Your Customers Think You Don’t Get Them. Here’s What the Data Says:

You’re running AI-powered campaigns. You’re personalizing subject lines. You’ve got automations humming away in the background. From where you’re sitting, things are looking pretty good.

But what if your customers don’t see it that way?

New research from SAP, surveying thousands of consumers and marketers across global markets, reveals a persistent and costly gap between what marketers believe they’re delivering and what customers actually experience. And the disconnect isn’t marginal. 

The findings are laid out in detail in our latest guide, 8 Costly Retail Marketing Blind Spots & How to Fix Them, Fast. But before you download it, let’s unpack why these gaps exist, and why closing them could be the highest-ROI move you make this year.

This gap represents a fundamental misalignment in the value exchange, the very foundation on which permission-based, first-party data strategies are built. And it has real consequences: if your customers don’t feel they’re getting a fair deal, they disengage, they opt out, and they take their data (and their money) elsewhere.

The uncomfortable truth is that a simple discount code at sign-up doesn’t constitute a value exchange anymore. A special offer might have felt sufficient a few years ago, but consumers have become savvier about what their data is worth. They expect the trade to be ongoing: share data, receive genuinely personalized experiences in return. Not a one-time coupon – a continuous, visible payoff.

If you’re using AI in your marketing, here’s a question worth asking yourself: Are your customers actually feeling the difference?

56% of consumers say AI has had no impact on their buying decisions. That’s because much of the AI investment right now is focused on internal efficiency, such as faster campaign launches, automated workflows, and optimized send times. Those are valuable operational gains, but if the customer experience on the receiving end still feels generic, you’ve optimized the engine without improving the overall journey.

The issue often comes down to what we think of as “shallow personalization”, like a first name in the subject line, or a product grid based on a single browsing session. 

This might technically be personalization, but it’s not the kind of personalization that makes a customer feel recognized and understood. They’ll accept the recommendation, but for it to truly pay off, it needs to be backed by first-party data and accurately reflect who they are, what they want, and what they’re likely to buy next.

AI investment and adoption continue to accelerate, which means the gap between what brands are doing with customer data and what customers trust them to do is widening. If you’re not actively communicating how you protect data, and demonstrating the tangible value customers receive in return for sharing it, you’re building your personalization strategy on increasingly shaky ground.

None of this means you should pump the brakes on AI. But there’s a difference between adopting AI internally and making your customers feel comfortable with how it’s being used. The brands that get ahead will be the ones that treat transparency as a genuine engagement strategy, not something buried in a privacy policy footer, but something that actively earns trust and gives customers a reason to stay.

Customers are giving brands a green light to do more with AI, and most brands aren’t taking them up on it. Product recommendations work. Customers appreciate them. So why stop there?

The opportunity is to expand from recommending products to personalizing experiences:

• Curated gift guides that adapt to a customer’s relationship milestones and purchase history
• Style quizzes and product finders that evolve as preferences change over time
• Loyalty offers that reflect not just what someone bought, but when and why they buy
• Replenishment reminders timed to actual usage patterns, not arbitrary intervals

Brands with the right data and automation infrastructure are already running these kinds of experiences and seeing the results, so the appetite from customers is clearly there. For most teams, it’s less about knowing what to do and more about finding the bandwidth and the right solutions to make it happen.

That’s a significant underinvestment in a channel customers are already comfortable with. And when connected to customer data, chatbots become far more than a deflection tool for support tickets. They become a real-time engagement channel that can:

• Resolve friction at checkout before it becomes an abandoned cart
• Surface personalized recommendations during a service interaction
• Feed intent and sentiment signals back into your marketing automations

The brands getting this right aren’t treating chat as a cost center. Instead, they’re viewing it as a data-enrichment and engagement channel that makes every subsequent interaction smarter.

Recognizing the blind spots is the first step. Closing them is where the commercial impact lives.

The good news is that none of these gaps require starting from scratch. In most cases, the data, channels, and tools already exist. They just need to be connected, integrated, and pointed more squarely at customer outcomes rather than internal metrics. Start here:

• Audit your value exchange: Make sure customers see, and feel, the payoff of sharing their data. If the only reward is a one-time discount, it’s time to rethink.
• Move beyond cosmetic personalization: Lifecycle-driven journeys that anticipate what customers need before they ask will always outperform a first-name merge tag.
• Make AI’s impact visible to the customer: Back-in-stock alerts, personalized replenishment, predictive recommendations. Show them AI is working for them.
• Communicate about privacy with care and creativity: Give it the same attention you’d give a product launch. Your customers are paying attention, even if they’re not saying so.

We’ve mapped out all eight blind spots, including the specific fixes and the platform capabilities to action them, in our latest guide.