What are best practices for anonymized analytics?

If your email program collects engagement data, opens, clicks, device type, location, you're probably collecting personal data under most privacy laws. Anonymized analytics let you learn from that data without holding onto personally identifiable information longer than you need to.

There are two approaches, and they're not the same thing:

True anonymization means removing or irreversibly altering identifying information so that the individual can no longer be identified, even with additional data. Once data is truly anonymized under GDPR, it's no longer personal data and isn't subject to GDPR restrictions. The challenge: it's surprisingly hard to achieve. IP addresses combined with other data can re-identify people. True anonymization usually requires aggregating data into group statistics rather than keeping individual records.

Pseudonymization replaces identifying information (name, email address) with an artificial identifier (user_12345). The data is less obviously personal, but re-identification is possible if you have the mapping table. This still counts as personal data under GDPR. You just get some credit for the protection measure.

For most email analytics use cases, aggregation is the most practical approach. Instead of tracking "subscriber A opened on March 15 at 9:07am in London," you track "12% of subscribers opened on Tuesday mornings, skewing urban UK." The same business insight, no individual record.

Practical steps: define what analytics you actually need before collecting data (data minimization is a GDPR principle), set automatic purge schedules on detailed tracking data, and check whether your ESP's analytics store data on your servers or theirs. Their DPA should cover what they do with it. For specific questions about your setup, our SOS line can help, or review the DPA clauses you have with your ESP.