the true return on marketing investment has become more and more difficult due to cookie limitations which cause significant data gaps, resulting in inaccurate calculation of channel ROI. This leads to bad business decisions. So how to get answers to the questions such as:
The answer lies in using measurement techniques that do not require user-level (cookie based) data – one prime example is modern automated marketing mix modelling (MMM). MMM is completely independent of cookies and is a time tested approach that has been used by Fortune 500 companies to measure marketing effectiveness – but technological advances in the past few years are making it widely available for all advertisers. MMM is a method recommended nowadays e.g. by Meta/Facebook, Google or TikTok to measure both short-term and long-term effects of advertising.
Statistical modelling technique to find the relationship between channel marketing investment (and media exposure – impressions, GRPs,…) and its effect on the target metric (typically revenue).
Key Benefits of MMM in a nutshell:
Week 1
Together with the client we reviewed their “channel grouping” – how they structure their marketing channels (both online and offline) resulting in a total of 27 channels, with for example Facebook being broken down into 5 different subchannels based on the targeting and creative approach (e.g. “Product Ads – Retargeting” or “Main promotions – reach” etc.).
Week 1
We connected to all the advertising platforms for data – requiring no IT capacities on the customer side for the integration.
Week 2
We got to the step of marketing mix model preparation and calibration. We use state-of-the-art machine learning and statistical modelling methods to prepare a model with high accuracy and predictive power.
Week 2
Visualization in the form of customized dashboards is available in a web application interface or alternatively in all major BI tools such as Tableau, Looker / Data Studio, Power BI.
Week 3
MMM solution not only offers reporting of past performance but also recommends the optimal marketing budget mix. In this case showing an uplift of 14% revenue for the same marketing budget – just with different allocation among channels.
Week 3
Handover workshop with the customer – first batch of recommendations to optimize their marketing budget from our analytics team. New data is automatically fetched from the platforms and the model and results are updated on a daily basis. The marketing team now has always-on dashboards with accurate and reliable results of their campaigns – from branding and prospecting video to lower-funnel retargeting or branded search.
Modern MMM solutions not only report actual channel or campaign ROI but also recommend optimal budget allocation – you can set constraints for each channel and let the MMM engine optimize your overall ROI respecting these constraints – thus improving the overall effectiveness.
Modern MMM solutions also work as an “always-on” software application (or live BI dashboards) where the marketer can view daily or weekly updated results of their campaigns.
Normally it is recommended to have least 2 years of historical data, however businesses that are not affected by seasonality can do with shorter history.
Multi-touch attribution (MTA) for each conversion (order, app install,…) tries to reconstruct the touchpoints (these may be ad clicks, direct visits, display ad views etc.) that preceded it for the given user and then using some set of rules it distributes the credit for that conversion to some or all of the touchpoints.
Marketing Mix Modelling is a method based on statistical modelling with the aim of finding the correlation between marketing cost (and/or media exposure) and target metrics such as revenue, number of new customers or number of app installs on a “macro level”. In this context the macro level means “top-down” approach working with aggregated data – such as sales and investment breakdown by days or weeks – at the level of marketing channels and campaign types (both online and offline).
Both of the methods have the same goal measure the business impact of marketing channels and help marketers invest their budget more effectively.
Would you like to find out more about the differences? Feel free to take a look at our article about MMM vs MTA.
There is no technically required minimum but once you invest at least 10ths USD per month into marketing and use several marketing channels, MMM is likely to be useful to your company.
Marketing Mix Modelling includes other factors than media – such as pricing, macroeconomic factors, weather etc – in the model, while Media Mix Modelling only focuses on media. But in practice many people use these terms interchangeably.
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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.