Modern Marketing Mix Modelling

Measure marketing in a cookieless world. Improve your marketing ROI.

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What is MMM?

Marketing Mix Modelling (MMM) is a tool to
1) Measure the real impact of marketing channels, campaigns and non-marketing activities on sales and other business
KPIs – it allows the advertiser to understand true channel ROI and impact of online + offline media, competitor activity, market trends or discounting…

2) Optimize marketing budget allocation across channels – fix marketing waste and invest into channels with real incremental impact & understand how each is saturated or can be scaled with more investment.

Resistant to Tracking Issues

MMM is a privacy-first solution

No need for user-level data = no consent and tracking issues

Solution ideal for post-cookie world

Fix Marketing Waste

Marketing budgets are under
pressure during economic downturn – fix any marketing waste quickly

Prove advertising ROI – of both upper and lower funnel activities

Continuous insights

Traditional MMM used to be a complex expensive exercise available only to the largest advertisers.

Modern approaches have made MMM more reliable, affordable and highly automated

Learn more about MMM solution

Additional services & Solutions

  • Experiment design + evaluation
  • Media effectiveness testing
  • Attribution modelling
  • Marketing Measurement Maturity Model
  • Marketing Data + Measurement Strategy 
  • Data engineering

What they said about us

Testimonials

"We have been able to level up our marketing analytics working with Marketing Intelligence. In recent months, we have started using MMM to get a complete understanding of our marketing ROI across both online & offline channels and gain useful insights for marketing planning and management.”
Jaroslav SlavičínskýPerformance Marketing, SEZNAM.CZ
"We cooperate with MarketingIntelligence.io at Slevomat on projects in CRM and Performance Marketing. Mi solutions help us with CLV prediction of our customer base, attribution of sales and determination of ROI for both digital and offline channels using MMM. I appreciate their business contribution and professional expertise."
Branislav MajorskýCommercial Director, SLEVOMAT.cz
I divided my business activity before I started using the Marketing Intelligence.io platform and after in our company. ​ Thank God I forgot the time before and I can't imagine that I wouldn't be able to open reports from Marketing Intelligence.io every morning. ​ The combination of the Marketing Intelligence.io tool with rich experience and professional approach of top analysts in czech e-commerce is the key to the growth of our e-shops. ​ By working with Marketing Intelligence.io I began to feel for the first time that we drive business and not the other way around.
Bohumil StaněkMarketing Manager Retail, HARTMANN GROUP
Guys from Marketing Intelligence.io  are the absolute leader of czech e-commerce in terms of working with data analytics. We closely cooperate with them on projects linked to our large customer base, which is generally a tough challenge and they always succeed in solving a problem. We appreciate especially the practical applicability of data outputs and a highly professional approach.
David MatoušekMarketing Director, SLEVOMAT.CZ
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Coming Soon

Predictive analytics in GA4

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:

  • Purchase probability: The probability that a user who was active in the last 28 days will log a specific conversion event within the next 7 days.
  • Churn probability: The probability that a user who was active on your app or site within the last 7 days will not be active within the next 7 days.
  • Predicted revenue: The revenue expected from all purchase conversions within the next 28 days from a user who was active in the last 28 days.

You can create audiences based on these values and leverage them in your advertising campaigns – for example, you could exclude users who have a high purchase probability as you may assume that these users will buy anyways and you don’t have to spend additional marketing dollars on them (test it) or try to win back an audience with higher churn probability with a special campaign or communication.

Data export to BigQuery

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

  • Joining GA4 data with other sources in your company – CRM, customer data, backend sales, and margin data.
  • Using GA4 data for advanced customer analytics for a better understanding of customer lifetime value or churn prediction.
  • Automate reporting in your BI tool.
  • Move the data to your on-premise or cloud data lake and data warehouse.

Example use cases might be

  • Joining GA4 data with other sources in your company – CRM, customer data, backend sales, and margin data.
  • Using GA4 data for advanced customer analytics for a better understanding of customer lifetime value or churn prediction.
  • Automate reporting in your BI tool.
  • Move the data to your on-premise or cloud data lake and data warehouse.

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!

Free data driven attribution in GA4

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.

World without Cookies and GA4

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.