Turn Your Retail Network into a Full-Scale Retail Media Business – In-Store and Online

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 Media & Retail 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

Media & Retail 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

Media & Retail 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

Media & Retail 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.

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 Media & Retail 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 Media & Retail 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

Media & Retail 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

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.

Life stage	Young single
preferences	Budget brands, frozen meals, energy drinks, convenience – first products
Propensity to visit	2*/week (weekend 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 roomates, urban center
Non-grocery potential	Budget banking apps, mobility subscriptions, early-career insurance

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.

Life stage	Young professional
preferences	Health-forward snacks, eco-friendly cleaning, cosmetics
Propensity to visit	3-5*/week (morning+ evenings)
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

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.

Life stage	Young couple
preferences	Premium, experiential grocery
Propensity to visit	1-2*/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

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.

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

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.

Life stage	Full Nest II
preferences	Branded snacks, family-sized goods
Propensity to visit	2*/week (midweek++Sun)
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

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.

Life stage	Full Nest III
preferences	Convenience, tech-savvy products, snacks
Propensity to visit	2*/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

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.

Life stage	Full Single Parent
preferences	Budget, efficiency, child nutrition
Propensity to visit	2*/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

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.

Life stage	Mature Single
preferences	Artisan, organic, health-enhancing
Propensity to visit	2*/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 management loans, renter’s insurance

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.

Life stage	DINKs
preferences	Premium, ethical, cross-category
Propensity to visit	1-2*/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

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.
Media & retail 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.

Indoor Audience Insights

Turn Store Visits into Measurable Media Opportunities
Measure:

Visitor counts
Engagement durations
Conversion funnel metrics

Overlay real-time shopping behavior with media exposures for full-funnel attribution.

Media Audiences Powered by In-Store Behavior

Where others see foot traffic, Media & Retail 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. Media & Retail 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 Media & Retail 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

Build smarter and more Premium media audience – Starting with real- life Behavior.

In-Store Dynamic Media Delivery

Where others see foot traffic, Media & Retail AI sees first-party media audiences.

Most in-store networks play ads on a loop. Media & retail 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 Media & retail 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

Media & retail 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

Media & retail 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:

Media & retail 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 Media & retail 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

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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. Media & retail 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.