How Loyalty Programs Collect First-Party Data, and How to Use It

First-party data is information a brand collects directly through its own relationship with customers, such as enrollment details, purchases, redemptions, responses to offers, app and site activity and service contacts, rather than buying it from or inferring it through other companies.

A loyalty program is one of the most practical ways to collect it, because members identify themselves each time they earn or redeem, which ties activity across channels to one person over time. This guide covers what a program collects, why that data is more useful than anonymous data, how to collect it without putting members off, how to keep it accurate, what privacy rules require, how to use it and how to tell whether it is paying off. For the data members state directly, such as preferences and intentions, see the guide to zero-party data and loyalty.

Key Takeaways

What first-party data does a loyalty program collect?

It collects seven kinds: enrollment and profile details; purchases linked to the member; receipt uploads; redemptions; offer and promotion responses; app, site and email activity; and service contacts.

Stated preferences, such as interests and purchase intentions, are often called zero-party data. The line between the two is a marketing convention, not a legal category: privacy law treats both as personal information when they can be linked to a person or household.

Why is loyalty data more useful than anonymous data?

Because members identify themselves to earn and redeem, purchases across stores, the website and the app can be linked to one person over time, through an account they opened.

Anonymous data shows what was sold; loyalty data shows who bought it, how often and what they did next. That makes it possible to see lapsing members, measure repeat purchase and test offers on real customers. It also depends less on tracking by other companies. Apple's App Tracking Transparency lets users "choose whether an app has permission to track their activity across other companies' apps and websites for the purposes of advertising or sharing with data brokers" (Apple). Chrome has not removed third-party cookies; in October 2025, Google confirmed that Chrome will keep its current approach of "offering users third-party cookie choice in Chrome" (Google). Collecting data members share in their own account with the brand does not rely on cross-site tracking. Using it to advertise on other companies' platforms can: Apple counts "Sharing a list of emails, advertising IDs, or other IDs with a third-party advertising network that uses that information to retarget those users in other developers' apps or to find similar users" as tracking (Apple).

Loyalty data has limits. It covers members only, and members who join are often already the more frequent customers, so it can overstate how all customers behave. It also misses purchases members make without identifying themselves.

How do you collect it without putting members off?

Keep enrollment short, make identifying at checkout quick and rewarding, ask for more over time in exchange for visible value and use receipts where retailers hold the sales data.

• Short enrollment. Ask for contact details and one or two fields the program uses straight away. Each extra field asks for effort before the member has received anything.
• Easy identification. Members should be able to identify at checkout with a phone number, app or card in seconds. Purchases made without identifying are lost to the data.
• Progressive profiling. Ask for more after the member has earned or redeemed, and tie each request to a benefit, such as birthday rewards for a birth date. Brandmovers' BLOYL™ platform supports earning for profile completion and surveys, so programs can reward members for sharing.
• Receipt uploads. Brands sold through retailers can ask members to upload receipts to earn, which shows what members bought on the receipts they choose to upload, even when the retailer keeps its own sales records. Members who upload may be the most reward-motivated, and receipts also capture other brands' items, so keep only the fields the program uses. The guides to receipt validation and first-party data for CPG brands cover the mechanics.
• Promotions. Sweepstakes and other promotions can bring in new accounts and opt-ins, as long as the official rules and privacy notice say how the data will be used. Entrants drawn by a prize may engage less than members who join for the program, so track whether promotion sign-ups go on to buy.

How do you keep the data accurate and connected?

Match each member's records across systems, remove duplicates, define fields the same way everywhere and refresh profile data, or decisions will rest on a misleading picture.

• Matching. The same person may appear under different emails, phone numbers or cards. Decide which identifiers link records, and how households are treated, before building reports.
• Duplicates. Duplicate accounts split one member's history and can also signal points abuse; the guide to loyalty program analytics covers using the data once it is clean.
• Consistent definitions. Agree on what counts as an active member, a purchase and a redemption, so that loyalty, CRM and finance reports match.
• Freshness. Addresses, life stages and interests change. Prompt members to confirm key fields from time to time, and age out data that has not been confirmed.
• Connected systems. Loyalty data is most useful when it reaches the CRM, email platform and service tools, and when changes such as opt-outs flow back.

What do privacy rules require?

Tell members what is collected and why, keep to those purposes, collect no more than needed and, in California, check whether rewards for data count as a financial incentive.

Under the California Consumer Privacy Act, a covered business must tell consumers "at or before the point of collection" what categories of personal information it collects and why, and its collection and use "shall be reasonably necessary and proportionate" to those purposes (Cal. Civ. Code 1798.100). The law "does not prohibit a business from offering loyalty, rewards, premium features, discounts, or club card programs consistent with this title." A covered business that offers financial incentives "shall notify consumers of the financial incentives" and needs "prior opt-in consent," which "may be revoked by the consumer at any time." A difference in price or service is allowed if it is "reasonably related to the value provided to the business by the consumer's data," and incentive practices must not be "unjust, unreasonable, coercive, or usurious in nature" (Cal. Civ. Code 1798.125). The state's regulations add that the notice must include "a good-faith estimate of the value of the consumer's data" and how it was calculated (CCPA Regulations, section 7016). A program that gives points or discounts in exchange for enrollment details can fall under this. Purchase histories can also reveal sensitive matters: California defines sensitive personal information to include "personal information collected and analyzed concerning a consumer's health" (Cal. Civ. Code 1798.140), so take care with categories such as health products. Other states have their own privacy laws, and they differ: Virginia's, for example, allows a different price or service where "the offer is related to a consumer's voluntary participation in a bona fide loyalty, rewards, premium features, discounts, or club card program" (Va. Code 59.1-578). If a program's site or app is directed to children under 13, or the brand knows it is collecting their data online, the federal Children's Online Privacy Protection Rule also applies (FTC). This is general information, not legal advice.

How do you put first-party data to use?

Use it to segment members by behavior, time offers to each purchase cycle, recognize members in service, suppress offers that do not fit and inform product and assortment decisions.

• Segmentation by behavior: recency, frequency, spend and categories bought, rather than demographics alone.
• Timing: offers and reminders based on each member's purchase cycle, such as when a regular buyer is overdue.
• Relevance and suppression: fewer offers for products a member never buys, and none for items they just bought.
• Service: agents who can see a member's tier and history can resolve problems with more context.
• Business decisions: which products members buy together, which promotions bring back lapsed members and which stores or regions underperform.

How do you measure whether the data is paying off?

Test data-based offers against generic offers sent to a random holdout, and track how much of the business the program can see, starting with member-linked sales.

• Coverage: the share of sales linked to a member ID, the share of members with a usable email or phone number and how many profiles have been confirmed recently.
• Quality: duplicate rate, match rate across systems and the share of records with missing key fields.
• Value: among members eligible for a data-based offer, send it to most and a generic offer to a random holdout, then compare response, repeat purchase and opt-outs. Weigh any lift against the cost of collecting and storing the data, including points given for sharing.

Comparing members with non-members is not enough on its own, because frequent customers are more likely to join. A holdout test measures the value of targeting only, so run it on enough members, for long enough, to separate a real difference from noise, and judge service and assortment uses separately.

As an illustrative example, a program sends a replenishment reminder to most members who are overdue for a repeat purchase and a generic newsletter to a random holdout. If the reminder group repurchases more within 30 days, after counting the points and messaging cost, the purchase-cycle data is paying off.

Conclusion

A loyalty program gives a brand a member-level view of purchases and engagement that anonymous data cannot. Collect it with a short enrollment and easy identification, add more over time in exchange for clear value, keep records matched and current, follow the notice and consent rules and use the data to make offers more relevant. Then test whether it actually improves results. Which share of your sales can your program link to a member today?

Sources

• Apple Developer, "User Privacy and Data Use"
• Google Privacy Sandbox, "Update on Plans for Privacy Sandbox Technologies," October 17, 2025
• California Civil Code section 1798.100 (notice and purpose limits)
• California Civil Code section 1798.125 (financial incentives)
• California Civil Code section 1798.140 (definitions)
• California Privacy Protection Agency, CCPA Regulations (effective January 1, 2026), section 7016
• Code of Virginia section 59.1-578
• Federal Trade Commission, Children's Online Privacy Protection Rule