Will Coupons Still Work When AI Does the Shopping?

The offer from another brand

When I shop for Nike running shoes, I often come across discounts from competing brands. Maybe Adidas offers 20% off a comparable pair.

Now I have a decision to make. Do I buy the Nike shoes I originally wanted, or switch brands to save money? It's a familiar battle between the rational appeal of a better price and the emotional attachment I have to a brand. Coupons work because they make us reconsider decisions we thought we'd already made.

But what happens when an AI agent does the shopping for me? You might expect the agent to be more rational. It doesn't have the same emotional attachment to Nike. It can compare products, evaluate prices, and identify the best deal in seconds.

Surprisingly, research suggests AI agents aren't necessarily immune to the same marketing nudges that influence human shoppers.

AI agents aren't necessarily rational shoppers

Researchers at MIT's ABxLab and Microsoft's Magentic Marketplace have studied how AI agents make decisions in simulated shopping environments.

Their research challenges the assumption that AI agents always make economically optimal decisions. Agents can exhibit systematic biases, and their choices can be influenced by how information, prices, and offers are presented.

One possible explanation is that AI models learn from human-generated content, including reviews, promotions, recommendations, and shopping advice. But the underlying causes are still being studied.

The implication is fascinating, particularly for coupons. If a coupon can influence an AI agent's decision, and that agent responds consistently to the same promotional signal, discounts could become even more effective in AI-driven shopping.

Google is already piloting Direct Offers, an advertising format that allows retailers to present promotions within AI-powered shopping experiences when consumers are close to making a purchase.

Coupons may not disappear in the age of AI. They may become more powerful.

But there's a catch.

The consumer still gets to set the rules

Consumers are already using AI to find better deals. A Snipp survey found that 71% of regular AI users had asked AI for a promotion or coupon, while 32% said they would switch from their usual brand for a better AI-found offer.

At the same time, consumers are becoming more specific about what they want. Google reports that shopping queries are becoming longer and more detailed as consumers describe exactly what they're looking for.

Instead of simply asking AI to find running shoes, I might say:

"Find me Nike Pegasus running shoes, size 11, in black, for the lowest available price."

Now the agent has a different job. It isn't deciding which brand I should buy. It's finding the best way to purchase the product I've already chosen.

A 20% discount from Adidas might be irrelevant because switching brands would violate my instructions. This creates an interesting tension. AI agents may be highly responsive to promotions when consumers leave their options open. But when consumers specify exactly what they want, those same agents may become much harder to influence.

The effectiveness of a coupon may depend less on the discount itself and more on whether it fits the consumer's instructions.

There's another important implication. As consumers give AI agents increasingly specific instructions, the information businesses provide must become equally specific.

An agent cannot determine whether a product or promotion meets a consumer's requirements if the relevant details are missing, outdated, or difficult to interpret.

A shopper might ask for a hotel room with a particular bed type, a flexible cancellation policy, and loyalty benefits that can be combined with a promotional rate. A general description of the hotel or a headline advertising 20% off won't provide enough information to answer that request.

In AI-driven shopping, the accuracy, granularity, and accessibility of product information may become just as important as the offer itself.

What happens to loyalty?

Loyalty programs face a similar challenge.

Today, loyalty is partly emotional. I may prefer an airline because I've flown with it for years, or a hotel brand because I trust the experience. But an AI agent doesn't automatically share those attachments. It needs to understand the value of my loyalty.

For example, if I'm a Marriott Platinum member, my agent should know that a Marriott hotel might offer me late checkout, a room upgrade, and other benefits that aren't reflected in the nightly rate.

A competing hotel offering a 15% discount may not be the better deal once those benefits are considered. The same applies to airlines, retailers, and other loyalty programs.

Google's Universal Commerce Protocol is laying the groundwork for loyalty account linking in agent-driven commerce, potentially allowing agents to recognize, earn, and redeem rewards.

The important change is that loyalty benefits must become information an agent can evaluate, rather than benefits that only a human customer understands.

Ironically, AI agents could make loyalty programs more powerful, not less, if they can recognize and account for the full value of an existing customer relationship.

In an AI-driven world, loyalty may increasingly depend on whether an agent can recognize its value.

The missing ingredient: consumer context

AI agents need more than product information, prices, and loyalty benefits. They also need to understand what individual consumers actually value.

This is where loyalty programs may have a significant advantage. They already capture purchase histories, preferences, and behavioral patterns that can help distinguish what customers say they want from what they choose.

My purchase history might reveal that I almost always buy Nike running shoes, even when competitors offer discounts. Or it might show that I'm willing to switch brands when the savings exceed 25%. Those are two very different shopping behaviors.

An agent with access to that history, with my permission, can make decisions that better reflect my preferences rather than relying on a generic assumption about what constitutes the best deal.

For brands, this creates an opportunity to make offers more relevant to individual customers. But it also introduces a challenge. As more shopping happens through AI agents, brands may lose visibility into the shopping journey and the signals they traditionally used to understand customer behavior.

That makes consent-based access to purchase history, loyalty information, and customer preferences increasingly important. Trust matters. Consumers need confidence that their agents are optimizing for their interests, not simply recommending whichever brand pays the most.

What should businesses do now?

As AI agents increasingly influence purchasing decisions, businesses need to rethink how they present products, design promotions, and communicate loyalty benefits.

Four priorities stand out:

1. Make granular product, promotional, and loyalty information accessible to AI agents.

Broad product descriptions and promotional headlines are no longer enough. Businesses need to make detailed product attributes, live availability, pricing, promotional eligibility, restrictions, and loyalty benefits accurate, accessible, and understandable to AI agents.

This means organizing information so agents can determine exactly which products and offers qualify for a consumer's specific requirements, including whether discounts can be combined with loyalty benefits.

If an agent cannot access or interpret these details, even a highly competitive product or promotional offer may never make it into the consumer's consideration set.

2. Rethink how promotions are designed.

Traditional coupons are designed to attract attention and influence consumer behavior. AI agents may respond differently, particularly when consumers give them explicit instructions.

Businesses should explore offers that align with the specific criteria agents are optimizing for, whether that's price, convenience, product features, flexibility, or loyalty value.

The winning promotion may not be the largest discount, but the one that best matches the consumer's requirements.

3. Turn loyalty into measurable value.

Emotional attachment alone may not be enough when AI agents compare competing offers.

Businesses need to make the economic and experiential value of loyalty understandable to AI. Free upgrades, flexible cancellation, priority service, exclusive access, and member pricing should be available as detailed information agents can evaluate.

Loyalty programs that clearly communicate their benefits may have an advantage over programs whose value is difficult for AI agents to recognize.

4. Build a better understanding of customer preferences.

Purchase history, loyalty activity, and customer preferences can help distinguish consumers who prioritize price from those who value specific brands, products, or benefits.

This information can help AI agents make more personalized decisions and businesses develop more relevant offers. Knowing customers this well is one of the superpowers of loyalty programs.

As shopping moves into AI interfaces, maintaining access to this consumer context may become increasingly important.

The bigger shift

Brands are no longer competing only for consumer attention. They're increasingly competing to be selected by AI agents acting on consumers' behalf.

Coupons and loyalty programs aren't going away. But their effectiveness will increasingly depend on whether businesses can provide the detailed information AI agents need to understand, compare, and recommend their offerings.

In a world where AI agents make purchasing decisions, having the best product, the best promotion, or the strongest loyalty program may not be enough. AI agents must be able to understand why it's the best choice.

Sources

• MIT ABxLab: abxlab.media.mit.edu
• Microsoft Magentic Marketplace: Magentic Marketplace: an open-source simulation environment for studying agentic markets
• Google Direct Offers and Universal Commerce Protocol: New tech and tools for retailers to succeed in an agentic shopping era
• Snipp survey: Chain Store Age