81% of Indian Marketers Have AI. Do They Know the Customer?

AI personalization fails when customer information is fragmented, stale, unverified or disconnected from the behavior a brand wants to influence. The solution is not simply another AI tool or a larger customer profile. Brands need a closed loop that captures real actions, verifies them, responds appropriately and learns whether the intervention changed what happened next.

Key Takeaways

• AI adoption is becoming common, making access to AI less of a competitive advantage.
• A unified customer profile can still be commercially weak if it lacks timely, verified behavioral signals.
• Brands need to distinguish profile data, transaction data and action data.
• Effective marketing AI starts with a commercial question, not simply a content-generation prompt.
• Promotions and loyalty programs can become learning systems when every intervention produces a measurable next action.

The Statistic That Matters Less Than It Appears

In July 2026, Salesforce published the India findings from its tenth State of Marketing report. The headline number was difficult to miss: 81% of marketers in India had adopted AI.

That sounds like transformation.

Then the rest of the report makes the picture far more interesting.

Salesforce found that 92% of Indian marketers believe customers increasingly expect two-way conversations with brands. Yet 71% said they struggle to respond promptly because they cannot access the context they need.

Only 60% reported complete access to customer-service data, 61% to sales data and 58% to commerce data. Almost every respondent reported some barrier to personalisation, with privacy concerns, poor data quality and limited technical expertise among the leading problems.

The report was based on a double-anonymous survey of 4,450 marketing decision-makers, including 250 respondents from India.

A report published by a company that sells marketing technology should, naturally, be read with normal commercial caution. But the contradiction it reveals is useful:

Marketers have acquired the intelligence layer before fixing the information layer.

AI Is Becoming the Electricity of Marketing

A few years ago, having access to generative AI could itself feel like an advantage.

Today, almost every marketing team can use similar models to write copy, generate images, summaries research, produce campaign variations or answer basic customer questions.

When the underlying models are widely available, the model is no longer the moat.

The difference comes from context.

Does the system know whether this person is a first-time buyer or a regular customer?

Did the consumer actually buy the product or merely click an advertisement?

Was the invoice genuine?

Did the retailer complete the display challenge?

Did the dealer finish the training module?

Was the reward delivered?

Did the customer buy again?

Without those answers, AI can produce polished communication without producing much intelligence.

It may know how to sound personal without knowing what is personally relevant.

The Customer-Data Problem Is Not Only Fragmentation

The usual prescription is to create a single customer view.

Connect the CRM, website, commerce platform, call center, app and campaign systems. Resolve identities. Remove duplicates. Feed the resulting customer profile into AI.

This is important work.

But it can create the impression that once all the data sits together, the marketing problem is solved.

It is not.

A beautifully unified database can still be filled with old, passive or ambiguous signals.

A customer opened three emails.

Someone visited a product page.

A household used a shared mobile number.

A dealer was billed for stock but may not have sold it.

A consumer uploaded a document, but the purchase was never verified.

The data may be connected without being commercially conclusive.

That is why brands need to distinguish three kinds of customer information.

Profile data provides context. Transaction data records commerce. Action data shows whether an intervention changed behavior.

Marketing AI needs all three, but action data is often the missing layer.

What Exactly Is Action Data?

Action data is information generated when a consumer, dealer, retailer, employee or partner completes a defined and measurable behavior.

Examples include:

• Scanning a unique code from an eligible product
• Submitting a valid invoice
• Trying a new product variant
• Completing a training module
• Photographing an approved retail display
• Referring a verified customer
• Returning for a second or third purchase
• Redeeming a reward linked to a specific milestone

The value comes from the connection between the action and the commercial objective.

If a brand wants trial, it should capture verified trial.

If it wants repeat purchase, it should measure purchase two and purchase three.

If it wants better retail visibility, it should verify displays rather than treating distributor billing as proof.

If it wants a more capable dealer network, it should connect learning, execution and sales outcomes.

This sounds obvious.

Yet many marketing systems still measure the message more carefully than the behavior.

The Content Trap

The Salesforce findings show that personalized content creation is the most common AI use case for Indian marketers. 83% said they need more personalized content than they can currently produce.

AI is particularly good at solving that visible production problem.

One campaign can quickly become fifty audience variants, ten subject lines and hundreds of product combinations.

But greater content volume can magnify weak decisions.

If the underlying segment is wrong, AI produces more irrelevant messages.

If purchase data is incomplete, it recommends products the customer already owns.

If the brand cannot distinguish a genuine buyer from a reward hunter, it personalizes the wrong incentive.

If no one measures the next action, the system learns from clicks rather than commercial outcomes.

The danger is not that AI will make marketing less personal.

The danger is that it will make bad personalization faster, cheaper and more convincing.

The RewardPort Action Data Loop

Brands can approach AI-powered engagement through six connected stages.

1. Ask a Commercial Question

Begin with the decision the brand needs to improve.

Which first-time buyers are most likely to make a second purchase?

Which retailers understand the new product?

Which dealer activities increase secondary sales?

Which reward creates the highest incremental response for this audience?

“How can we use AI?” is not a commercial question. It is a technology question looking for a problem.

2. Define the Target Action

Specify the behavior that would constitute progress:

• Verified purchase
• Repeat purchase
• Referral
• Training completion
• Display execution
• Product registration
• Challenge completion

3. Capture the Signal

Use the appropriate route, such as:

• QR
• Unique code
• OTP
• Receipt upload
• Invoice parsing
• WhatsApp interaction
• Image submission
• Approved sales data

4. Verify the Action

Separate genuine behavior from duplication, invalid evidence, scheme gaming and accidental activity.

AI trained on unverified actions can become very confident about the wrong customer.

5. Respond With Appropriate Value

The response may be:

• Cashback
• Progress
• Recognition
• Merchandise
• Vouchers
• Cinema
• Travel
• Experiences
• Training access
• A next-best challenge

The reward should fit the audience and the behavior, not simply come from a generic catalogue.

6. Learn From What Happened Next

Did the person repeat the action?

Did the dealer improve?

Did the retailer sell through?

Did participation increase without fraud rising?

Did the incremental value justify the reward and operating cost?

The learning returns to the next intervention.

Ask → Define → Capture → Verify → Respond → Learn

This is what turns a promotion or loyalty program into an intelligence system.

A Practical Indian Brand Scenario

Consider a beverage company launching a lower-sugar variant.

A conventional campaign may advertise the product, offer cashback and count redemptions.

An AI tool may personalize the creative by age, location or media behavior.

An action-data program begins with a sharper question:

Which trial customers can be encouraged to buy the new variant again within the natural replenishment window?

The first eligible purchase is verified through a unique pack code or approved invoice.

The consumer chooses a relevant reward and can opt into a follow-up journey.

The next message arrives at an appropriate time and offers progress toward a second milestone rather than another blanket discount.

The system observes whether the second purchase occurs.

It learns which timing, message and reward combination works for comparable customers.

Fraud rules ensure that repeated scans or copied evidence do not become false training signals.

The brand is no longer asking AI to guess who may be interested based only on a profile.

It is helping AI learn from verified behavior.

Data Quality Also Means Permission and Restraint

Better data does not mean collecting every possible detail.

Salesforce’s connected-customer research reports that 71% of customers feel increasingly protective of their personal information, while 64% believe companies are reckless with customer data. The same research found that transparency becomes more important as AI advances.

This reinforces the lesson from our earlier article, The Loyalty Program That Knew Too Much: a larger customer profile is not automatically a better one.

The right data is useful, permissioned, current and connected to a defensible purpose.

The wrong data introduces privacy risk, irrelevant personalization and misleading model outputs.

AI needs context. Customers still deserve boundaries.

How Should a Brand Prepare Its Marketing Data for AI?

1. Choose Three Commercial Questions

Avoid starting with a company-wide AI transformation. Select questions with measurable outcomes.

2. Map the Current Evidence

Identify what the CRM, commerce, promotion, loyalty, support and channel systems actually know.

3. Separate Observation From Verification

A click is observed.

An eligible purchase may be verified.

They should not carry the same weight.

4. Identify Missing Actions

Determine which behaviors matter but are currently invisible.

5. Create Permissioned Capture Points

Explain why information is requested and what the participant receives in return.

6. Connect Response to Outcome

Record not only what message or reward was sent, but what happened next.

7. Establish a Non-AI Baseline

Compare AI recommendations with existing rules or control groups so that apparent improvement is not confused with real incrementality.

8. Feed Learning Back Into Program Design

AI should improve the next decision, not merely produce a dashboard explaining the last campaign.

The Metrics That Reveal Whether AI Knows the Customer

Brands should look beyond content output, open rates and model-generated engagement scores.

Track metrics such as:

• Percentage of target actions that can be captured
• Verification pass rate and suspected-fraud rate
• Time from verified action to relevant response
• Conversion from one target action to the next
• Incremental lift compared with a control or rules-based journey
• Reward cost per incremental behavior
• Percentage of recommendations using current rather than stale data
• Customer preference and permission retention
• Frequency of irrelevant or contradictory messages
• Operational time saved without a decline in customer outcomes

The core question is not whether AI created the message.

It is whether the system understood the situation well enough to improve the next decision.

The Winners Will Not Be the Brands With the Most AI

Eighty-one percent AI adoption is not evidence that marketing has become intelligent.

It is evidence that AI has become available.

The harder work begins after adoption: connecting the right information, verifying what actually happened, respecting permission, responding at the right moment and measuring whether behaviour changed.

That is where promotions, loyalty and channel programs can play a much larger role.

They are not merely mechanisms for distributing points or cashback.

Designed correctly, they create structured opportunities for people to act and for brands to learn.

RewardPort combines program strategy, action verification, rewards, engagement journeys and measurement across consumers, dealers, retailers, employees and partners.

The objective is not to add AI to every campaign.

It is to use AI where it can make the next decision more relevant, more accountable and more commercially useful.

Before buying another marketing AI tool, ask a simpler question:

What verified customer action will make this AI smarter?

To explore an Action Data Loop for a consumer, channel or loyalty program, speak with RewardPort.

The End of the Discount? Why FMCG Brands Need to Measure Incremental Sales, Not Promotional Redemptions

The End of the Discount? Why FMCG Brands Need to Measure Incremental Sales, Not Promotional Redemptions

For decades, consumer promotions have been one of the most widely used growth tools for FMCG brands.

A discount.

A cashback offer.

A free gift.

A contest.

A reward.

The success of these campaigns has often been measured through one simple question:

“How many consumers participated?”

But participation alone does not always represent business impact.

A campaign can achieve thousands of redemptions and still fail to create incremental growth.

The more important question for marketers today is:

Did the promotion create new behavior, or did it simply reward behavior that would have happened anyway?

As brands become more data-driven, FMCG marketers are shifting from measuring only redemptions and payouts towards understanding:

• Incremental sales
• Repeat purchase behavior
• Consumer acquisition
• Category expansion
• Long-term loyalty

At RewardPort, we believe the future of consumer promotions is not about offering bigger discounts.

It is about designing smarter engagement journeys that influence measurable consumer behavior.

Key Takeaways

• Redemption numbers alone do not define promotion success.
• Brands need to measure whether campaigns create incremental consumer behavior.
• Discounts can drive short-term transactions but may not always create loyalty.
• Purchase verification and data capture help brands understand promotion effectiveness.
• Reward strategy should align with the behavior a brand wants to influence.
• The strongest promotions create a bridge between acquisition, engagement and loyalty.

Why Redemption Numbers Can Be Misleading

A high redemption rate is often considered a successful campaign indicator.

However, redemption only answers one question:

Did consumers claim the reward?

It does not answer:

• Did the promotion bring new consumers?
• Did existing consumers buy more?
• Did consumers switch from competitors?
• Did the campaign increase repeat purchase?
• Would the purchase have happened without the incentive?

For example:

A consumer who already planned to buy a product and receives cashback has created a successful redemption.

But from a growth perspective, the brand needs to understand whether that cashback created additional value.

The difference between:

Rewarding an existing purchase

and

Creating additional purchase behavior

is where promotion effectiveness is determined.

Understanding Incremental Sales

Incremental sales refer to the additional sales generated because of a campaign or intervention.

The key question:

“What additional business did the promotion create?”

Incremental growth can come through different behaviors:

New Consumer Acquisition

A promotion encourages a new consumer to try the brand.

Example:

A first-time buyer purchases because of a cashback or reward offer.

Brand Switching

A consumer chooses the brand instead of a competitor.

Example:

A customer trying a new detergent brand because the promotion provides additional value.

Purchase Acceleration

A consumer purchases earlier than planned.

Example:

A customer buys during a festive promotion instead of waiting.

Basket Expansion

A consumer buys more products or chooses higher-value options.

Example:

A reward encourages a larger purchase quantity.

Repeat Purchase

A promotion creates a reason for the consumer to return.

Example:

A loyalty journey encourages continued engagement after the first purchase.

The Difference Between Redemption and Incrementality

A successful promotion should move beyond:

Purchase → Reward → End

Towards:

Purchase → Engagement → Relationship → Repeat Behavior

Redemption is an activity.

Incrementality is an outcome.

Both are important, but they answer different business questions.

Brands that measure only redemption may miss whether their investment actually created growth.

RewardPort Framework: The Five Jobs of Promotion

Every consumer promotion should have a clear purpose.

At RewardPort, we believe promotions typically perform five strategic jobs.

1. Recruit

Bringing New Consumers Into The Category

The first role of a promotion is acquisition.

Brands can use:

• Cashback offers
• Trial rewards
• QR-based promotions
• Assured rewards
• Sampling campaigns

The objective:

Convert non-users into first-time customers.

2. Switch

Changing Consumer Preference

Promotions can encourage consumers to move from competing brands.

Effective switching campaigns focus on:

• Clear value proposition
• Relevant rewards
• Simple participation
• Strong product experience

The reward becomes the reason to try.

The product becomes the reason to stay.

3. Accelerate

Influencing Purchase Timing

Some promotions do not create new demand.

They bring forward existing demand.

Examples:

• Festive campaigns
• Limited-period rewards
• Seasonal promotions

The objective:

Encourage consumers to purchase sooner.

4. Expand

Increasing Basket Value and Category Adoption

Promotions can encourage consumers to:

• Buy more quantity
• Try additional products
• Explore premium variants

Rewards can help create opportunities for category expansion.

5. Repeat

Creating Long-Term Consumer Behavior

The strongest promotions do not end after redemption.

They create the next interaction.

Examples:

• Loyalty programs
• Reward journeys
• Membership benefits
• Personalized offers

The objective:

Move from a transaction to a relationship.

Why Discounts Alone Are Losing Effectiveness

Discounts remain useful.

But discount-led engagement has limitations.

When consumers become accustomed to offers, brands may face:

• Reduced emotional connection
• Lower differentiation
• Higher promotional dependency
• Margin pressure

A discount answers:

“Why should I buy now?”

A loyalty experience answers:

“Why should I continue choosing this brand?”

Modern consumers increasingly value:

• Experiences
• Recognition
• Convenience
• Personalized benefits
• Instant value

This is why reward-led promotions are becoming more important.

The Role of Purchase Verification in Promotion Effectiveness

A strong promotion starts with accurate purchase validation.

Verification helps brands understand:

• Genuine participation
• Consumer behavior
• Purchase patterns
• Geographic insights
• Reward effectiveness

Methods can include:

• QR code scanning
• Unique code validation
• Receipt upload
• Digital purchase verification

RewardPort enables brands to create structured consumer journeys where purchase verification connects directly with reward fulfilment and engagement.

Reward Strategy: Moving Beyond Discounts

The right reward depends on the behavior a brand wants to influence.

Different audiences respond differently.

Examples:

For Immediate Action

• Cashback
• Digital vouchers
• Instant rewards

For Engagement

• Movie tickets
• Entertainment benefits
• Food rewards

For Premium Audiences

• Travel experiences
• Lifestyle rewards
• Exclusive access

For Long-Term Loyalty

• Points
• Tiers
• Membership benefits

A reward should not only create excitement.

It should support the business objective.

RewardPort’s Perspective and Solution Approach

RewardPort helps brands design consumer promotions that connect engagement, verification and rewards.

Our solutions include:

Consumer Promotion Campaigns

Helping brands execute:

• Cashback campaigns
• QR Scan-to-Win campaigns
• Gift-with-purchase programs
• Gamification campaigns

Purchase Verification Solutions

Supporting:

• QR verification
• Code-based validation
• Digital claim journeys
• Fraud management

Reward Fulfilment

Offering rewards across categories including:

• Digital vouchers
• Cashback
• Entertainment
• Travel experiences
• Lifestyle rewards

Loyalty Integration

Helping brands convert promotional interactions into longer-term engagement through loyalty programs and personalised consumer journeys.

Practical Recommendations for FMCG Marketers

1. Define the Behavior Before Designing the Promotion

Ask:

What should change after this campaign?

• Trial?
• Repeat?
• Higher basket?
• Brand switching?

2. Select Rewards Based on the Objective

Do not start with:

“What reward should we give?”

Start with:

“What behavior do we want to influence?”

3. Capture Consumer Intelligence

Use promotions as opportunities to understand:

• Who participated
• What they purchased
• Which rewards they prefer
• How they engage afterwards

4. Measure Beyond Redemption

Track:

• Repeat purchase
• Incremental sales
• Consumer retention
• Reward effectiveness
• Cost per incremental action

The future of FMCG promotions is moving beyond discounts and redemption numbers.

Brands need to understand whether campaigns create meaningful consumer behavior change.

The most effective promotions will not simply reward purchases.

They will:

• Recruit new consumers
• Influence switching
• Accelerate purchase decisions
• Expand category adoption
• Create repeat behavior

At RewardPort, we believe successful promotions are built around one important question:

Did the campaign create growth that would not have happened otherwise?

Because the true measure of a promotion is not how many rewards were claimed.

It is the behavior that continues after the reward.