How to structure Custom Reports based on User IDs, IPs, and Groups?

Custom reports in Matomo lets you create tailored views of your data by building reports using dimensions, custom dimensions and metrics. You can organise and analyse data specific to individual users, custom groups, or geographic location by correctly structuring reports using User IDs, groups, and visitor IP addresses.

This guide explains how to structure the custom reports and what type of data you can expect to uncover.

Important: Always prioritise user privacy. If tracking sensitive information, such as IP addresses or user IDs, ensure your practices comply with privacy regulations.

Adding Dimensions and Metrics

Dimensions and custom dimensions are best used for categorical data such as User IDs or Groups to enable effective grouping and filtering.

Metrics are quantitative data points used to analyse performance, trends, or behaviours. Additionally, including metrics like the Visitor IP is useful for geographic analysis or diagnosing infrastructure-related issues, especially when paired with load times or error rates. To comply with privacy regulations, IP data should always be anonymised or aggregated.

By selecting the right combination of custom dimensions and metrics, you can reduce noise in your reports and focus on the data that matters most. Read more about [custom dimension scopes and structure](Understanding custom dimension scope and structure).

User ID

When enabled, the User ID feature assigns a unique identifier to logged-in users, enabling you to track their behaviour across devices and sessions. This includes details like pages visited, actions taken, and session frequency for a focused analysis on your users’ behaviour.

For deeper insights, you can pair the User ID dimension with custom dimensions that complement default reports and provide additional context, such as categorising traffic sources beyond the Channel Type report (e.g. by specific campaigns or referral partners).

Visit Scope

The User ID feature in Matomo allows you to track logged-in users across multiple sessions and devices, providing a comprehensive view of individual user behaviour. To enhance User ID tracking, you can configure complementary visit-level custom dimensions, such as membership type (e.g., Trial, Business) or preferred language.

From the extensive list of metrics, include those that are important to your objectives, for example, number of visits, average visit duration, and total days since last visit. Analysing user engagement levels and the frequency of interactions can help to improve retention and re-engagement efforts.

Action Scope

Pair an action-scoped custom dimension to capture specific details about user interactions, such as button clicks, file downloads, or video plays.

For example, with a Content Type custom dimension (e.g. blog post or product page), you can analyse which content specific users engage with the most by tracking button clicks or file downloads.

Groups

Breaking down data by groups provides detailed insights into how different audiences engage with your site. For example, if you offer different subscription plans like Trial, Business, and Platinum, then you could track these values in a Subscription custom dimension and compare the levels of user interaction, such as login frequency and feature usage.

Groups can also be based on technical factors to assess the technical performance of your platform. For example, having a group custom dimension for server regions with relevant metrics like average page load time or error rates can help evaluate how performance varies geographically.

Visit Scope

Adding a Group custom dimension in the Visit scope lets you group and compare data between user groups (e.g., subscription tiers).

You could pair this with the User ID and include metrics such as the number of visits, average visit duration per visit, and bounce rate to compare engagement levels across the different groups.

Action Scope

Adding a Group custom dimension in the Action scope allows you to break down data at the action-level, enabling detailed comparisons of how different user groups interact with specific features or content.

For example, you can include metrics for tracking link clicks (e.g., on the Contact link), form submissions (e.g., to complete the Sign Up process), or video plays (e.g., Watch Demo) by group. You can analyse group-specific engagement with calls-to-action, interactive elements, and content consumption.