As a loyalty consultant, data enrichment is always one of the key priorities of a business looking to design or enhance an existing loyalty program.

A loyalty program is one of the few places a brand can ask a customer a direct question and reasonably expect an honest answer. That access is valuable, and it is also fragile. Members will hand over meaningful data when they understand the exchange and trust the brand to honour it, but will disengage the moment the exchange feels one-sided.

This is the starting condition for any data enrichment strategy. Brands do not need more data. They need the right data, collected at points in the member journey where the request makes sense, in exchange for something the member actually values.

A loyalty program is the natural home for data enrichment

Consumers have grown more aware that their data has value, and more cautious about giving it away. Qualtrics XM Institute’s 2025 global consumer study, which surveyed more than 23,000 people, found that consumers want personalised experiences but that trust in how brands handle data has not kept pace with that expectation, leaving a gap between what brands want to collect and what members are willing to share. Separate research from Contentstack found that only 11 per cent of consumers describe themselves as “very willing” to share personal data with a brand, even when a more relevant experience is guaranteed in return, and 64 per cent had experienced personalisation that felt invasive rather than helpful.

Loyalty programs sit on the more favourable side of this trade-off. Bond Brand Loyalty’s 2025 research found that 80 per cent of consumers are comfortable with their data being used if it results in preferred benefits in return, a considerably higher figure than data sharing outside a value exchange. This is the mechanic loyalty programs are built on: points, tiers, and rewards for value in return, including information.

This category of data has a name, zero-party data. This is data a member intentionally and proactively shares with a brand, such as preferences, intentions, and how they want to be recognised. It differs from first-party data (what a brand observes from behaviour) and third-party data (what a brand buys or infers from external sources) in one important way, the member decided to share it, which makes it both more trustworthy and more sensitive to how it is requested. Brands collecting zero-party data should only collect data with a defined use, and prioritise quality over quantity.

For program operators, data enrichment is a design question about where, when, and why the program asks for information, running through each stage of the member journey, rather than a data project bolted onto the loyalty program.

Start with the member journey, not the data fields

Best-practice brands treat data enrichment as a continuous cycle rather than a one-off registration form. Each stage of the member journey creates a different, natural opportunity to collect a different type of data.

Mapping the journey this way does two things. It spreads the data request across many small, low-friction moments instead of one long form, and it means each data point is collected close to the context that makes it meaningful, which improves both response rates and data quality.

Get the join process right

The join process is the first data decision a brand makes, and it is one of the easiest to get wrong. A long registration form filters out members before the relationship has begun. Research compiled by SAP Engagement Cloud found that reducing a registration form from eleven fields to four increased conversion by 120 per cent, a substantial difference for a single design change.

To enhance the data strategy, the fix is to separate mandatory data from optional data at the point of registration, rather than to stop collecting data altogether.

Every optional field should carry a short explanation of why it is being asked, tied to a benefit the member can picture, such as a more relevant offer or a faster checkout. This is the technique known as progressive profiling, asking for a small amount of information at each interaction rather than everything at once, and never asking a member for data the brand already holds.

Build a preference centre members actually use

Registration only captures a first impression. A preference centre gives members a reason to return and add more detail after they have experienced some value from the program, typically in exchange for bonus points or an equivalent reward.

A well-designed preference centre goes beyond demographic basics such as birthday and gender, useful as these are for triggering rewards. It captures the data that drives personalisation:

• Preferred products or categories, for example a member’s typical spending across product lines
• Preferred shopping moments, such as which occasions or times of year a member is most active
• Typical purpose, for example whether a member is usually shopping for themselves or buying gifts for others
• Communication preferences, both what a member wants to hear about and which channel they want to hear it on

Adidas’s adiClub program is a working example of this mechanic. Members earn 100 points for completing their profile, and a separate birthday field unlocks a birthday reward once a member reaches a qualifying membership level. The points are a small individual amount, but the profile completion prompt runs continuously through onboarding, not as a single form at sign-up, which is what makes it effective.

Profile completion should stay incentivised beyond the first prompt. A member who ignores the request in week one may respond to it in month three, once the program has demonstrated some value.

Ask forward-looking questions, not just backward-looking ones

Most loyalty data describes what a member has already done. Some of the most useful data describes what a member is about to do.

Prompting a member to share their next intended purchase, visit, or renewal date creates a specific, time-bound signal that a brand can act on immediately, rather than waiting to infer intent from behaviour after the fact. Brands can incentivise this in a few ways to enrich their data strategy:

• A time-limited bonus point offer unlocked when a member shares a future date
• A “plan ahead” prompt triggered when a member adds an item to a wishlist, asking when they expect to need it
• A countdown or reminder mechanic once a date is entered, which gives the brand a reason to send a relevant, timely offer rather than a generic one

This is a comparatively small ask for the member and a disproportionately useful signal for the brand, because it converts a guess about future behaviour into a stated fact.

Treat each transaction as a data event

Transaction data, meaning what a member buys, where, how often, and how much, remains the richest and most reliable signal a loyalty program collects, because it is inferred from real behaviour rather than a stated preference. The condition for capturing it well is straightforward, the member must be identified at the point of purchase, whether by scanning a card or app, signing in online, or providing verification to staff.

The most common failure point is the prompt, not the technology. Members need to be reminded to identify themselves at the moment of purchase, whether through staff training at physical tills or an on-screen prompt during checkout, or transactions risk going unattributed to the member’s profile. A brand can build a sophisticated preference centre and still hold a thin, inconsistent transaction history if identification at the point of sale is left to chance.

Capture the data members give you without a form

Many useful data points come from ordinary interactions with a brand rather than a direct question, and this data is often overlooked because it does not arrive through a structured form.

• Wishlists. A member adding items ahead of a purchase reveals intent and product interest, and creates a natural trigger for a follow-up offer.
• Customer service interactions. Staff who are encouraged to ask about preferences during a service interaction, and to log the answer against the member’s profile, turn a single conversation into a lasting data point.
• On-ground engagement. A QR code that members scan to access a campaign or piece of content indicates which physical locations and campaigns generate genuine engagement, not just footfall.
• Communication engagement. Open rates and click-through rates across email, SMS, app push, and other channels reveal a member’s preferred channel, which matters because preferences vary widely. EY’s 2025 research on loyalty programs found that 59 per cent of consumers prefer email as their communication channel, with 18 per cent preferring text messages and 13 per cent preferring app push notifications, meaning a single default channel across an entire membership base risks under-serving a large portion of it.
• Browsing behaviour. App and website activity shows which parts of the program, and which product categories, a member is actually interested in, independent of what they say they want.

None of this requires a new form. It requires the brand’s systems to route this behavioural data back to the member’s profile rather than treating it as disposable interaction logs.

Use surveys as a targeted data instrument

Surveys remain one of the more direct ways to collect data a brand cannot otherwise observe, particularly around sentiment, competitor behaviour, and reasons behind a purchase. Qantas Frequent Flyer’s Red Planet panel, launched in 2015, is a working example, members opt in and complete surveys in exchange for bonus points, and the results feed both Qantas’s own market research and broader consumer insight.

Two conditions make surveys work as a data enrichment tool rather than a source of member fatigue. First, the exchange needs to be explicit, members should understand that sharing feedback helps the brand personalise future communications and offers, not that it disappears into an unused database. Second, surveys should stay targeted and occasional, reserved for questions that cannot be answered through transaction or behavioural data alone, rather than becoming a routine touchpoint members learn to ignore.

Use light gamification to surface preference data

A short, low-stakes game can surface a preference a member would not volunteer through a standard form, because the format feels like play rather than data collection. Common mechanics include:

• This-or-that prompts, asking a member to choose between two product categories or occasions to earn a small number of points
• Budget or “spend the points” games, asking members to allocate a hypothetical points balance across options, which reveals what they value most
• True-or-false statements about shopping habits, which build a lightweight behavioural profile over a series of short interactions

These mechanics work best as a complement to a preference centre, not a replacement for one. A game reveals a preference in the moment. A preference centre stores it, and connects it to the member’s transaction history so it can inform future personalisation.

Add location and context data carefully

Location data, where members shop, how long they linger in a location, and which routes or visits repeat over time, can meaningfully sharpen personalisation when members opt in through a brand’s app. It reveals interest that a member may never state directly, such as dwell time near a particular section of a store or a repeated pattern of visits.

Location data also carries the highest sensitivity of any data type covered here, and the value exchange needs to be proportionate and clearly explained. Members who turn on location sharing expect a direct benefit in return, such as a nearby offer or a faster in-store experience, and a brand that collects location data without a visible use for it risks the exact trust erosion that XM Institute’s research warns against.

Where to start

Brands do not need to build all of these mechanics at once. A practical sequence for enhancing your data strategy looks like this:

1. Audit the join process. Separate mandatory from optional fields, and add a stated reason for each optional field.
2. Fix identification at the point of transaction. No enrichment strategy outperforms a transaction history that is only partially attributed to members.
3. Build or improve the preference centre, and keep the profile completion prompt running through onboarding rather than treating it as a one-time form.
4. Route existing behavioural signals, such as wishlists, customer service notes, and channel engagement, back to the member profile before investing in new collection mechanics.
5. Add forward-looking prompts, surveys, and gamification once the foundational data is flowing reliably, since these mechanics work best layered onto a program that already has clean identification and a working preference centre.

Every mechanic in this sequence depends on the same underlying discipline which is, only ask for data with a defined use, explain the exchange in plain terms, and act on what members share. That discipline, not the number of data points collected, is what determines whether a loyalty program becomes a genuine data asset or an unused database of stale fields.

Unlock the full potential of your brand with our expert loyalty consulting services

Whether you’re looking to enhance customer engagement, boost retention, develop your data enrichment strategy or create a world-class loyalty program, our team is here to help. Contact us today to start your journey towards building your own loyalty program strategy.