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.
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.
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.
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.
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.
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.
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.
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.
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.
"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
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.
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.
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
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
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
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)
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
Young Single
Budget brands, frozen meals, energy drinks, convenience-first products
2x/week (weekday evenings)
Frozen meals, snacks, basic personal care
Quick meals, digital engagement, plant-based interest
Female, 24–28, lives alone/with roommates, urban center
Budget banking apps, mobility subscriptions, early-career insurance
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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
Young Professional
Health-forward snacks, eco-friendly cleaning, cosmetics
3–5x/week (morning + evening)
Functional foods, cosmetics, light dinners
Wellness, ethical products, grab-and-go routines
Female, 27–30, urban, mid-to-upper income
Wellness apps, travel, career finance tools
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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
Young Couple
Premium, experiential grocery
1–2x/week (Sat + midweek)
Fresh food, imported sauces, gourmet bundles
Cooking, exploration, shared experiences
Couple, 30–35, dual income, mid-high income
Joint accounts, travel insurance, premium vehicles
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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
Full Nest I
Family-friendly formats, nutritional kids’ food
Weekly + emergency top-ups
Baby care, produce, family frozen meals
Health for kids, bundled promotions
Parent 30–35, suburban, mid-income
Family insurance, health care, savings plans
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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
Full Nest II
Branded snacks, family-sized goods
2x/week (Sun + midweek)
Pantry, meat, snack categories
School-driven patterns, energy products
Family with kids 8–14, suburban, budget conscious
Auto insurance, school banking tools
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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
Full Nest III
Convenience, tech-savvy products, snacks
2x/week
Bulk frozen meals, beverages
Teen influence, digital lifestyle
Parents 45–50, suburban, 2+ teenagers
Teen insurance, higher education finance
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
Full Single Parent
Budget, efficiency, child nutrition
2x/week
Diapers, snacks, frozen meals
Time-saving products, reward bundles
Female, 30s, urban/suburban, limited income
Micro-loans, renter’s insurance
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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
Mature Single
Artisan, organic, health-enhancing
2x/week
High-quality, functional items
Solo lifestyle, cooking, premium discovery
Single, 45–50, urban, mid-high income
Micro-Boutique health, solo travel, wealth managementloans, renter’s insurance
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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
DINKs
Premium, ethical, cross-category
1–2x/week
High-end food, pet care, skincare
Lifestyle-led bundles, experience focus
Couple, 38–45, urban, high income
Luxury travel, smart investment, EV leasing
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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
Empty Nest
Health-forward, light indulgence
2x/week
Wellness food, light gourmet
Health aging, slow food, weekend leisure
Couple 55–65, suburban/urban, upper-mid income
Retirement plans, cultural travel
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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
Active Senior
Functional foods, simple formats
2x/week
Digestive health, light meals
Community, wellness, light indulgence
Female, 65+, retired, digitally cautious
Pension planning, curated senior travel
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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
Solitary Survivor
Familiar brands, easy-to-prepare food
2x/week (mid-morning)
Small portions, senior care items
Simplicity, routine, nostalgic brands
Female, 75+, widowed, fixed income
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
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
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
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.
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.
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.
