How to Anonymize Customer Data

RetentionX is built to help you deeply understand customer behavior while maintaining full control over personal data. To calculate customer, product, and marketing insights, RetentionX processes customer-level data provided by your brand. This is expected and compliant, as RetentionX acts as a data processor, while you remain the data controller under regulations such as the GDPR.

At the same time, you can decide to anonymize customer data within RetentionX. This allows you to continue working with all analytical insights without exposing personally identifiable information inside the platform. To configure this, navigate to Company Settings → Report Settings → Data Privacy.

Within this section, you can manage two privacy-related settings:

• Customer data anonymization

• Gender detection.

Anonymization

When anonymization is enabled, the following personal identifiers are no longer displayed or accessible in the RetentionX interface:

• First and last name

• Email address

• Phone number

Customers are shown using masked values (**** ****) instead of personal identifiers. These placeholders cannot be used to identify a real person. Only the customer ID and address remain available as references.

The underlying data relationships remain intact, which means all calculations and reports continue to work as expected. Insights remain fully available even though customer identities are hidden.

In practice, this allows RetentionX to be safely used by broader teams who do not need access to personally identifiable information, while still preserving the full analytical depth of the platform.

Gender Detection

Gender can be a valuable demographic attribute for personalizing customer communication. However, many brands do not collect this information, especially when it is not requested by default during checkout or account creation.

RetentionX addresses this by predicting customer gender based on two parameters: the customer’s first name and country. The combination of these parameters is important to account for local and cultural differences. You can learn more here.

Gender predictions are enabled by default. If you choose to disable them, you can decide whether existing gender predictions should be kept while preventing new ones from being created, or whether all existing predictions should be deleted entirely.
 
 

Not using RetentionX yet?