Privacy policy

At Marketing Intelligence s.r.o. we, just like everybody else, process the personal data of our customers and business partners. We are committed to protecting the privacy of visitors of our website, and our customers (hence also referred to as “Data Subjects”).

This Privacy Policy describes how we process personal data (information that can be used to directly or indirectly identify a Data Subject). It also lists Data Subjects’ choices regarding their rights such as access to their personal data or removal of such data.

1. Personal Data we collect

From visitors to our web pages we collect data based on their web browsing activity after their confirmation consent. All such collected data is only processed for the purpose of analysis of our web page’s effectiveness and possible improvements so that we are able to provide our visitors and customers with better service.

A wider range of data about the browsing activity of our visitors may be collected so that we can get detailed statistics and more information about our visitor’s preferences to improve our services and to target our marketing better. Such data, however, are collected only after we get our visitor’s cookie consent to do so, therefore, in this case, the legal basis for the processing of personal data is consent (point an of article 6 paragraph 1 of GDPR).

Our visitors may contact us or request newsletters using forms on our website. Data provided in these forms will be used only to reply to or send newsletters. This processing is based on the data subject’s consent (point an of article 6 paragraph 1 of GDPR).

We also collect and process data provided to us by the visitors directly in the process of them becoming our customers. This includes name, phone number, email, and company details. This set of data is crucial for us entering into a contract with the visitor, meaning it falls under point b of article 6, paragraph 1 of GDPR (“contract processing”).

None of the personal data mentioned above are sensitive (special categories of personal data).

The data is stored securely, i.e. behind firewalls and, where applicable, encrypted. We have taken multiple organizational and technical measures to make sure the data will be stored securely.

2. Disclosure of Personal Data

For the purposes stated in this Privacy Policy, Personal Data may be disclosed, when necessary, to authorities, other companies within the same group of companies as us, companies which the group cooperates with and to other third parties. An example of such disclosure is disclosing customer’s email to Facebook when customer is singing up into Marketing Intelligence s.r.o. (as we may need to connect accounts in Marketing Intelligence s.r.o. to customer’s Facebook account).

Personal Data may be transferred outside the European Union and the European Economic Area (“EU/EEA”), including but not limited to, the United States of America, China, Australia, Singapore and Argentina as well as other locations and jurisdictions in which we conduct our business. Such transfers outside the EU/EEA are performed subject to appropriate safeguards such as standard data protection clauses adopted or otherwise approved by the EU Commission in accordance with the GDPR.

3. Retention Period

We strive to retain Data Subjects’ data only for the period we need them. If a customer decides to stop working with us, we will only retain the customer’s personal data inside the application for 2 months. If the processing of personal data is based on consent, we may process the data until the consent is withdrawn.

4. Data Subjects’ Rights

Data Subject has a right to request from us:

  • access to and rectification or erasure of Data Subject’s Personal Data
  • for restriction of processing concerning the Data Subject or to object to processing
  • to receive, under certain preconditions, Data Subject’s Personal Data in a structured, commonly used, and machine-readable format and to transmit those data to another controller

Data Subject may exercise the aforementioned rights by contacting our support or client partners. We shall endeavor to comply with legitimate requests by Data Subjects as quickly as possible.

5. Contact information

Data controller: Marketing Intelligence s.r.o. (IČO 09546529)

All contacts and inquiries related to this Privacy Policy should be addressed to info@marketingintelligence.io.

© 2023 by Marketingintelligence.io

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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.