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In briefShow moreShow lessData modeling is about structuring and organizing customer data so that it makes sense and can be used effectively in Customer Insights.
- Data modeling is about structuring and organizing customer data so that it makes sense and can be used effectively in Customer Insights.
- A good data model combines data from multiple sources, such as a CRM, online store, and email platform, into a unified customer model.
- Proper modeling enables advanced queries, such as identifying customers who have abandoned their shopping carts, opened emails, and are loyalty program members.
- Data modeling makes it possible to set up customer journeys triggered by specific events, such as a change in loyalty status.
- Good data modeling provides better insights, more precise segments, and more effective marketing activities through automation and personalization.
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But what does data modeling actually mean in this context, and how is it used in practice?
What is data modeling?
Data modeling is about structuring and organizing data so that it is meaningful and can be used effectively. In Customer Insights, this means defining how different types of customer data (purchase history, online activity, customer service inquiries, demographics, etc.) are related.
A good data model makes it possible to:
From rows and columns of data to insights
Let's say you have several data sources: CRM data containing names, email addresses, and loyalty status; online store data containing purchase history and shopping cart status; and an email platform containing open and click data.
By modeling this data in Customer Insights, you can create a unified customer model that connects all the data points.
This makes it possible to ask questions such as: “Show me all customers who have abandoned their shopping cart in the past 7 days, have opened at least one email, and are loyalty program members.” This query is only possible if the data is modeled correctly—with relationships between entities such as Customer, Cart, Email Interaction, and Loyalty Status.
Customer journey trigger based on modeled data
Data modeling also makes it possible to set up journeys that are triggered by specific events.
For example:
This requires loyalty status to be part of the data model and changes to this value to be tracked and used as a trigger.
Targeted customer communication
Data modeling is not just a technical exercise—it is the foundation for successful, targeted customer communication. It gives you better insight into your customers, more precise and relevant segments, opportunities for automation and personalization—and greater impact from your marketing activities.
So the next time you build a journey, ask yourself: Is our data structured enough?
"Find all customers who have abandoned their shopping cart in the past 7 days, opened at least one email, and are loyalty program members."
