Personalized Loyalty Programs: What to Personalize and How

A personalized loyalty program adjusts what members see, earn and receive, such as messages, offers, rewards, challenges and progress, based on what the program knows about each member or segment. It works when the data behind it is accurate, collected with consent and used for things members value.

Any loyalty program that tracks points balances already has enough data to personalize something, such as progress reminders. The harder questions are what to personalize first, how much data each tactic needs, where personalization starts to feel intrusive and how to tell whether it changed anything. This guide covers why to personalize, what a program can personalize, segment-level versus individual personalization, the data it needs and how to collect it, the privacy line, how to measure results and when personalization is not worth the effort.

Key Takeaways

Why personalize a loyalty program?

Personalization can make messages, offers and rewards more relevant to each member, and cut spending on offers that change nothing, but its value has to be tested rather than assumed.

A generic offer sent to every member pays for responses from members who would have bought anyway and is ignored by members it does not suit. Personalization aims offers and messages at members whose behavior they are likely to change, not simply at members likely to buy anyway. It also has costs: data work, more rules to manage and more versions to test. The aim is to personalize where the gain is likely to exceed that cost.

What can you personalize in a loyalty program?

Programs can personalize communications, offers, rewards, earning rules, challenges, the member dashboard and content, each needing different data and effort.

Should you personalize by segment or by individual?

Start with segments such as tier, lifecycle stage or main purchase category, then move toward individual personalization once the data, rules and testing are in place.

• Segments: a few well-defined groups, such as new members, lapsing members or members in a given tier, are easier to build, test and explain. The guide to loyalty personas covers building segments members recognize.
• Individuals: recommendations and offers chosen for each member need reliable data and rules that decide what each member sees. The guide to next best action covers choosing the next message or offer for each member.
• Mixing both: a program can set segment-level rules and personalize individual details, such as the member's name, balance and nearest reward, within them.
• B2B programs: the member is often a business account with several users, so segments may follow account type and size, personalization may need to work at both account and user level, and too few accounts can make holdout tests hard to read.

Case study (disclosed by Brandmovers). Signia, an audiology manufacturer, runs Aspire, a B2B loyalty program for hearing care professionals. Brandmovers implemented a segmentation model that groups its customers into segments including Buying Groups, SMBs, Family Offices and Independent Providers, allowing Signia to "Offer customized promotions, incentives, and rewards based on customer tier, purchase behavior, and engagement level." The case page reports "+15% unit growth in 12 months among Aspire members" and an "87.3% average engagement rate on a recurring basis." It does not describe a comparison group, so these are program results rather than a measured effect of personalization alone.

What data does personalization need, and how do you collect it?

Personalization draws on purchase history, program activity and preferences members share, so give members a reason to share and record their consent.

• Purchase history: what, when and where members buy.
• Program activity: earning, redemptions, logins and responses to messages.
• Stated preferences: interests and preferences members tell the program directly, through profiles, surveys or quizzes.
• Consent: record what members agreed to and honor their choices about communications and data use. California's consumer privacy law lets covered businesses offer financial incentives "for the collection of personal information" but requires notice and the member's "prior opt-in consent" (Cal. Civ. Code § 1798.125), so review points offered for profiles and surveys against it.
• Age: the FTC's COPPA Rule applies to "operators of general audience websites or online services with actual knowledge that they are collecting, using, or disclosing personal information from children under 13" (FTC), and California bars selling or sharing the data of consumers known to be under 16 without authorization (Cal. Civ. Code § 1798.120). Set an enrollment age and keep minors out of individual profiling.

BLOYL™, Brandmovers' enterprise loyalty platform, supports earning for non-purchase actions such as profile completion, surveys, reviews and content engagement, which gives members a reason to share preferences.

Case study (disclosed by Brandmovers). GT's Living Foods "lacked direct engagement with their consumers." Its "Culture Club" program rewards activity "such as consuming content, taking quizzes or surveys," uses "sales data captured from uploaded receipts" and encourages engagement "using personalized progress trackers and 'micro-motivations' modules that showcase featured member activities and CTAs for new activities or earning opportunities" (GT's Living Foods case study). Those activities are how purchase and preference data reach the program. The case page reports no figures.

Where is the line between personalized and intrusive?

Use data in ways members would expect from the program, explain why they see what they see, and be careful with inferences about sensitive traits or circumstances.

• Expected uses: suggesting rewards based on past redemptions is expected; referring to something a member never shared with the program, such as data bought from a third party, may not be.
• Explanations: "Because you bought X" or "You are 200 points from your next reward" can make personalization feel helpful rather than watchful, but do not name a sensitive purchase in an email or push notification that others might see.
• Inferences: California treats "Inferences drawn from any of the information identified in this subdivision to create a profile about a consumer reflecting the consumer's preferences, characteristics" and other traits as personal information, for businesses covered by its consumer privacy law (Cal. Civ. Code § 1798.140).
• Fairness: members compare notes. Offers that differ by tier, activity or stated preference are easy to explain; different prices for the same item based on inferred traits are harder to defend.
• Sensitive data: California's definition of sensitive personal information includes precise geolocation and "Personal information collected and analyzed concerning a consumer's health" (Cal. Civ. Code § 1798.140).

The guide to hyper-personalization without being creepy covers this line in more depth. This is general information, not legal advice.

How do you measure whether personalization works?

Compare personalized versions with generic versions and a no-offer holdout, and judge results by incremental behavior and margin rather than opens or clicks.

• Personalized vs generic: shows whether tailoring the message or offer adds anything over a standard version of equal value; if the personalized offer is richer, the test measures generosity, not tailoring.
• Against a holdout: shows whether either version changed behavior at all.
• Incremental margin: the extra margin from additional purchases minus the cost of offers and the work to run them.
• Longer-term behavior: retention and purchase frequency over the following months.

BLOYL supports A/B testing against a control group, and its rules engine sets earning and redemption rules by product SKU, purchase channel, customer segment, time window or behavioral action, so personalized versions can be tested inside the program. The guide to personalized offers for churn covers one common use.

When is personalization not worth the effort?

Personalization may not pay when members or data are few, when building and testing versions costs more than the gain, or when the basics still need work.

• Thin data: with little purchase or preference data, personalization may be guesswork.
• Unreliable data: shared household accounts, outdated preferences and purchases made without a member ID can make individual personalization wrong, so check how complete and current the data is first.
• Small programs: too few members to test versions reliably.
• High cost, low value: many versions of low-value offers add work without changing behavior.
• Basics first: a clear value proposition and easy redemption usually matter before tailored details.

A simple first step, such as progress reminders based on each member's balance, needs little data and is easy to test.

Conclusion

Personalization makes a loyalty program more relevant when it starts small, rests on data members agreed to share and is tested against generic versions and a holdout. Begin with low-data elements such as progress reminders, personalize by segment before moving to individuals, explain why members see what they see, use sensitive data only with consent and a clear member benefit and keep only the personalization that changes behavior at an acceptable cost.

Sources

• California Civil Code section 1798.140
• California Civil Code section 1798.125
• California Civil Code section 1798.120
• FTC, Complying with COPPA: Frequently Asked Questions
• Brandmovers, GT's Living Foods loyalty program case study
• Brandmovers, Signia B2B loyalty program case study