Precise geolocation has long been regarded as one of retail marketing’s most valuable signals. It powers localized promotions, store visit measurement and loyalty programs that depend on knowing not just who a customer is, but where they are. This capability once felt like a competitive advantage, but it has quickly become a major operational liability.
The legislative picture is moving at breakneck speed. Virginia banned the sale of precise geolocation data with effect from July 1. Connecticut followed, with its own ban taking effect in October. Maryland banned the sale of sensitive data (including precise geo) last year, and New Jersey just joined them. Further, California’s AB 1542, banning the sale of all sensitive data, appears set to land on the governor’s desk. Nearly a dozen other states are considering opt-in consent requirements to outright bans, but the damage has already been done.
This is no longer a patchwork of isolated state experiments – it’s an accelerating shift away from the infrastructure that location-based retail marketing has depended on for a decade.
Like Politics, All Laws are Local
Many retailers will respond to each new law as a routine compliance hurdle: update the consent banners, ring-fence by region, or partner with a third-party identity broker to paper over the gaps. These may seem like practical responses but are in fact fundamental misreadings of the tectonic shifts happening in nearly every statehouse.
Procedural laws can be addressed through changes to consent flows and better data governance whereas bans are a strict prohibition. You cannot consent-banner your way out of a ban. The data flows that a retail marketer’s entire stack has depended on are getting severed at the source, and no privacy policy, however carefully drafted, changes that.
The critical question for retail marketers isn’t how to navigate the legislation. It’s whether their technology actually needs this sensitive data to perform in the first place.
The Surveillance Assumption
Location-based marketing has always rested on a specific assumption: that knowing where a consumer is, or has been, tells you something meaningful about what they’re likely to do next. In many cases that assumption held. A consumer who visits a competitor’s store is a reasonable retargeting candidate. Someone who regularly passes a particular storefront is plausibly in-market for what that location sells. However, this assumption also carried systemic costs that the industry was slow to acknowledge.
Precise geolocation is classified as sensitive data for good reason. It can reveal where people live, work, worship, seek medical care, and more. The legislative backlash now reshaping the landscape reflects a growing realization that the ad stack was being used for purposes well beyond retail marketing. The data flowing through it was far more revealing than any individual advertiser intended, that it had value to organizations with far less noble intent.
Retailers who built their measurement and attribution models on this foundation are now exposed – not because they acted in bad faith, but because their counterparties’ legal status is now their compliance problem. If a store-visit measurement vendor or loyalty app transfers or sells precise coordinates, the retailer is facing material, legal, contractual and reputational exposure through its data supply chain.
The financial math is also unforgiving. When penalties are assessed on a per-record basis, the potential fines applied to a localized audience segment vastly exceed any incremental revenue that segment could ever produce.
What Replaces Geolocation?
The honest answer is that most retail marketing outcomes don’t require precise location data at all. What they require is accurate prediction of consumer intent – and that is a solvable problem without crossing regulatory boundaries.
Retailers already have access to channels that capture intent without touching sensitive location data. Commerce media networks built on first-party purchase signals, CTV platforms that surface in-market behaviour through content consumption, and AI-driven contextual targeting that reads what a consumer is actively engaging with. All of these offer meaningful intent signals that don’t depend on knowing where someone is.
Within programmatic advertising, Deep Learning systems can identify complex relationships across large volumes of privacy-compliant contextual information that would be impossible for conventional models to process. Rather than depending on a consumer’s precise location, these systems can evaluate a far richer combination of intent signals, contextual behavior, browsing patterns and campaign performance to predict which marketing opportunities are most likely to generate incremental business outcomes.
By evaluating the true incremental ROI of every single bid through advanced modeling, this shifts the conversation from ‘where is this consumer?’ to ‘what is this consumer most likely to do next?’ For most retail marketing objectives, the second question is both more valuable and achievable.
Prepare for Tomorrow
Retailers that continue treating each new privacy law as an obstacle to work around will spend the next several years in permanent reactive mode, constantly retrofitting old workflows while their underlying exposure remains unchanged. That is an expensive and unsustainable position.
The far more resilient approach is to decouple marketing stacks from sensitive data, not just because regulators demand it, but because the business case for doing so is stronger. Measurement strategies that don’t rely on invasive tracking. Technology stacks built for the privacy demands of tomorrow rather than the data availability of yesterday.
The retailers who move earliest to shed expiring data sets will spend less time managing compliance risk and more time building the kind of performance infrastructure that lasts.
Charles Simon is VP of Private Advertising, RTB House
