The Consent Illusion: Why Retail Personalization Is Running Ahead of Consumer Trust

Only 39% of consumers trust organizations to use their personal information responsibly, according to the Qualtrics 2026 Global Consumer Trends Report, which surveyed more than 20,000 consumers across 14 countries in Q3 2025. That figure sits alongside another from the same report: 64% of those same consumers say they prefer personalized experiences. The distance between those two numbers is not a paradox. It is a description of how most consumers currently experience the retail data economy, wanting relevance while doubting the intentions of the parties delivering it.

Consumer concern about personal data misuse rose eight points year-over-year in the Qualtrics research, reaching 53% of respondents. Nearly one-third said they are uncomfortable with brands learning their habits, remembering website behavior, predictive ordering, and device listening or watching. Only 41% believe the benefits of personalization justify the privacy costs. Isabelle Zdatny, head of thought leadership at Qualtrics XM Institute and the report’s author, described the dynamic as a “creepiness to value ratio,” noting that shoppers are continuously making intuitive judgments over whether personalization feels helpful or like surveillance.

None of this means personalization is failing as a commercial strategy. McKinsey research documents that it most often drives 10 to 15 percent revenue lift, with companies that excel at it generating 40% more revenue from those activities than slower-growing counterparts. The question is what kind of personalization, built on what data, delivering what visible value to the consumer, and whether the people receiving it have any meaningful sense of the exchange they entered into.

The Compliance Problem Underneath the Trust Problem

Joseph Turow, professor emeritus of communication at the University of Pennsylvania’s Annenberg School for Communication, frames the structural issue as one of resignation rather than consent. In his forthcoming University of Chicago Press book, “The Problem with Personalization: How Advertisers Learned to Make and Break Us from Ancient Times to the AI Age,” Turow argues that consumers accept data collection not because they endorse it but because they perceive no meaningful alternative. That distinction matters for retailers and brands because apparent tolerance for data collection, visible in opt-in rates and loyalty sign-ups, does not map reliably to genuine trust. A consumer who joins a loyalty program to access discounts has not necessarily agreed to receive inferences drawn from their browsing behavior across unrelated sessions.

Zdatny draws a useful taxonomy. Low-intensity personalization, such as recently viewed items and location-based currency changes, generates little friction. Relationship-based personalization, including remembered preferences, pre-filled forms, and reorder reminders, lands well when there is an established customer relationship to draw on. The discomfort concentrates around what she describes as high-intensity approaches: browsing-based recommendations, churn prediction, and surveillance pricing. That is where retail technology has been advancing most aggressively, and where the Qualtrics data shows consumer tolerance running thinnest.

The trust deficit is compounding in retail media specifically because of how first-party data is activated across networks. Retailers build their media propositions on the richness of their purchase and behavioral data, and CPG brands invest in those networks because the targeting is more precise than what open programmatic environments can offer. eMarketer’s December 2025 forecast puts US retail media spending at approximately $71.09 billion for 2026, with close to 90% of that investment flowing to Amazon and Walmart. The targeting infrastructure at both companies depends directly on data that consumers, in survey after survey, say they are not confident they have knowingly shared for advertising purposes.

Walmart’s retail media business generated $4.82 billion in 2025 and is forecast by eMarketer to continue growing by double digits through 2027. That scale represents an enormous volume of consumer behavioral data being activated for CPG brand targeting. When only 39% of consumers trust organizations with their personal information, a meaningful share of the audience being reached through retail media networks sits in the skeptical majority, and the programs running against them are largely invisible to the people they target.

What Breaks and Why

The asymmetry between how personalization succeeds and how it fails has specific operational implications. When it works well, Zdatny notes, consumers do not notice it. The well-timed reorder reminder, the product suggestion that fits the established basket, the continuity of a promotion across channels: these generate no complaints and require no explanation. When personalization misfires, it creates a story worth sharing. The frequently cited example is the recommendation engine that concludes pregnancy from a single search, then generates a persistent stream of irrelevant and sometimes invasive content. No one tweets about an airline remembering their preferred seat, as Zdatny observed. Everyone notices when targeting inference produces an absurd or unsettling result.

Jeannie Walters, founder of Experience Investigators, raises a related failure mode tied to operational disconnection. When a personalized offer delivered through a mobile app cannot be accessed by a customer service representative after the underlying code fails, the consumer experience is not a minor inconvenience. From the consumer’s perspective, the brand “broke a promise,” as Walters put it. That failure does not originate in the personalization strategy; it originates in the disconnected systems underneath it. CPG brands operating across multiple retail environments face this problem at meaningful scale. A shopper interaction personalized through one retailer’s loyalty ecosystem and another’s retail media network may rest on data that does not reconcile, producing experiences that feel incoherent even when each individual system is functioning as designed.

Katie Costanzo, president of CX at CSG, frames the coherence problem as a baseline issue: “Personalization only works when trust is already in place.” That observation carries a specific consequence for brands investing in increasingly sophisticated targeting capabilities. The Qualtrics 2026 report finds that 86% of customers are willing to share more personal data if organizations are more transparent and clear about its usage. The constraint is not consumer unwillingness to participate in personalized experiences. The constraint is that the value exchange has not been made sufficiently legible.

The Threshold Question for Retail Operators

For senior operators across retail and CPG, the practical question Zdatny’s research poses is not whether to personalize but which forms of personalization can be explained to the consumer in terms that make the value exchange credible. Her test is direct: if a brand cannot articulate why it needs a particular piece of data and how it benefits the consumer, that personalization approach should not be adopted. Applied to retail media, that test is more demanding than it might initially appear. Off-site targeting that activates purchase history to reach shoppers on third-party properties sits at a different point on the intensity spectrum than a reorder reminder sent to a loyalty member. Both are described in category terms as personalization, but consumers respond to them very differently, and the data collection necessary to power them was likely not framed that way at sign-up.

The Qualtrics finding that misuse of personal data is now consumers’ top concern about AI is significant for how retail operators should be thinking about the technology layer in their personalization programs. Recommendation engines, churn prediction models, and dynamic pricing systems are all AI applications now embedded in major retail platforms, and half of respondents in the Qualtrics research said they worry about losing human connection as those systems scale. When those concerns are concentrated in the same population that retail media networks are targeting with first-party behavioral data, the exposure is not theoretical.

A VP of retail media at a large CPG company and a head of digital commerce at a mid-market brand are operating in the same environment with meaningfully different risk profiles. The large CPG brand likely has media volume and retailer relationships that allow it to access first-party targeting at scale, along with compliance infrastructure to manage consent governance. The mid-market operator may be relying on inferences drawn through retail media network targeting without full visibility into what data is being used or how it was collected. Each faces a version of the same problem; the consequences of a trust failure are not the same size.

Loyalty program design, which has become foundational to retail media strategy across every major network, deserves particular scrutiny here. A consumer joining a loyalty program expecting promotions has not necessarily consented to having their behavioral data used to predict churn or adjust pricing. The difference between those two experiences is invisible until it is not, and the Qualtrics research suggests that a large share of consumers are already operating with low reserves of goodwill toward the brands making those inferences. Zdatny’s framing of trust as “the prerequisite, not the outcome” is most useful when read alongside Turow’s description of resigned acceptance: the loyalty member who does not opt out is not the same as the loyalty member who trusts the brand. Those two populations may look identical in the data and respond very differently when the inference goes wrong.

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