Stop discounting to everyone: The case for personalized promotions

Mass promotions hand the same deal to everyone and hope it lands. Personalized promotions start instead from the individual customer and the moment they are in. That difference is what separates promotions that change behavior from promotions that just give away margin.

In this blog post, we'll break down how personalized promotions work and how to deliver them, including:

Personalized promotions are offers tailored to an individual customer. They reflect that customer's history, preferences, real-time context, and intent. Mass promotions send the same offer to an entire database, such as the same 20%-off email to everyone. Targeted promotions send segment-based offers to groups such as women aged 25 to 34 who bought running shoes last quarter.

Personalization draws on four dimensions of customer understanding:

All four matter, but they behave differently. History and preferences can come from a batch database, while context and intent require live data flowing in real time. That difference is why personalization needs a different architecture than targeting.

A targeted promotion says: "Customers in our 'frequent buyers' segment get 15% off this week." A personalized promotion goes further. Imagine a shopper who browsed winter jackets three times this week and abandoned a cart with that jacket yesterday. Now visiting from a city where temperatures just dropped, that shopper gets a 10% offer on the jacket in the moment.

The mindset shift behind this matters as much as the technology. Sender-based programs start with what the brand wants to say, while recipient-based programs start with what the customer needs in the moment. Reorganizing promotions around the recipient changes how they are built, delivered, and measured.

Mass promotions lose ground because they treat every customer as the same customer. A single blanket discount ignores who is buying, what they would have paid, and whether they needed an incentive at all. According to Harvard Business Review and Talon.One, only 48% of executives rate their promotional spending as effective.

Three patterns repeat across categories:

Customers can tell when a promotion serves the brand at their expense, and that awareness is reshaping how companies plan. According to HBR and Talon.One, only 14% of organizations intend to increase mass discounting, while 24% plan to cut it back. Rising expectations point the same way, with 71% of consumers now expecting companies to interact with them on a personalized level.

Personalization improves customer engagement and retention and can lift revenues by 5% to 15%. The payoff compounds over time, with fast-growing companies pulling 40% more of their revenue from personalization than their slower-growing peers.

Personalized offers consistently outperform blanket discounts, and the gains show up even with a partial shift. Moving just 25% of mass promotions to personalized offers can lift offer ROI by 200%.

Retail personalization leaders grow revenue 10 percentage points faster each year than laggards. Analysts estimate that $570 billion in incremental growth will flow to those leaders before the decade is out.

80% of shoppers prefer brands that offer personalized experiences, and they report spending 50% more with them. Yet a perception gap remains. While 92% of retailers believe they personalize effectively, only 48% of consumers agree. That 44-point gap is the opportunity.

According to HBR and Talon.One, whose survey covered more than 400 executives, the outcomes are concrete. Among organizations already personalizing their promotions, 62% saw increased sales, 47% reported higher customer loyalty, and 44% saw improved customer experience. Respondents also reported growing interest in more personalized loyalty experiences.

The core logic of personalized promotions holds across sectors, but the data sources, channels, and constraints shift from one industry to the next. The sections below show how it plays out in retail, quick-service restaurants, financial services, and B2B.

In retail and ecommerce, the core use case is matching offer logic to individual customer context without relying on one-size-fits-all discounts. Adidas is a useful example. The brand needed to run promotions at global scale while still tailoring them to individual customer attributes. Using Talon.One, it generates millions of personalized coupons with unlimited discount options, including cart-level, per-item, and selective discounts for specific customer groups.

That setup changes the operating model. Teams build offers around product interest, customer profile, and timing, moving past the single-promotion-per-region approach. They gain control over which incentives appear, when they appear, and how much margin they give away.

Restaurants face a different version of the same challenge. Promotions need to work across apps, web ordering, loyalty programs, and physical locations, often at high frequency and with low tolerance for operational friction.

Panera Bread shows what that looks like in practice. Panera brought loyalty and discounts together on a single platform, migrated 1,100+ campaigns without disruption, and completed the rollout in only five months. The company also supports 60+ million MyPanera members.

Restaurant promotions rarely depend only on discount depth. They also depend on timing, channel coordination, and operational simplicity. A personalized offer that shows up at the right point in the customer journey is more useful than a generic offer sent to everyone at once.

In financial services, personalization usually centers on lifecycle moments, product relevance, and trust. A next-best-offer that arrives when the customer's situation calls for it beats a blanket cross-sell pushed to everyone, like a travel card surfaced before a booked trip. The mechanics differ from retail, but the principle holds. Use what you already know about the customer to make each offer more timely and useful.

Bilt Rewards shows how this plays out in financial services. The rent-to-rewards program lets members earn and redeem points across rent, travel, shopping, and fitness. Its marketing team builds and launches those campaigns on a no-code platform. That setup supports more than five million members and 40,000 merchant partners.

B2B has been slower to adopt personalization, but the companies that commit to it pull ahead. 77% of B2B companies that use one-to-one personalization report gains in market share.

B2B buyers expect the same level of personalization they experience as consumers. The gap between that expectation and current B2B promotional maturity is enormous. B2B programs often need to account for account hierarchies, negotiated pricing, sales involvement, and longer buying cycles. That makes the logic more complex, but personalization still has value.

Carlsberg shows that B2B personalization scales. The brewer runs personalized coupons and referral codes for over 25,000 resellers in more than 150 markets. It uses profile and real-time session data to tailor what each account sees. That approach cut promotion-related support tickets by 90%.

Delivering personalized promotions in any of these industries depends on a stack that can act on customer signals in real time. A customer data platform (CDP) brings together behavioral, transactional, and identity data across every touchpoint. Without unified customer data, personalization stays stuck at the segment level.

The decisioning layer takes a customer profile and determines which promotion to show, to whom, and at which moment. It requires rules-based logic, such as blocking discount stacking above a defined margin threshold. It can also use machine learning to decide which eligible offer is most likely to convert.

This is where an incentives platform like Talon.One becomes practical, bringing loyalty and promotions into one decisioning layer. Its Rule Builder lets marketing teams build if-then logic for promotions without filing engineering tickets.

The next layer is integration. Promotional logic needs to connect wherever customers transact, whether that is a website, mobile app, point-of-sale system, or drive-thru. Retail, restaurants, and banking each transact in different places, so the offer has to follow the customer across all of them.

Activation is the final layer. Customer engagement platforms like Braze deliver personalized promotions across email, push, SMS, in-app messaging, and web. When these platforms sync directly with commerce data, the offer reflects current catalog and purchase activity. The commerce platform and the engagement layer stay in sync.

These layers must operate continuously. A product view, a cart addition, or a purchase each creates an event that moves through the full stack. Platforms relying on batch data syncs personalize based on yesterday's customer state, missing the live signal that personalization requires. Many teams already recognize that limitation, and IT leaders are increasingly focused on upgrading the underlying systems to support stronger personalization.

Customer identity, purchase history, and promotional response data often sit in separate systems owned by separate teams. Data integration, technology, and organizational setup repeatedly surface as the factors that make or break personalization. The business case for fixing that fragmentation is tangible. According to HBR and Talon.One, 60% of organizations that integrated promotions and loyalty saw improved customer loyalty, and 58% saw increased sales or revenue.

A few traps derail personalization programs most often:

The shift from mass promotions to personalized offers is a structural change in how brands compete for customer attention and spending. The companies seeing the biggest returns share a common thread. They treat promotions as behavior-change tools, with discount depth calibrated to shift specific customer actions.

Across the board, executives are putting more scrutiny on promotional effectiveness and profitability. That scrutiny is healthy. Promotions are too expensive to run unless teams know whether they are changing behavior or subsidizing purchases that would have happened anyway.

The brands that win in this environment will deliver the right offer, to the right customer, at the moment it changes behavior.