Use Case: Loyalty on Your Terms
Rebuilding loyalty and rewards around trust, transparency and customer-controlled data
Overview
Loyalty schemes were originally designed to reward customers for repeat purchases and build stronger relationships between brands and consumers. But over time, many programmes have become increasingly opaque, fragmented and frustrating.
Customers are often asked to trade significant amounts of personal data for rewards that feel inconsistent, difficult to understand or of declining value. At the same time, brands struggle with rising acquisition costs, low engagement, poor data quality, fraud, duplicated accounts, limited interoperability and growing privacy expectations.
DataPal introduces a fundamentally different approach to loyalty and rewards — one built around trusted, permissioned, customer-controlled data relationships.
Using DataPal’s fiduciary infrastructure, MyTerms machine-readable agreements and MyKey sovereign identity, organisations can create loyalty ecosystems that are transparent, portable, interoperable and genuinely valuable for both parties.
The result is a next-generation loyalty model where customers feel rewarded rather than exploited, and organisations gain access to higher quality, permissioned first-party data (1PD) with stronger long-term engagement.
Context
Retail loyalty schemes have become one of the primary mechanisms through which organisations collect first-party customer data.
Supermarkets, airlines, retailers, travel companies, media organisations and hospitality brands increasingly rely on loyalty ecosystems to:
Encourage repeat purchasing
Increase basket size
Drive app adoption
Personalise offers
Build retail media capabilities
Capture customer insight
Compete in increasingly margin-sensitive markets
However, consumer trust in loyalty schemes is weakening.
Many programmes now suffer from:
Reward inflation and declining perceived value
Complex rules and inconsistent incentives
Poor portability between services
Limited customer visibility into how data is used
App fatigue and account fragmentation
Concerns over surveillance and profiling
Accessibility challenges for non-smartphone users
Duplicate or fraudulent identities
Low customer understanding of the actual value exchange
A recent Lidl example (May 2026) demonstrates this tension perfectly. While businesses seek more controllable and margin-efficient rewards systems, customers increasingly perceive loyalty changes as a reduction in value and fairness.
This creates a strategic opportunity for organisations willing to redesign loyalty around trust, transparency and mutual value exchange.
Challenge
Traditional loyalty systems are organisation-centric.
The company owns:
The identity
The loyalty account
The reward rules
The transaction history
The customer profile
The data permissions
The communication channels
This creates several major problems:
For Consumers
Limited control over their own transaction and loyalty data
Poor transparency around data usage
Rewards that can be changed unilaterally
Fragmented loyalty experiences across multiple brands
Repeated sign-ups and onboarding friction
Difficulty tracking true value received
Lack of portability between schemes
Fear of excessive profiling and surveillance
For Organisations
Poor quality customer data
Duplicate and fake accounts
Expensive customer acquisition and retention costs
Declining trust in loyalty programmes
Compliance complexity under GDPR and emerging Smart Data frameworks
Difficulty creating meaningful personalisation
High operational costs for managing fragmented loyalty infrastructure
Increasing pressure to prove real customer value
As AI-driven commerce accelerates, these challenges become even more significant. Organisations need trusted, permissioned, high-quality customer data relationships — not simply more data.
Solution
DataPal enables a new model for loyalty and rewards built around customer-controlled data relationships.
Instead of brands merely extracting customer data in exchange for limited rewards, DataPal creates a transparent and contractual value exchange between individuals and organisations.
Using:
MyKey for sovereign identity
MyTerms for machine-readable permissioned contracted agreements
Smart Data Receipts for portable transaction history
DataPal AI agents for customer-side optimisation and automation
…organisations can create loyalty ecosystems that are more trusted, more interoperable and significantly more valuable.
In this model:
Individuals retain visibility and control over their loyalty and transaction data
Organisations request purpose-specific access to data via MyTerms agreements
Smart Data Receipts provide portable, standardised transaction records
Loyalty rewards become more transparent and explainable
Customers can aggregate and manage rewards across multiple brands
AI agents help individuals optimise offers, rewards and spending decisions
Organisations receive cleaner, consented and higher-quality customer insight
This shifts loyalty from being a surveillance mechanism into a mutually beneficial relationship infrastructure.
How It Works (Flow)
1. Customer Creates Their Trusted Identity
The individual creates a secure DataPal account linked to their MyKey identifier.
This gives them:
A portable digital identity
A secure personal data vault
Control over permissions and sharing relationships
A unified loyalty and rewards dashboard
2. Organisation Requests a Loyalty Relationship
A retailer, supermarket or service provider requests permission to establish a loyalty relationship using a machine-readable MyTerms agreement.
The agreement clearly defines:
What data is requested
Why it is needed
How long it will be retained
What rewards and value will be provided in return
Whether data can be used for advertising or profiling
What data can be shared onward
3. Customer Accepts Terms Transparently
The customer reviews and accepts the terms using their DataPal dashboard.
Instead of ticking opaque consent boxes, the customer enters into a structured and auditable agreement.
4. Smart Data Receipts Are Issued
Each purchase generates a Smart Data Receipt.
These receipts:
Are stored in the customer’s DataPal vault
Are portable and machine-readable
Can contribute to loyalty qualification
Can be shared selectively with permission
Help eliminate disputes and missing points
Create higher quality transaction data
5. AI Optimises Loyalty and Rewards
Customer-side AI agents analyse:
Spending patterns
Loyalty opportunities
Reward value
Cross-brand optimisation
Better offers and switching opportunities
Personal budgeting and preferences
The AI works for the individual — not the retailer.
6. Organisations Deliver More Relevant Rewards
Because organisations now receive:
Cleaner permissioned data
Better identity resolution
More trustworthy customer relationships
Higher quality purchase signals
…they can create:
Better targeted offers
More meaningful rewards
Reduced fraud
Improved retention
Lower acquisition costs
7. Customer Maintains Ongoing Control
The customer can:
Revoke permissions at any time
Change sharing preferences
Export loyalty history
Compare reward value across brands
Use AI agents to negotiate better offers
This creates a much healthier long-term relationship between individuals and organisations.
Actors
The Individual (Data Owner)
The customer manages:
Their transaction data
Loyalty relationships
Permissions and preferences
Reward visibility
Cross-brand reward optimisation
The individual becomes an active participant in the value exchange rather than simply a passive data source.
The DataPal Platform
DataPal provides:
Secure data ingestion
Smart Data Receipt management
AI augmentation
Permission orchestration
Identity management via MyKey
Machine-readable agreements via MyTerms
Data cleaning and normalisation
Cross-service interoperability
The Organisation (Retailer)
The organisation:
Requests purpose-limited access to customer data
Provides transparent value in return
Delivers rewards and offers
Benefits from higher quality permissioned insight
Builds more trusted customer relationships
The AI Agent
AI agents provide:
Reward optimisation
Offer comparison
Spending analysis
Loyalty automation
Fraud detection
Personalised recommendations
Customer-side negotiation support
Importantly, the AI acts fiduciarily on behalf of the individual.
Benefits
For Consumers (Individuals)
So what are the benefits for individuals?
Greater transparency and control
More trustworthy loyalty relationships
Better and more relevant rewards
Portable loyalty history
Reduced app fatigue
Easier comparison of reward value
Improved privacy and reduced surveillance
Better budgeting and shopping optimisation
Unified loyalty management across brands
For the Ecosystem
More interoperable loyalty systems
Reduced waste in marketing spend
More ethical data usage
Better consumer outcomes
Increased transparency and fairness
Foundation for Smart Data ecosystems
For Organisations
Whereas the benefits for organisations are:
Higher quality first-party data
Improved customer trust and retention
Better identity resolution
Reduced fraud and duplicate accounts
More effective personalisation
Lower acquisition costs
Improved regulatory compliance
Stronger retail media opportunities
Increased loyalty programme engagement
Outcomes
Potential measurable outcomes include:
Increased customer trust and programme engagement
Higher active loyalty participation rates
Reduced churn from loyalty dissatisfaction
Improved reward redemption rates
Reduced fraud and duplicate identities
Higher quality first-party customer profiles
Improved offer relevance and conversion
Reduced consent and compliance friction
Increased portability and interoperability of loyalty data
Research consistently shows that consumers are more willing to share data when:
There is clear value exchange
Transparency exists
Control remains with the individual
Benefits are immediate and understandable
DataPal operationalises these principles directly into the loyalty infrastructure itself.
