Are you getting enough value from your loyalty data?

Loyalty programs underperform because brands fail to collect useful customer data

Thirty percent of brands rate their loyalty programs as only somewhat effective, at best. The deeper problem starts with data collection. A Forrester Research survey of 310 loyalty and marketing professionals, commissioned by Zeta Global, found that 27% of brands do not use their loyalty programs to collect first-party data. Another 38% do not collect zero-party data.

First-party data records what customers actually do. It can include purchases, product interactions, discount usage, and other observed behavior. Zero-party data captures information customers deliberately provide, such as product preferences, interests, or purchase intentions. Together, these data types can explain both customer behavior and stated preferences.

This matters because loyalty members are among the customers a brand has the strongest opportunity to understand. They have identified themselves, joined a program, and established an ongoing relationship. Failing to capture useful information during that relationship leaves a large part of the program’s potential unused.

The business objective should therefore determine what data gets collected. A retailer that wants to personalize product recommendations needs information that changes those recommendations. A travel company may need destination interests or expected travel dates. Collecting fields simply because the loyalty system supports them adds complexity without improving decisions.

Customer behavior also deserves greater weight as the relationship develops. A member may state a preference when joining and later behave differently. First-party behavioral data allows the brand to detect that change and adjust its decisions. This makes data collection an ongoing process rather than a one-time enrollment exercise.

For executives, the key measure is whether each additional data point can improve a customer decision. Better collection should support more relevant communication, stronger personalization, and more informed loyalty investments. A program that cannot do this will struggle to demonstrate value beyond discounts and rewards.

Poor data integration prevents brands from building a usable customer view

Twenty-three percent of brands have minimal or no integration between their loyalty data and other customer data sources, according to Forrester. This creates a basic operating constraint. Data captured inside a loyalty platform has limited value when other marketing and customer systems cannot use it.

A customer data platform, or CDP, can address this problem by creating a centralized customer record. Loyalty information can be combined with first- and zero-party data captured elsewhere, along with third-party information obtained through data enrichment. The combined record gives analytics and marketing systems more context for each customer.

The practical value comes from connecting events across channels. A customer’s purchases, loyalty status, stated interests, and interactions with other parts of the business can inform the same decision process. Marketing teams can then coordinate messages using a shared understanding of that customer.

Integration also has an organizational dimension. Connecting databases will deliver limited gains when separate teams maintain different customer definitions, access rules, or activation processes. Leaders need clear ownership of customer data, common identifiers, agreed data-quality standards, and explicit rules about which systems can use specific information.

Forrester’s finding that 23% of brands have minimal or no integration shows the scale of the technical gap. Yet integration itself is an enabling capability. The business return appears when the combined data changes decisions: which offer to send, what content to recommend, when to contact a member, and which channel to use.

The executive priority is therefore a usable flow of information from collection to decision and activation. Loyalty data should enter the broader customer-data environment quickly enough to affect current interactions. That creates the foundation for consistent personalization across channels and allows the loyalty program to operate as part of the wider customer strategy.

Operational and technology barriers prevent brands from activating loyalty data

Every respondent in Forrester Research’s survey reported at least one barrier to using loyalty data effectively. The leading problems were poor data quality, weak system integration, organizational silos, and limited internal expertise or resources. These issues constrain the final step that produces business value: turning customer data into action.

Data quality is a fundamental requirement. Customer records may contain outdated preferences, duplicate identities, incomplete profiles, or inconsistent fields across systems. Those problems weaken segmentation and personalization. AI-driven systems also depend on reliable inputs. Giving an advanced model poor customer data can produce irrelevant recommendations and reduce confidence in automated decisions.

Organizational design creates another constraint. Loyalty, e-commerce, CRM, analytics, and other marketing functions may control separate parts of the customer relationship. When teams use different systems, definitions, and decision processes, a customer signal captured by one group may never reach another group that could act on it. Executives can address this through shared data standards, clear ownership, and cross-functional processes for customer activation.

There is also a customer expectation to manage. Asking loyalty members for information creates an implied purpose: the brand intends to use that information to improve their experience. A company that repeatedly asks about preferences and then sends irrelevant communications gives members little reason to keep responding. Effective activation therefore supports future data collection as well as current personalization.

Investment plans show that brands recognize the problem. Forrester found that 92% expect to invest in new technology or processes within the next 12 months to improve their use of loyalty data. Priorities include customer-data centralization, advanced analytics, AI-driven personalization, and omnichannel activation.

The executive challenge is sequencing those investments. Data quality, integration, decision rules, and operating processes need to support the intended customer use case. AI can then increase the speed and scale of personalization. The relevant outcome is measurable improvement in decisions and customer experiences.

Extracting actionable insights is the central constraint on loyalty data

Only 37% of respondents were more than somewhat confident in their ability to extract actionable insights from loyalty data, according to Forrester. This points to a deeper problem than storing or moving information between systems. Brands need to determine what a customer signal means and define the business action it should trigger.

First-party data provides one route. Analytics can identify patterns in observed customer behavior, including affinities for individual products or categories and sensitivity to discounts. Those signals can inform product recommendations, offer selection, audience segmentation, and communication strategy.

The key requirement is to connect each insight to a decision. Knowing that a loyalty member frequently buys one product category has limited operational value until the company defines how that behavior should change subsequent interactions. The same principle applies to discount behavior. Identifying a customer’s response to promotions becomes useful when it changes offer selection or targeting.

Zero-party data adds customers’ stated preferences and intentions. This information can be especially straightforward to activate because the customer explicitly provides an answer. A preference for a product category can directly influence messaging. A stated seasonal purchase intention can influence communications during the relevant period. As customer behavior accumulates, first-party data can reveal whether those stated preferences still reflect actual interests.

Forrester’s findings show why insight generation deserves executive attention. Twenty-seven percent of brands do not collect first-party data through their loyalty programs, while 38% do not collect zero-party data. Limited collection reduces the evidence available to analytics teams, while weak analytical capability reduces the value of data that is already available.

Executives should define loyalty analytics around specific decisions and measurable outcomes. Teams can start with questions such as which customer behavior should trigger an offer, which preference should alter content, and what evidence indicates disengagement. That approach gives data collection, analytics, and activation a common purpose and creates a clearer path from customer information to business value.

Zero-party data needs different strategies for long-term preferences and short-term intent

Zero-party data is information customers deliberately share with a company. It can reveal interests and intentions that transactional history cannot yet show. Its value depends on how quickly the information changes and whether the business has a clear action tied to each response.

Long-term preferences can remain useful for several years. Examples include a favorite sports team, hobby, brand, or book genre. These signals are especially valuable early in a customer relationship, when behavioral history is limited. They can also help brands reconnect with customers after periods of inactivity.

Short-term preferences have a different operating cycle. Interest in a winter destination, an upcoming movie, a summer home-improvement project, or a particular type of holiday gift may remain relevant for only a few months. Brands can ask these intent-based questions repeatedly because customers’ plans naturally change. The resulting information can support timely campaigns, product recommendations, and content selection.

Executives should treat the expected lifetime of a preference as part of the data model. Durable preferences can have longer refresh cycles. Intent signals need expiration dates and more frequent updates. Otherwise, personalization systems risk making current decisions from old information.

Observed behavior also provides an important validation mechanism. A customer may declare an interest and later behave differently. First-party data from purchases and interactions can identify that change. Brands should continuously reconcile stated preferences with recent behavior so personalization reflects the strongest current evidence.

Question design is equally important. Teams should begin with the decision they want to improve and ask directly for the information required. A retailer deciding between menswear and womenswear content, for example, can ask customers which categories interest them. Asking for gender requires the company to infer purchasing preferences and may produce inaccurate targeting when a customer shops for a partner or has different clothing interests.

For brands beginning zero-party data collection, short-term preferences offer a practical starting point because answers can translate directly into near-term actions. Response rates may initially be low. Consistently using customer answers to improve subsequent experiences gives members a stronger reason to provide information again.

Integrated loyalty data increases the value of omnichannel marketing

Loyalty data becomes more useful when it informs decisions across the full customer relationship. Forrester Research’s study emphasizes combining loyalty information with other customer data and using the resulting insights coherently across channels. This gives marketing and customer systems a shared view of preferences, behavior, and loyalty status.

Customers already interact with brands across websites, apps, email, physical stores, service channels, and other touchpoints. An action in one channel can provide information relevant to the next interaction. A purchase can change future recommendations. A stated preference can alter email content. Recent engagement can influence when and where the next communication appears.

This requires loyalty systems to participate in the broader customer-data architecture. Loyalty status, purchase history, preferences, and engagement signals should be available to the systems responsible for analytics and activation. Information from those other systems should also improve decisions inside the loyalty program. This two-way flow supports a more current customer record.

Consistency is the business objective. Consumers have repeatedly expressed frustration when actions taken in one channel are ignored in communications delivered through another. They experience their relationship at the brand level, while many companies still manage customer interactions through separate internal teams and systems. Breaking those internal boundaries allows each interaction to reflect a broader history of the relationship.

This also changes how executives should govern loyalty programs. Loyalty should sit within the wider customer and marketing strategy, with shared data standards, identity management, consent controls, measurement, and activation processes. Treating it as an isolated program limits the number of decisions that loyalty insights can improve.

The investment priorities identified by Forrester support this direction. Ninety-two percent of surveyed brands expect to invest in technology or processes to improve loyalty-data use within the next 12 months. Customer-data centralization, advanced analytics, AI-driven personalization, and omnichannel activation are among the leading priorities.

The executive goal should be a connected decision system. Customer information needs to move from collection to analysis and then into the channels where a company can act on it. When loyalty data participates in that process, the program can contribute to personalization across the customer relationship and provide a clearer foundation for measuring business impact.

Key takeaways for leaders

• Collect data you can use: Forrester found that 27% of brands do not collect first-party loyalty data and 38% do not collect zero-party data. Leaders should tie every data request to a clear customer or business decision.
• Integrate loyalty data across systems: Twenty-three percent of brands have minimal or no integration between loyalty data and other customer sources. Connect loyalty platforms with centralized customer data to support a consistent view across channels.
• Fix activation barriers before scaling: Every surveyed brand reported at least one barrier to effective loyalty-data use, including data quality, integration, silos, and resource gaps. Prioritize these foundations before expanding advanced analytics and AI personalization.
• Turn insights into defined actions: Only 37% of respondents were more than somewhat confident in extracting actionable loyalty insights. Build analytics around specific decisions such as offer selection, product recommendations, discount targeting, and disengagement triggers.
• Manage preferences by lifespan: Separate durable zero-party preferences from short-term customer intent and refresh each at an appropriate cadence. Use observed first-party behavior to keep personalization aligned with changing customer interests.
• Make loyalty part of the customer data strategy: Loyalty signals gain value when combined with broader customer data and activated consistently across channels. Leaders should include loyalty systems in shared data, identity, consent, analytics, and omnichannel processes.