All Results of Your Marketing Efforts in One Place
We provide an end-to-end solution for reporting needs of marketing executives and specialists – covering the whole process from data integration and cleaning to customized dashboards covering:
Channel Results
Channel and campaign performance incl.
Data-Driven Attribution, Media Mix Modelling
Product Performance
Slice data by product brands, categories or individual SKUs
Customer Analytics
Customer lifetime value models, cohort analysis, segmentation
Business Overview - Marketing Funnel, KPI Time Series
Cost & Profit Funnel - Scalable level of costs
Mi Data-Driven Attribution Analysis - Bar Chart Visualization
Mi. Product Name Performance - Breakdown by Marketing Channels
Mi. Cohort Analysis - Revenue of Customers by acquisition month
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Cover all your data and reporting needs in marketing
Example Use Cases
Model Scenarios
Easily model scenarios “what would happen if I made change X to my marketing plan”
Customer Lifetime Value
Understand future lifetime value of your customers
Predict Customer Behavior
Predict customer churn, propensity to buy and act on these information
See P&L on SKU Level
Understand full profit & loss on SKU, brand or product line granularity
Identify High Value Audiences
Identify top-value customers and see what is important to them
Measure Incremental Impact
Understand the incremental business impact of TV, Youtube, TikTok, Snapchat or influencer marketing
Boost Promotion of Profitable Products
See what the most profitable products, categories, brands are and boost their promotion in Facebook and Google
Improve Overall ROI
Find optimal allocation of marketing budget across channels and improve your total marketing ROI
Identify Budget Risks
Quickly identify areas that are at rick of not fulfilling the budgeted numbers
Optimize Online Marketing
Optimize online marketing using advanced data signals about products and customers - activate to Facebook, Google, your emailing tool or other advertising platforms
Over 200 Marketing And Other Data Sources
Data Connectors Examples
..and more
How much does it cost
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Let us Help You Create Reports Tailored to Your Business Needs Get in Touch with Our Mi Team Today.
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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.