Marketing Measurement & Optimization

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Understanding the impact of Marketing

Our solutions leverage a holistic view of channels, customers, product portfolio and other factors to measure and optimize the effectiveness of marketing activities. 

 

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All our solutions are developed for a privacy-first & post-cookie world.

Actionable Insights

Insights to support your brand, growth and efficiency targets. Analytics expertise combined with practical marketing experience.

Robust data

We always make sure the data going into the modelling and analytics are reliable, clean and help our clients setup appropriate processes to track and capture them.

Our Solutions

Continuous Marketing Mix Modelling

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Media Effectiveness Measurement

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Product Portfolio Optimization

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Customer Lifetime Value Optimization

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Testimonials

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
We cooperate with Marketing Intelligence.io on a comprehensive project for Zbozi.cz (Czech Comparison Shopping Engine), where we analyze user paths and marketing contributions of individual channels according to various attribution models. Thanks to the outputs of post-Impression DDA, we optimize tactics and arrange effective balance of weights for brand and performance communication, especially in display advertising, but also in all other online channels.  In addition to excellent knowledge of attribution issues, guys from Mi stand out in their ability to incorporate data outputs into the overall business operations, for example by connecting to the Customer Lifetime Value (CLV). The result is not only tables and charts but also real changes in the marketing mix. 
Jaroslav SlavičínskýPerformance Marketing, SEZNAM.CZ
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Marketing Intelligence s.r.o.

IČO: 09546529     

DIČ: CZ09546529

 

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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.