Collecting more customer data can seem useful for future campaigns, but it also increases privacy risk and can weaken trust. A data minimization strategy starts with a specific purpose, then limits collection and analytical use to the data needed for that purpose. section 2.2.2 section 2.3.3
Table 2.1 Aligning data collection with marketing goals
| Goal |
Appropriate data to collect |
| Personalizing recommendations |
Purchase history, browsing activity, product preferences |
| Improving customer support |
Contact details, past support tickets |
| Targeted marketing campaigns |
Age range, location (city/state), product interests |
| Fraud prevention |
IP address, transaction patterns, device information |
The goal-to-data table is a practical decision aid: recommendation, customer-support, campaign, and fraud-prevention goals each call for different information. section 2.2.2
Personalized recommendations
Purchase history, browsing activity, and product preferences may help personalize recommendations. Marital status or income does not serve that stated goal and adds unnecessary risk. The question is not whether a field is available, but whether the purpose requires it. section 2.2.2
Apply the same test as data moves through sourcing, storage, analysis, and activation. Anonymization, pseudonymization, aggregation, and privacy-enhancing technologies can add protection, but they do not replace the decision to use only essential data. Pair minimized collection with clear consent and user control. These examples address collection and analytical use; they do not prescribe retention periods. section 2.3 section 2.3.3 section 2.2.2
