What’s In Your Store for Retail Media with Footprints AI?

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Overview

Turn every store visit into new media revenue

Retailers are sitting on a goldmine. Every shopper walking into your store, every item scanned at checkout, every product on a shelf - it’s not just commerce. It’s data. And that data can power a whole new business: Retail Media.

Turn every store visit into new media revenue

With Footprints AI, you can start generating high-margin revenue from brands and agencies, without changing how you run your stores.

Here’s how it works:

You already know what sells. Now, know who’s buying and why

Footprints AI uses your existing ePOS data to profile every visitor. No apps. No loyalty programs. Just behavior.

You already know what sells. Now, know who’s buying and why

From this, it builds 12 real shopper personas, customized to your business. Imagine: the Nest 1 family with newborn where dad does weekly shopping, the Dual Income No Kids couple shopping for health-conscious products, the Young Professional who’s impulse buying on their lunch break.

The more you know about who’s walking into your store, the more valuable your media becomes. And if you’ve got WiFi, sensors, or cameras - even better! Our AI adds real-time precision without adding cost.

Your store becomes a media channel. Automatically

Footprints AI turns your in-store screens and audio systems into smart media assets.

Instead of looping the same content all day, the AI decides what ad to show, based on who's present and what’s likely to convert. It’s real-world programmatic.

Brands love it, because it performs like digital, but in the physical world.

And you love it - because they’ll pay premium, performance-based pricing for the opportunity. No new hardware. No added staff. Just smarter media, powered by the data you already have.

Your store becomes a media channel. Automatically

Now monetize what happens online too

Your best in-store customers leave signals behind. We use those signals to build media audiences online.

The AI finds people nearby who are browsing your website, using your app, or searching on Facebook or Google and identifies which of them behave like your in-store shoppers.

Monetize online too

You can now offer brands the chance to reach these high-intent users across channels. Not just in-store, but online too!

That’s omnichannel media, without needing an ad tech team.

It gets smarter. And more valuable

Because the AI sees behavior over time, it begins to understand more than just products; it sees life stages, income patterns, and upcoming needs.

Brands don’t just get reach. They get relevance. They can speak to shoppers who are ready to act, across any format: SMS, email, loyalty, Google, Meta, even Connected TV and digital-out-of-home.

Which means higher engagement. Better performance. And more revenue back to you - for every campaign.

Ai Gets Smarter

Everything ties back to sales. Every time

Footprints AI tracks media performance all the way to the SKU and store level.

When an ad plays, we measure what happens next. Who saw it or heard it. Who bought. What changed.

That level of transparency is why brands are willing to pay more and why your retail media business becomes more profitable, fast.

Two people staing in the store

Start making more from the media opportunity already in your store.

You’ve already built the traffic. You already have the data.Now it’s time to monetize it—like a media company would.Footprints AI makes it possible.

START NOW

In-Store Behavior Analytics

Turn Your Store Into a Living, Breathing Intelligence Engine

What If You Could See What Your Store Sees?

Imagine knowing where every shopper goes. What draws their attention. What makes them stop. What makes them act.

With Footprints AI, your store becomes self-aware. Every aisle, every zone, every screen - sensing, learning, optimizing. Everything in real-time.

And here’s the kicker: you don’t need new infrastructure. Our AI-powered engine uses your existing ambient connectivity: Wi-Fi, GSM, sensors, and cameras to unlock a powerful new layer of intelligence. No hardware investment. No operational disruption.

AI Girl looking into data server

With Footprints AI, you can start generating high-margin revenue from brands and agencies, without changing how you run your stores.

How It Works: Real-Time, AI-Driven Customer Intelligence

Footprints AI turns ambient signals into intelligent insights:

  • Wi-Fi and GSM data show how people move
  • CCTV and sensors track what they engage with
  • POS systems confirms what they buy

This data is mapped to a Digital Twin of your store. Zones are brought to life. Behavior is analyzed in real time. Predictions are generated before decisions are made.

AI girl staying in the business center

"Heatmap the flow of people. Predict the moments they’re most likely to buy. Target the exact screen at the exact second. That’s what in-store analytics should do."

What You Learn, Instantly

  • Which zones drive the most engagement (and which don’t)
  • How shopper behavior changes by hour, day, or weather
  • Where repeat customers tend to go first
  • What time of day a certain audience segment shops
  • Which promotions actually shift behavior
Photo of grocery store

Example Insight: "64% of lunch-hour traffic dwells in ready-to-eat zones. Ideal for snacking or beverage campaigns."

Why It Matters for Retail Media

This isn’t just about analytics. It’s about power - Power to:

  • Sell media based on audience, not screen time
  • Plan campaigns that hit with precision, not hope
  • Report value not impressions, but impact

And it gets better: this is the backbone of everything.

  • Real-time profiling? Powered by in-store analytics.
  • Dynamic screen targeting? Built on behavioral intelligence.
  • Measurement and media-to-sales attribution? All made possible by the same engine.
Family in the grocery store

When you power your store with real-world data, you’re not just selling ads - you’re selling access to high-value audiences with verified behavior, precision targeting, and provable results.

That’s why media buyers will pay more. A lot more.

  • Score audiences live - trigger campaigns only for high-value shoppers.
  • Place screens where the dwell happens. Not where they used to be.
  • Let AI tell you the best time to play your ad.
  • Sell by audiences, attention & impact - not assumptions.
  • Prove uplift. Show zone behavior shifts. Track attention to purchase.
  • Use behaviors to strengthen your predictive audiences. Retarget smarter next time.
Two people looking at the floating screen

In-Store Behavioral-Based Profiling

Turn anonymous foot traffic into the most valuable media audience on planet Earth.

Know who’s shopping - even if they never tap a screen.

Our In-Store Behavioral-Based Profiling engine transforms anonymous in-store behavior into powerful, real-time audience signals. It gives retailers and advertisers what they’ve never had inside physical stores:

  • The ability to recognize, segment, and act on behavioral intent
  • In a privacy-first way
  • At scale, applicable to all and any of your in-store visitors
Photo of supermarket interier

This isn’t demographic guesswork. It’s precision modeling, powered by AI, observed behavior, and shopping mission prediction.

From people flow to customer DNA

Every step, aisle pause, and repeated visit reveals intention. Our proprietary models observe in-store behavior over time and map it to:

  • Life Stage
  • Shopping Mission
  • Shopping Preferences
  • Visit Rhythms
  • Category Affinities
  • Household Disposable Income
People in the supermarket

Examples:

Anonymous Shopper A1083XYZ
Visits a mall-based supermarket twice a week after work. Picks up frozen meals, energy drinks, and basic personal care.

  • Profiled as a Young Single, value-oriented, low-to-mid disposable income, high interest in convenience formats and on-the-go nutrition.
  • Preferences: Budget brands, frozen meals, energy drinks, convenience-first products. Propensity to Visit: 2x/week (weekday evenings).
    Propensity to Buy: Frozen meals, snacks, basic personal care.Affinities: Quick meals, digital engagement, plant-based interest.
  • Socio-Demo: Male, 24–28, lives alone/with roommates, urban renter.
  • Non-Grocery Potential: Budget banking apps, mobility subscriptions, early-career insurance.

Anonymous Shopper D2045NRC
Visits every Friday mid-day with a toddler, purchasing formula, fresh produce, family-sized frozen meals, and wipes.

  • Profiled as a Full Nest I Family, parent 30–35, suburban, mid-income, stock-up mission, family health focus.
  • Trigger time: Pre-weekend provisioning. Likely to respond to parenting bundles and weekend cashback offers.
  • Non-Grocery Potential: Family insurance, health care, savings plans.

Each profile tells a story, not just of who they might be, but why they came, what they want, and how often they return.

Profiles built on signals, not assumptions

Our profiling engine builds and evolves each audience using real, cross-referenced in-store behavior, including:

  • Life Stage – inferred from rhythm, mission, and preference clusters
  • Shopping Mission – stock-up, top-up, discovery, indulgence, urgency
  • Preferences – brand tier (value, mass, premium), flavor profile, format loyalty
  • Propensity to Visit – frequency, timing, and journey pattern
  • Propensity to Buy – confidence levels across categories
  • Affinities – co-purchase tendencies and thematic interest (e.g., health & wellness, family meals)
  • Household Disposable Income – based on product choices, basket size, and mission frequency
  • Visit–Purchase–Profile Match – AI-generated pattern matching across store traffic, basket data, and behavioral segmentation
People in the supermarket

Anonymous Shopper H8832RJP
Shops mid-week and Saturdays. Buys functional foods, artisan snacks, and specialty cookware. Engages with wellness signage and recipe kiosks.

  • Profiled as a Mature Single, 45–50, urban, upper-middle income, strong wellness + culinary interests, indulgent weekend pattern.
  • Affinities: Solo lifestyle, cooking, premium discovery, gut health, gourmet snacks, lifestyle categories. High brand loyalty and promo responsiveness.
  • Non-Grocery Potential: Boutique health, solo travel, wealth management

This isn’t a static persona. It’s a dynamic behavioral graph, continuously updated, scored, and ready for activation.

12 Life Stage Segments. 1 scalable engine.

We built a segmentation model that mirrors modern retail audiences, from Young Singles and DINKs, to Full Nest Families, Empty Nesters, and Solitary Survivors.

Each with their own:

  • Shopping rhythm
  • Motivations
  • Purchase drivers
  • Message and channel response signals
1. Young Single (Ages 18-30)

1. Young Single (Ages 18-30)

Shopper Journey

Visits a mall-based supermarket twice a week after work. Picks up frozen meals, energy drinks, basic personal care. Visits are short (12–18 min), mostly focused on convenience. Occasionally interacts with in-store QR codes and promo screens.

Profiling Inferences
Life Stage

Young Single

Preferences

Budget brands, frozen meals, energy drinks, convenience-first products

Propensity to Visit

2x/week (weekday evenings)

Propensity to Buy

Frozen meals, snacks, basic personal care

Affinities

Quick meals, digital engagement, plant-based interest

Socio-Demo

Female, 24–28, lives alone/with roommates, urban center

Non-Grocery Potential

Budget banking apps, mobility subscriptions, early-career insurance

Shopper Avatar - 2. Young Professional (Ages 22-30)

2. Young Professional
(Ages 22-30)

Shopper Journey

Shops weekday mornings and evenings at a convenience-format store near work. Regularly buys Greek yogurt, granola, cold-pressed juices, pre-packed salads, and cosmetics. Uses in-app coupons. Recently engaged with QR ads for smart lunch deals.

Profiling Inferences
Life Stage

Young Professional

Preferences

Health-forward snacks, eco-friendly cleaning, cosmetics

Propensity to Visit

3–5x/week (morning + evening)

Propensity to Buy

Functional foods, cosmetics, light dinners

Affinities

Wellness, ethical products, grab-and-go routines

Socio-Demo

Female, 27–30, urban, mid-to-upper income

Non-Grocery Potential

Wellness apps, travel, career finance tools

Shopper Avatar - 3. Young Couple (Ages 25-35)

3. Young Couple (Ages 25-35)

Shopper Journey

Joint shopping on Saturdays and midweek. Buys fresh produce, wine, cheese, and gourmet meal kits. Uses loyalty cards, browses world food aisle, and engages with digital shelf signage and pairing recommendations.

Profiling Inferences
Life Stage

Young Couple

Preferences

Premium, experiential grocery

Propensity to Visit

1–2x/week (Sat + midweek)

Propensity to Buy

Fresh food, imported sauces, gourmet bundles

Affinities

Cooking, exploration, shared experiences

Socio-Demo

Couple, 30–35, dual income, mid-high income

Non-Grocery Potential

Joint accounts, travel insurance, premium vehicles

Shopper Avatar - 4. Full Nest I (Young Kids)

4. Full Nest I (Young Kids)

Shopper Journey

Shops Friday mornings with toddler. Purchases include baby formula, fruit, dairy, wipes, and frozen meals. Uses family loyalty coupons. Engages with parenting bundles and digital displays showing family deals.

Profiling Inferences
Life Stage

Full Nest I

Preferences

Family-friendly formats, nutritional kids’ food

Propensity to Visit

Weekly + emergency top-ups

Propensity to Buy

Baby care, produce, family frozen meals

Affinities

Health for kids, bundled promotions

Socio-Demo

Parent 30–35, suburban, mid-income

Non-Grocery Potential

Family insurance, health care, savings plans

Shopper Avatar - 5. Full Nest II (Older Kids)

5. Full Nest II (Older Kids)

Shopper Journey

Sunday stock-up and midweek top-ups. Basket includes cereals, juices, meats, sports drinks, lunchbox snacks. Teen-driven influence visible in choices. High use of in-app deals and loyalty perks.

Profiling Inferences
Life Stage

Full Nest II

Preferences

Branded snacks, family-sized goods

Propensity to Visit

2x/week (Sun + midweek)

Propensity to Buy

Pantry, meat, snack categories

Affinities

School-driven patterns, energy products

Socio-Demo

Family with kids 8–14, suburban, budget conscious

Non-Grocery Potential

Auto insurance, school banking tools

Shopper Avatar - 6. Full Nest III  (Teens-Young Adults)

6. Full Nest III (Teens/Young Adults)

Shopper Journey

Bulk weekend shopping and quick weekday meals. Teenager presence seen in basket: frozen food, drinks, gadgets. Engages with tech promos and music-themed displays.

Profiling Inferences
Life Stage

Full Nest III

Preferences

Convenience, tech-savvy products, snacks

Propensity to Visit

2x/week

Propensity to Buy

Bulk frozen meals, beverages

Affinities

Teen influence, digital lifestyle

Socio-Demo

Parents 45–50, suburban, 2+ teenagers

Non-Grocery Potential

Teen insurance, higher education finance

Shopper Avatar - 6. Full Nest III  (Teens-Young Adults)

7. Single Parent Family

Shopper Journey

Shops Monday and Friday for school lunch items, diapers, and affordable dinners. Uses paper coupons and digital loyalty. High sensitivity to bundle pricing and promotions.

Profiling Inferences
Life Stage

Full Single Parent

Preferences

Budget, efficiency, child nutrition

Propensity to Visit

2x/week

Propensity to Buy

Diapers, snacks, frozen meals

Affinities

Time-saving products, reward bundles

Socio-Demo

Female, 30s, urban/suburban, limited income

Non-Grocery Potential

Micro-loans, renter’s insurance

Shopper Avatar - 8. Mature Single (Ages 40-55)

8. Mature Single (Ages 40-55)

Shopper Journey

Shops midweek for lean protein, organic groceries, and weekend indulgences like wine and cheese. Interested in home cookware. Interacts with digital recipes and health signage.

Profiling Inferences
Life Stage

Mature Single

Preferences

Artisan, organic, health-enhancing

Propensity to Visit

2x/week

Propensity to Buy

High-quality, functional items

Affinities

Solo lifestyle, cooking, premium discovery

Socio-Demo

Single, 45–50, urban, mid-high income

Non-Grocery Potential

Micro-Boutique health, solo travel, wealth managementloans, renter’s insurance

Shopper Avatar - 9. DINKs (Dual-Income, No Kids) (Ages 30-50)

9. DINKs (Dual-Income, No Kids) (Ages 30-50)

Shopper Journey

Sunday shopping for gourmet products, meal kits, and imported condiments. Midweek refresh for oat milk, pet food, and skincare. Uses platinum loyalty tier and recipe kiosks.

Profiling Inferences
Life Stage

DINKs

Preferences

Premium, ethical, cross-category

Propensity to Visit

1–2x/week

Propensity to Buy

High-end food, pet care, skincare

Affinities

Lifestyle-led bundles, experience focus

Socio-Demo

Couple, 38–45, urban, high income

Non-Grocery Potential

Luxury travel, smart investment, EV leasing

Shopper Avatar - 10. Empty Nest (Ages 50–65)

10. Empty Nest (Ages 50–65)

Shopper Journey

Saturday main shop for vegetables, fish, wine, and supplements. Tuesday visit for fresh top-ups. Engages with wellness signage and loyalty content. Recently redeemed cooking class voucher.

Profiling Inferences
Life Stage

Empty Nest

Preferences

Health-forward, light indulgence

Propensity to Visit

2x/week

Propensity to Buy

Wellness food, light gourmet

Affinities

Health aging, slow food, weekend leisure

Socio-Demo

Couple 55–65, suburban/urban, upper-mid income

Non-Grocery Potential

Retirement plans, cultural travel

Shopper Avatar - 11. Active Senior (Ages 65+)

11. Active Senior (Ages 65+)

Shopper Journey

Monday and Friday visits to local supermarket. Buys probiotic yogurts, soft fruit, soups, and OTC vitamins. Engages with wellness ads and community programs. Uses senior loyalty tier.

Profiling Inferences
Life Stage

Active Senior

Preferences

Functional foods, simple formats

Propensity to Visit

2x/week

Propensity to Buy

Digestive health, light meals

Affinities

Community, wellness, light indulgence

Socio-Demo

Female, 65+, retired, digitally cautious

Non-Grocery Potential

Pension planning, curated senior travel

Shopper Avatar - 12. Solitary Survivor (Ages 70+)

12. Solitary Survivor (Ages 70+)

Shopper Journey

Tuesday and Saturday morning visits. Buys single-portion foods, soft bread, herbal tea, and flowers. High brand loyalty, paper coupons, interacts with staff frequently.

Profiling Inferences
Life Stage

Solitary Survivor

Preferences

Familiar brands, easy-to-prepare food

Propensity to Visit

2x/week (mid-morning)

Propensity to Buy

Small portions, senior care items

Affinities

Simplicity, routine, nostalgic brands

Socio-Demo

Female, 75+, widowed, fixed income

Non-Grocery Potential

Estate planning, home safety

Anonymous Shopper I7204FNC
A dual-income couple visiting every Sunday and Wednesday, buying gourmet kits, imported condiments, kombucha, and luxury skincare.

  • Profiled as DINKs, high income, experiential shopping focus, indulgence-led weekend missions.
  • Responds to: Lifestyle bundles, ethical brand storytelling, curated recipes.

Anonymous Shopper L1407ZCR
Visits a local supermarket twice a week in the morning. Buys single-serving meals, low-sodium soups, soft fruits, and OTC digestive aids.

  • Profiled as a Solitary Survivor, low disposable income, wellness-prioritized, high loyalty to staff suggestions and printed offers.
  • Ideal for in-store messaging on convenience, familiarity, and safety.

Behavior becomes media, intent becomes strategy, profiles become performance.

Why it works at scale: Data fusion with real-world grounding

Our profiling engine isn’t standalone. It’s embedded in Footprints AI’s In-Store Data Mesh, powered by our Homomorphism Engine.

That means profiling connects:

  • Offline behavior with online signals
  • Media with transactions
  • Visit journeys with product purchases and life signals
  • Real-world actions with omnichannel targeting, always privacy-safe
Girl stays in futuristic grocery store

Anonymous Shopper E6710PLV
Family of four. Bulk basket of snack multipacks, frozen meals, kids’ drinks every Sunday. Midweek top-ups for quick dinners.

  • Profiled as Full Nest II, price-sensitive, teen-driven, influenced by school schedule.
  • Footprints AI maps store traffic to basket contents and family routines, triggering dynamic content on midweek campaigns and “snack hack” bundles.

This enables:

  • Identity-light behavioral profiling
  • Offline-to-online personalization
  • Real-time, dynamic in-store screen delivery
  • Full-circle media-to-sales attribution

Built for business outcomes

In-Store Behavioral-Based Profiling powers:

  • Audience-led campaign strategy
  • Real-time targeting by mission, time, and store
  • Brand and category growth through shopper segmentation
  • Smarter screen playlists and content decisions
  • Full-loop ROI tracking - from audience signal to in-store purchase
Girl in white blouse stays in supermakter

Stop guessing who walks into your store.

Media Audiences Powered by In-Store Behavior

Where others see foot traffic, Footprints AI sees first-party media audiences.

Every step of each and every shopper browsing and purchasing inside your store can fuel your next media audience. Footprints AI captures this anonymous behavioral data and turns it into precise, segmentable audience profiles ready for omnichannel activation.

We don’t need loyalty cards. We don’t need surveys. We don’t even need a mobile app.

We observe behavior, in-store, and match it to high-confidence audience signals.

What we build audiences from:

  • Life Stage
  • Shopping Mission
  • Product & Category Affinity
  • Brand Affinities
  • Visit Frequency and Timing
  • Store Type and Aisle-Level Behavior
  • Household Disposable Income

But we don’t stop at in-store. Your best in-store customers leave signals behind. We use those signals to find lookalikes in the digital world.

Our AI identifies people nearby who:

  • Are browsing your website
  • Are using your mobile app
  • Are searching for you on Google
  • Are engaging with your content on Facebook or TikTok

Then Footprints AI’s unique offline-to-online customer data fusion matches them to your highest-value in-store segments, the ones who’ve already walked your aisles, stood in front of your displays, and checked out with your products.

This Digital Twin of the Customer creates true omnichannel customer profiles that can be uniquely identified by up to 468 unique attributes, for each individual, based on unique behaviors observed in each store and its physical and digital surroundings, what we call the “catchment area.”

This is how you offer brands something powerful: The ability to reach high-intent shoppers across every channel, not just in-store, but online too.

No DMP. No CDP. No ad tech team is required. Just real behavior, turned into real audience reach.

These aren’t modeled personas. They’re grounded, real, and ready for:

  • In-store screen & radio targeting
  • Website retargeting
  • In-app targeting
  • Off-site retargeting like on DOOH and CTV
  • CRM enrichment
  • Email, SMS & WhatsApp engagement
Girl in futuristic supermarket

Build smarter and more premium media audiences — starting with real-life behavior.

In-Store Dynamic Media Delivery

Most in-store networks play ads on a loop. Footprints AI plays what matters, based on who’s walking by.

Once we know who’s in the store, we don’t wait. We dynamically deliver the right ad, on the right channel, at the right second, based on who’s walking by and what their behavior tells us they’re likely to do.

This is real-time in-store targeting. Not playlist loops. Not static ads.

How it works:

  • Each screen location is linked to high-frequency audience detection zones
  • The playlist updates dynamically based on the current or forecasted audience mix
  • Creative rotation is managed in real time to match shopper segments

With Footprints AI, media dynamically adapts across three channels in real time:

  • In-store digital screens
  • In-store radio
  • Companion mobile apps used during the shopping journey

But dynamic delivery starts with Relevance Scoring, our AI-driven decision layer that connects behavioral intelligence to campaign logic.

Relevance Score: The Brain Behind the Delivery

Footprints AI combines insights from:

  • In-Store Behavioral-Based Profiling
  • Life Stage segmentation
  • Real-time in-store analytics
  • Location and mission-specific media audiences

And calculates a Relevance Score in real time for each ad:

  • Who is currently in proximity?
  • What do we know about their behavior and intent?
  • Which campaign asset has the highest predicted match?

Based on this, the system selects:

  • The right creative
  • On the right channel (screen, audio, or mobile app)
  • At the right moment
Shopper cohort detected near snacks: High Relevance Score for Family Bundles = screen + radio promo. Shopper near beauty aisle with past app usage = trigger mobile push for cosmetics loyalty offer.

Dynamic Digital Screens

Screen playlists adapt based on:

  • The audiences currently in front of the screen
  • Predicted high-affinity audience patterns for the current hour
  • Mission and purchase intent inferred by aisle

This enables:

  • Creative rotation tied to real audiences
  • Screen zones that respond to second-by-second shopper behavior
  • Campaign frequency optimization to reduce waste and boost ROI

Dynamic In-Store Radio

Footprints AI uses real-time audience data to adapt audio ad delivery:

  • Predicts which segments are currently present in the store
  • Optimizes audio ad timing based on profile mix
  • Skips irrelevant messages and boosts delivery of high-match content

Dynamic In-App Experiences

For shoppers using your mobile app in-store:

  • Footprints AI detects their in-store presence via geofencing and indoor positioning
  • Delivers personalized, time-sensitive offers on their device
  • Syncs mobile content with the screen and shelf experience in real-time

This isn’t a fixed playlist. It’s a predictive, intelligent retail media network. More premium inventory created dynamically to get you out of the deprecated time-based ad selling model.

Girl in futuristic supermarket

In-Store Media Measurement

Performance media is only as powerful as its measurement. Brands want proof — not just reach, but impact. They want to pay for performance, not for promises. That’s how retail media becomes accountable. That’s how budgets become dynamic. That’s how scale becomes sustainable.

Every impression matters. And we measure every one.

With Footprints AI, you get full proof of delivery, reach, and impressions for both visual and audio retail media campaigns that were delivered in your store — grounded in behavioral signals, not estimates.

Visual Media (Digital Screens, Companion Mobile App)

  • Reach = how many unique shoppers, identified as audience profiles, entered the screen’s view zone (based on Viewability and Capture rate)
  • Impressions = total number of exposures per campaign, per screen, per second
  • Exposure Quality = dwell time, view angle, zone classification, time-on-screen
  • App Sync = Companion app media tracking for multi-surface exposure

Audio Media (In-Store Radio)

  • Reach = count of total number of unique shoppers, identified as in-store audience profiles, present during audio ad playback within the full perimeter of the audio signal.
  • Impressions = estimated delivery via acoustic zone coverage and profile overlap.
  • Timing Matching = correlates dwell time + motion of people within store with audio delivery schedules.
Want to know if a breakfast ad aired when our "Morning Replenishment Shoppers" were in-store? We can tell you — down to the screen, second, and aisle.

Curious if a radio spot moved shoppers toward beverages? We track their motion path, pause zones, and eventual product choices just after they heard a certain radio ad.

This is media you can trust — with proof of play, audience-level exposure, and media ROI that justifies every retail media campaign.

Family of 4 in the supermarket

In-Store Media-to-Sales Attribution

Attribution inside a store is hard. We made it simple. Footprints AI tracks a shopper’s journey from store entry to checkout - mapping:

  • Media exposure zones
  • SKU interaction zones
  • Product selection behavior

Then we connect that journey to sales. Not just estimates - real, incremental lift compared to non-exposed shoppers. Our model doesn’t rely on black-box panels or extrapolated intent. It uses deterministic, path-based attribution grounded in:

  • Behavioral presence and zone matching
  • Time-stamped exposure to retail media ads
  • Actual scanned purchases at checkout

What powers our attribution engine:

  • Store- and SKU-level sales forecasting
  • Real-time behavioral path tracking
  • Creative exposure timestamping
  • Predictive modeling for baseline sales lift
  • Cohort comparison between exposed and unexposed shoppers

It answers real business questions to your media client – the brand:

  • Which campaign increased purchase to my product relative to the entire category?
  • Did this specific screen ad or radio message move more product?
  • What’s the true ROI of retail media campaign versus the incremental sales uplift?

And it delivers:

  • Uplift in sales by SKU, store, time slot, and creative
  • Attribution at the shopper level (anonymous but behaviorally matched)
  • Media mix performance across in-store screen, audio, and app touchpoints
  • Clear causality from media exposure → behavioral shift → product scan
Shopper enters at 5:12 PM. Walks by beverage zone. Sees digital ad of screens for your zero-sugar cola. Adds it to cart at 5:17 PM. We tracked it. We matched it.

We even detect delayed impact - when a shopper sees an ad, skips the product, then returns days later and buys after another exposure.

This is attribution that closes the loop. It empowers dynamic campaign optimization. It enables performance-based retail media that scales.

Wide shot of big supermarket