The Loyalty Program That Knew Too Much -7
When Personalization Starts Feeling Like Surveillance
Direct answer: A loyalty program should collect enough customer data to make participation useful, relevant and secure, but not so much that the customer would be shocked to see the complete profile. The right boundary is not defined by what technology can collect. It is defined by purpose, proportionality, transparency and genuine customer control.
Key Takeaways
- More customer data does not automatically create better loyalty.
- Personalization becomes uncomfortable when customers cannot understand why information was collected or how predictions are being made.
- India’s evolving data-protection framework makes clear purpose, limited collection and customer control increasingly important.
- Brands should measure trust and permission quality alongside redemptions, repeat purchases and engagement.
- The most useful loyalty data usually comes from verified actions, not from collecting everything available.
A 515-Page Surprise
In August 2026, WIRED senior writer Reece Rogers asked McDonald’s for a copy of the information connected to his loyalty account.
He expected order history, points and perhaps a record of the offers he had received. What arrived was a 515-page file.
According to Rogers’ first-person account in WIRED, the file contained detailed transaction records, locations, loyalty activity, offers and entries connected with McDonald’s Monopoly promotions. More strikingly, it included algorithmic predictions about his future behaviour.
The system estimated that he would visit 2.16 times over the following six weeks, spend an average of $13.49 per order and spend $29.15 in total. It reportedly assigned him a customer attrition likelihood of zero.
After seeing the extent of the information and prediction, Rogers requested that his data be deleted and wrote that he intended to stop eating there.
That is a remarkable loyalty outcome. A system designed to understand a customer more accurately ended up making the customer want to leave.
But this should not be read as a simplistic story about a failed loyalty program. McDonald’s operates one of the world’s largest loyalty ecosystems. The company reported nearly 210 million 90-day active loyalty users at the end of 2025 and almost $37 billion in annual systemwide sales to loyalty members across 70 markets. McDonald’s 2025 results provide the scale.
That is precisely why the WIRED story matters. It shows that even an effective and sophisticated loyalty system can approach an invisible boundary between relevance and intrusion.
The Uncomfortable Question Behind Personalization
Marketers have been taught to treat a complete customer profile as an unquestioned advantage.
Know what the customer buys.
Know when they buy it.
Know where they buy it.
Predict what they will buy next.
Estimate their risk of leaving.
Then choose the message, offer and reward most likely to influence the next action.
From the brand’s side, this sounds efficient.
From the customer’s side, the same process can sound very different:
How much do you know about me?
When did I agree to this?
What exactly are you predicting?
Who else receives the information?
Can I see, correct or delete it?
The difference between personalization and surveillance is often not the data itself.
It is the gap between what the company is doing and what the customer reasonably believes the company is doing.
What Is Useful Personalization?
Useful personalization employs data to deliver a clear benefit connected with the customer’s participation.
A shopper buys coffee every week and receives a relevant coffee reward.
A consumer scans an eligible pack and does not have to submit the same details again.
A dealer completes a training module and sees the next challenge appropriate to that product category.
A loyalty member chooses travel and entertainment as preferred rewards and sees more of those options.
The relationship is understandable.
The customer performs an action, the brand uses a reasonable amount of information and the customer receives recognizable value.
Personalization begins to feel like surveillance when the collection or inference becomes disproportionate to that visible benefit.
| Data Use | Possible Customer Benefit | Question the Brand Should Ask |
|---|---|---|
| Mobile number | Account access, OTP verification and reward delivery | Is every later communication covered by a clear choice? |
| Purchase and SKU history | Relevant rewards, replenishment reminders and easier service | How long is this history genuinely useful? |
| Location | Nearby availability or a location-specific benefit | Is precise or continuous location really necessary? |
| Engagement history | Fewer irrelevant messages and better timing | Can the customer change frequency and channel preferences? |
| Predicted traits or psychological characteristics | Potentially deeper personalization | Could this be explained comfortably and defended as proportionate? |
The RewardPort Useful Data Test
Before adding a new field, signal or prediction to a loyalty program, brands should apply five tests.
1. Visible Benefit
Can the customer identify what improves because this information is being used?
“ We use your preferred city to show nearby experiences” is understandable.
Collecting location without a visible customer benefit is much harder to justify as part of a loyalty relationship.
2. Appropriate Purpose
Is the information being used for the purpose the customer reasonably understood when providing it?
A mobile number supplied to deliver a cashback confirmation should not silently become permission for unlimited promotional messaging.
3. Limited Collection
Is this the smallest amount of data required to provide the benefit, verify the action or protect the program from abuse?
The discipline is to collect what is useful, not everything that is technically available.
4. User Control
Can the customer easily review preferences, change communication choices, withdraw permission or request correction and deletion where applicable?
Control should not be hidden behind multiple screens or depend on contacting an unknown department.
5. Explainable Inference
Could the brand explain the prediction to an ordinary customer without creating discomfort?
If a model labels someone as price-sensitive, at risk of leaving or likely to respond to a particular pressure tactic, the organisation should understand the inputs, intended use and possible harm.
Together, these five questions create a simple V.A.L.U.E. discipline:
V — Visible Benefit
A — Appropriate Purpose
L — Limited Collection
U — User Control
E — Explainable Inference
Why This Matters Especially in India
India’s Digital Personal Data Protection Act, 2023 and the notified Digital Personal Data Protection Rules, 2025 place greater emphasis on clear consent, specified purpose, data minimisation, security, accountability and individual rights.
The Rules use a phased commencement timetable. Brands should therefore verify which provisions apply at the time of implementation rather than treating every requirement as having commenced simultaneously.
The official text is available from the Ministry of Electronics and Information Technology.
This is not only a legal issue.
It changes the commercial design of loyalty.
A program built on vague consent and constant data accumulation may technically generate more information while gradually weakening trust.
A program that clearly explains the exchange can generate less data but stronger permission.
That stronger permission is often more valuable because the customer understands why the relationship exists.
How Should Brands Redesign Loyalty Data Collection?
1. Map Every Collected Field
Include registration details, transactions, location, device information, campaign responses, inferred attributes and information received from partners.
2. Connect Each Field to a Customer or Operational Purpose
If the team cannot identify a current purpose, question whether the information should continue to be collected or retained.
3. Separate Participation From Broad Marketing Permission
Entering a promotion, receiving a reward and joining ongoing communications are not automatically the same decision.
4. Rewrite Notices in Ordinary Language
A customer should understand what is collected, why it is required and what will happen next.
5. Create a Preference and Control Layer
Let customers choose channels, frequency, reward interests and whether they want personalised recommendations.
6. Set Retention Rules
Loyalty data should not remain indefinitely merely because storage is inexpensive.
7. Audit Predictive Models
Review what is inferred, how it is used, whether it creates unfair treatment and whether the prediction can be explained.
8. Test Customer Reaction
Ask a simple question:
Would a reasonable member be surprised if this appeared in their personal data file?
The Metrics Loyalty Teams Should Add
Redemption rate, active membership, repeat purchase rate and customer lifetime value remain useful.
They do not reveal whether the program is building healthy permission.
Brands should also track:
- Consent acceptance and withdrawal rates
- Communication opt-out rates by campaign and channel
- Preference-setting completion
- Complaints concerning irrelevant or intrusive messaging
- Percentage of collected fields connected to a documented purpose
- Time taken to respond to data-access, correction and deletion requests
- Customer trust or comfort scores
- Incremental performance from personalization compared with non-personalized journeys
The final metric is particularly important.
If extensive profiling produces only a marginal improvement, the additional data and trust risk may not be worth it.
A Practical Example
Consider a packaged-food brand running a repeat-purchase program.
The brand needs a mobile number to create the account and send an OTP.
It needs purchase proof to confirm eligibility.
It may ask the customer to select reward interests so that cinema, merchandise, cashback or experiences can be presented appropriately.
It probably does not need continuous location access, unrelated browsing history or an opaque psychological profile.
After the first verified purchase, the customer can choose whether to receive reminders, participate in another challenge or stop communication.
The brand learns from real, permissioned actions while the customer retains control.
This produces a smaller customer profile.
It may also produce a better customer relationship.
The Real Loyalty Advantage May Be Restraint
For years, the loyalty industry has treated the complete customer profile as the ultimate asset.
The next competitive advantage may be different:
Knowing which information not to collect, which prediction not to use and which message not to send.
A customer who receives one relevant reward with a clear explanation may trust the brand more than a customer who receives ten perfectly timed offers without understanding how the brand knows so much.
RewardPort designs consumer, channel and partner engagement programs around verified actions, appropriate rewards and measurable outcomes.
The objective is not to build the largest possible customer file.
It is to use the right information to make participation more useful, secure and rewarding.
Planning or reviewing a loyalty or consumer-promotion program?
RewardPort can help assess the journey, verification method, reward architecture, permission points, fraud controls and measurement framework before launch.
When AI Chooses What Consumers Buy, What Happens to Brand Loyalty?
For decades, brands competed for consumer attention.
They fought for:
- Search visibility
- Shelf presence
- Marketplace ranking
- Advertising recall
- Social media engagement
The consumer still made the final decision.
They compared options, read reviews, explored alternatives and selected a brand.
But commerce is entering a new phase.
Artificial intelligence is moving from helping consumers find products to helping them decide what to buy.
This shift changes the marketing challenge.
The question is no longer only:
“How do we get noticed?”
It becomes:
“How do we remain preferred when AI is helping someone choose?”
Brand loyalty does not become less important in this environment.
It becomes more valuable.
The brands that build strong direct relationships, understand consumer preferences and create meaningful reasons for customers to choose them will have a stronger advantage in AI-mediated commerce.
At RewardPort, we believe the next generation of loyalty will require what we call:
Loyalty Moat for Agentic Commerce
A framework designed to help brands build stronger consumer relationships before autonomous shopping becomes mainstream.
Key Takeaways
- AI is moving from search assistance towards decision assistance and transaction support.
- Brand visibility alone may not guarantee consideration when AI narrows choices for consumers.
- Loyalty programs must evolve beyond points accumulation towards stronger preference creation.
- First-party consumer relationships will become increasingly valuable.
- Promotions, rewards, warranties and post-purchase engagement can help brands build direct connections.
- Brands should start building a Loyalty Moat for Agentic Commerce.
AI Is Moving From Search to Decision
Traditional digital marketing focused on winning attention.
Brands competed to appear:
- Higher in search results
- More prominently on marketplaces
- More frequently in advertising
- More visibly on social platforms
The consumer then completed the evaluation process.
AI changes this journey.
Shopping assistants can increasingly help consumers:
- Discover products
- Compare options
- Understand features
- Evaluate alternatives
- Make purchase decisions
Research from NIQ highlights the growing role of AI in product discovery and purchase journeys.
Technology platforms are also building commerce infrastructure that allows AI systems to participate further in shopping journeys.
Google’s Universal Commerce Protocol is designed to support commerce interactions between AI agents and retailers, while Universal Cart enables consumers to manage products from multiple merchants within a connected shopping experience.
Source:
https://blog.google/products-and-platforms/products/shopping/google-shopping-cart/
Open Ai’ Agentic Commerce Protocol similarly focuses on enabling interactions between consumers, AI agents and merchants to support purchasing experiences.
Source:
https://openai.com/index/buy-it-in-chatgpt/
The infrastructure for AI-assisted commerce is already developing.
What Is Agentic Commerce?
Agentic commerce refers to a shopping model where AI performs part of the discovery, evaluation, recommendation or transaction process on behalf of consumers.
The level of AI involvement can vary.
Today, a consumer may ask:
“Which smartphone should I buy under ₹30,000?”
The AI compares options.
Tomorrow, the interaction could become:
“Choose the best option under ₹30,000 with good battery life and delivery before Saturday.”
The consumer defines the objective.
The AI helps execute the journey.
This creates a major shift for marketers.
The brand may no longer compete only for human attention.
It may also need to remain relevant within machine-assisted decision-making.
What Happens If Consumers Never See the Shelf?
Traditional buying behavior gives brands many opportunities to influence decisions.
A consumer walking through a store may notice:
- Packaging
- Promotions
- Brand familiarity
- Displays
- Recommendations
An online shopper may compare:
- Reviews
- Prices
- Features
- Offers
But an AI-assisted shopper may receive only a few recommendations.
Imagine a consumer asking:
“Recommend a protein snack for my child with low sugar under ₹50.”
Instead of comparing dozens of products, the consumer receives a shortlist.
The marketing challenge changes.
Brands must move from:
How do we appear?
to:
How do we remain preferred?
This affects categories including:
- Consumer electronics
- Appliances
- Beauty
- Packaged foods
- Travel
- Insurance
- Financial products
- Fashion
- Household products
Does AI Make Brands Less Important?
Not necessarily.
Trusted brands may become even more important.
AI systems require signals to understand what consumers value.
Those signals can include:
- Past purchases
- Loyalty membership
- Brand preference
- Reviews
- Product experience
- Consumer feedback
A consumer may tell an AI assistant:
“Choose my usual brand unless another option is significantly better.”
That preference becomes a powerful signal.
Google has already introduced identity-linked loyalty capabilities within its commerce ecosystem, allowing participating shopping experiences to recognise retailer-linked information such as loyalty benefits.
Source:
https://blog.google/products-and-platforms/products/shopping/ucp-updates/
The future of loyalty is not necessarily disappearing.
It is evolving into:
Portable consumer preference.
RewardPort’s Loyalty Moat for Agentic Commerce
Brands should begin thinking about loyalty as a connected system.
A strong loyalty moat consists of six layers:
1. Recognition
Does the brand know who the customer is?
Anonymous purchases create limited relationship value.
Recognition converts an unknown buyer into a known consumer.
Brands can build recognition through:
- QR registration
- Warranty activation
- Membership
- WhatsApp opt-in
- Purchase verification
- Cashback redemption
- Referral programs
- Contest participation
The objective is not only immediate sales.
It is creating a relationship foundation.
2. Permission
Can the brand continue the relationship?
Knowing a customer exists is different from having permission to engage.
Brands need clear value exchanges.
Consumers may provide permission in return for:
- Loyalty benefits
- Rewards
- Warranty support
- Personalized offers
- Product education
- Service updates
- Exclusive access
As AI-driven commerce grows, direct communication channels become increasingly valuable.
3. Preference
Why would a customer choose the brand again?
Many loyalty programs measure activity.
But activity does not always equal preference.
A customer collecting points may not necessarily prefer the brand.
True preference means:
“When I have a choice, I want this brand.”
Brands should understand signals such as:
- Repeat purchase
- Purchase frequency
- Reward choices
- Category preference
- Referrals
- Engagement behavior
- Response to offers
The goal is not only tracking transactions.
The goal is understanding preference.
4. Reward
What value changes behavior?
Rewards remain an important part of loyalty.
But the reward itself is not the strategy.
The strategy is understanding:
Which value is meaningful for which customer?
Different consumers may value different benefits.
Examples:
- Cashback for immediate value
- Entertainment benefits for engagement
- Travel experiences for aspiration
- Lifestyle rewards for premium audiences
- Practical vouchers for everyday needs
A reward catalogue supports loyalty.
It does not create loyalty by itself.
The behavior strategy comes first.
5. Direct Relationship
Can the brand maintain a connection without depending completely on intermediaries?
As AI shopping grows, brands may increasingly compete through platforms, marketplaces and AI agents.
The brands that maintain direct consumer relationships will have a stronger advantage.
That relationship can begin through:
Purchase → Verification → Benefit → Permission → Engagement
The transaction may happen through an intermediary.
The relationship can still belong to the brand.
6. Re-Engagement
How does the brand strengthen preference over time?
The future of loyalty is not sending discounts repeatedly.
It is creating relevant interactions.
Brands can:
- Recognize milestones
- Recommend relevant products
- Reward meaningful behavior
- Encourage referrals
- Reactivate inactive customers
- Personalize engagement
The objective:
Make consumer preference stronger over time.
Loyalty Benefits Will Need to Become AI-Readable
Today, loyalty programs are primarily designed for humans.
Consumers understand:
- Gold membership
- Reward points
- Cashback offers
- Exclusive benefits
- Free delivery
But as AI systems increasingly participate in shopping decisions, loyalty benefits may also need to become structured and understandable for machines.
Future AI shopping assistants may need to understand:
- Is the consumer a loyalty member?
- What benefits are available?
- Can rewards be applied?
- Does the customer have preferred status?
- Are there personalized offers available?
- Does buying directly create additional value?
Google’s commerce ecosystem already indicates movement towards identity-linked loyalty experiences where benefits can be recognized within shopping journeys.
Source:
https://blog.google/products-and-platforms/products/shopping/ucp-updates/
This creates a new opportunity.
Loyalty is no longer only about communicating benefits to customers.
It may also become about making those benefits visible to systems acting on behalf of customers.
Could AI Expose Weak Loyalty Programs?
AI-assisted commerce may create challenges for brands that rely only on discounts.
Consider a consumer instruction:
“Buy my usual coffee unless another equally rated option is 20% cheaper.”
An AI system can continuously compare:
- Price
- Reviews
- Availability
- Offers
- Alternatives
If price is the only reason a customer stays, AI may make switching easier.
This creates a risk for discount-led loyalty.
Strong loyalty requires deeper reasons to choose a brand.
These can include:
- Trust
- Familiarity
- Product experience
- Service quality
- Warranty
- Exclusive access
- Membership benefits
- Personal relevance
- Emotional connection
The future advantage will belong to brands that build preference, not only promotions.
AI Can Also Make Loyalty More Intelligent
The conversation around AI and loyalty should not only focus on disruption.
AI can also improve how brands understand and engage customers.
Future loyalty systems can potentially identify:
- What customers regularly purchase
- When they may need replenishment
- Which rewards they value
- Which incentives are unnecessary
- Which products are relevant next
- When a customer is becoming inactive
- Which intervention has worked previously
This can transform loyalty from:
Everyone receives the same offer
to:
Each customer receives the most relevant next action.
The objective is not simply increasing rewards.
It is improving relevance.
Why India Could Experience AI Commerce Differently
India’s consumer journey is already highly fragmented.
Customers move across:
- Physical stores
- Marketplaces
- Quick commerce
- Social platforms
- Messaging apps
- Brand websites
The future journey may not be:
Website → AI Assistant → Purchase
Instead, it may look like:
Creator → AI → Marketplace → Store → QR → WhatsApp → UPI → Loyalty → AI-assisted Repurchase
The winning brands may not be those with the highest number of channels.
They may be those that can recognize the same consumer across multiple interactions.
Meta and the Retailers Association of India highlighted the growing importance of omnichannel shopping behaviour in India, including online research before offline purchase and offline research before online purchase.
Google has also expanded AI-powered shopping experiences in India through Gemini and AI Mode.
What Should CMOs Do Now?
Brands do not need to wait for fully autonomous shopping.
They can begin preparing today.
1. Convert Anonymous Buyers Into Known Consumers
The first step is building recognition.
Brands should identify moments where consumers have a reason to connect directly.
Examples:
- Product registration
- Warranty activation
- QR engagement
- Cashback redemption
- Loyalty enrolment
- Customer support interaction
A known customer creates relationship possibilities.
2. Audit Loyalty Beyond Points
Brands should ask:
Does the program create genuine preference?
Or does it only distribute discounts?
A successful loyalty program should understand:
- Why customers return
- What benefits they value
- What behaviors indicate preference
3. Build First-Party Behavioral Signals
AI-powered engagement requires quality signals.
Brands should understand:
- Purchase frequency
- Reward preference
- Engagement behavior
- Product interest
- Repeat behavior
- Referral activity
Data should create better experiences, not just better reports.
4. Connect Promotions With Loyalty
A promotion should not end when the reward is delivered.
The customer journey can continue:
Purchase
↓
Verification
↓
Reward
↓
Permission
↓
Relationship
↓
Repeat Engagement
Consumer promotions can become entry points into deeper loyalty ecosystems.
5. Make Benefits More Portable
As commerce becomes more connected, customers will expect benefits to move with their identity.
Brands should think about:
- Membership recognition
- Reward availability
- Warranty access
- Consumer preferences
- Purchase history
The future of loyalty may depend on whether benefits can travel with the customer.
6. Measure Preference, Not Only Redemption
A high redemption rate tells brands:
“Customers liked receiving value.”
It does not necessarily mean:
“Customers became more loyal.”
Brands should measure:
- Repeat purchase
- Retention
- Preference signals
- Direct engagement
- Referral behavior
- Incremental behavior after incentives
How RewardPort Helps Brands Build Future-Ready Loyalty
RewardPort helps brands create consumer engagement ecosystems combining:
- Consumer promotions
- Loyalty programs
- Cashback campaigns
- QR-based engagement
- WhatsApp engagement journeys
- Digital reward fulfilment
- Personalized rewards
The objective is to help brands move from one-time transactions towards continuous relationships.
A typical journey can look like:
Consumer Purchase
↓
Verification & Identification
↓
Reward Experience
↓
Consumer Permission
↓
Personalized Engagement
↓
Repeat Purchase & Loyalty
As AI changes how consumers discover and buy products, owning the relationship becomes increasingly important.
Measuring Loyalty in an AI-Assisted Commerce World
Brands should move beyond traditional loyalty metrics.
Consumer Identity Metrics
- Known consumer rate
- Registration rate
- Opt-in percentage
- Profile completeness
Engagement Metrics
- Repeat purchase
- Purchase frequency
- Reward interaction
- Content participation
- Referral behavior
Reward Metrics
- Redemption rate
- Reward preference
- Cost per incremental action
- Reward effectiveness
Relationship Metrics
- Direct consumer engagement
- Retention after incentives
- Reactivation
- Category expansion
Operational Metrics
- Purchase verification success
- Fraud prevention
- Fulfilment performance
- Customer support experience
The objective is not simply measuring rewards.
It is measuring whether preference is becoming stronger.
The Future of Loyalty: From Points to Preference
For many years, loyalty was defined by:
Earn points → Collect points → Redeem points
That model still has value.
But the future will require more.
Brands will need to create systems where consumers:
- Are recognized
- Give permission
- Develop preference
- Receive relevant value
- Maintain direct relationships
- Continue engaging
This is the foundation of a stronger loyalty moat.
AI-assisted commerce will change how consumers discover, compare and purchase products.
But it will not eliminate brand loyalty.
It will redefine it.
The brands that succeed will not only be those that appear in AI recommendations.
They will be those that consumers already prefer.
Building that preference requires a stronger approach:
Loyalty Moat for Agentic Commerce
Recognition
Know the consumer.
Permission
Earn the right to continue the relationship.
Preference
Create reasons to choose the brand.
Reward
Deliver meaningful value.
Direct Relationship
Maintain connection beyond transactions.
Re-engagement
Strengthen loyalty over time.
The future of loyalty is not about having more points.
It is about building relationships strong enough to survive when AI starts making choices alongside consumers.