Blis AI Platform Offers Deeper Targeting
Blis, recently acquired by T-Mobile, has launched Blis AI, a platform that uses foot traffic data and other real-world signals such as in-store sales to help brands with audience building and media planning.
If a brand wants to reach moms during back-to-school shopping season, they can enter KPI parameters such as “foot traffic,” and Blis AI will serve up different potential audiences to target, which could include those who are cost-conscious shoppers, focused on health and fitness, or those more inclined toward fashion.
The Blis DSP interface also provides zip-code locations for those audiences in the form of a map. Retail brands and their partners can zoom in on specific geo-locations for targeting. But there’s more to it than that.
Blis President Alex Boras talked to StreetFight about what is possible with Blis and how consumer behavior is changing.
How is the definition of “local marketing” changing, if at all, regarding consumer behavior?
You can’t just target consumers based on which ZIP code they live in. You need to understand their behavior in that ZIP code to do local marketing effectively.
Consumers don’t think of themselves as “local” audiences. They think in terms of habits, context, and intent. Whether they’re picking up dinner, shopping for school supplies, or test-driving a car, their real-world behavior generates signals that transcend traditional DMA boundaries.
That’s why modern local marketing is less about ZIP codes on a map and more about understanding what people are doing in those locations and why.
What are the biggest challenges retailers face when activating local marketing at scale?
The biggest challenge is balancing national efficiency with local nuance. Retailers often have hundreds or thousands of locations, each with its own competitive landscape, customer base, and behavioral patterns—but they rarely have the time or resources to plan bespoke campaigns for every market.
That tension leads to a lot of generic, one-size-fits-all media. It may be efficient to buy at scale, but it’s rarely effective at driving real outcomes like store visits or regional sales lift.
Another major hurdle is signal loss. Traditional targeting methods—based on IDs, cookies, or broad demographic overlays—are breaking down. That makes it harder to understand who your audiences are, where they are, and what’s driving their decisions.
How does Blis’s approach to location and behavioral data change the traditional media planning and buying model for retailers?
Instead of starting with an audience definition and building a plan around presumed interests or IDs, Blis AI starts with the outcome, like awareness, store visits, or sales—and works backward using real-world data.
That data includes verified movement patterns, purchase behavior, and environmental context. It’s not raw GPS or stitched-together IDs. It’s a dynamic intelligence layer that helps retailers understand not just where people are, but who they are and why they’re likely to act.
This changes the entire planning process. With Blis AI, retailers can input a goal and receive pre-built, outcome-optimized media strategies across mobile, DOOH, CTV, and desktop—complete with geographic targeting and performance scoring.
Are retailers using Blis to bridge the gap between upper-funnel brand campaigns and lower-funnel performance goals in localized markets? How so?
Absolutely. Retailers often struggle to connect awareness-driven media like CTV or DOOH with performance outcomes like store visits or sales. Those channels are traditionally siloed, and measurement rarely extends beyond views or impressions.
Blis helps bridge that gap by unifying upper- and lower-funnel strategy around real-world behavior. A retailer might use Blis to run CTV ads in specific DMAs during a product launch. At the same time, they can track how those exposed audiences behave across other channels—like mobile or DOOH—and measure whether foot traffic or in-store sales increase in targeted regions. Retailers can target the same cohort across every screen and measure full-funnel impact.
How does Blis integrate with or complement retail media networks like those of Walmart, Target, or Kroger?
Retail media networks are powerful, but they’re inherently limited to owned data and owned properties. Blis AI doesn’t replace RMNs, it enhances them. We complement first-party retail data with privacy-safe, location-driven insights that help marketers plan, activate, and measure omnichannel campaigns tied to real-world outcomes.
What three predictions can you make for local marketing for the next two years?
Local media will shift from planning by ZIP code to planning by behavior. As identity fades and consumers become harder to track, retailers will lean more heavily on real-world behavioral signals, like movement patterns and purchase context, to define local audiences and drive outcomes. Geography won’t go away, but it will become a layer, not the foundation.
Measurement will become location-anchored. Retailers will prioritize media partners who can tie spend to outcomes like store visits, regional lift, or incremental sales. Impressions and CTRs won’t be enough—especially in channels like CTV or DOOH where physical results matter most.
AI will become the local marketer’s co-pilot. Campaign planning at scale will be increasingly automated, with AI generating audience strategies, budget allocation, and creative recommendations based on real-time conditions. The winners will be the brands who treat AI as a performance tool not just an efficiency tool and ground it in real-world data.
