AI-powered experiential displays turn stores into measurable, optimisable sales channels. Learn what tech to use, how to run pilots, and what to demand from partners.
AI-Powered In-Store Displays That Actually Sell More
Most retailers already know static signage is fading fast. The more interesting shift is why: stores are turning into data engines. When your displays can sense what shoppers touch, react to what they browse, and learn what converts, the shop floor starts to look a lot like your e-commerce stack—just with real people, real products, and real-time intent.
This is especially timely heading into late December, when Irish retailers are balancing post-Christmas footfall, gift returns, and January promo planning. Store teams need fewer “pretty campaigns” and more measurable, adaptable experiences. Data-driven experiential displays—powered by AI where it counts—are one of the cleanest ways to get there.
The National Retail Federation has projected that 47% of store executions will soon include some form of digital. That’s not a niche upgrade; it’s a new baseline. What separates winners from expensive experiments is whether the signage is connected to customer behaviour analysis, personalization, and the rest of your omnichannel strategy.
Data-driven experiential displays: what they change in a store
Answer first: Data-driven experiential displays turn in-store marketing from a one-way broadcast into a feedback loop you can optimize—similar to how you tune product pages and ads online.
Traditional POS signage is a dead end: you print it, ship it, install it, and hope it works. Digital and interactive displays behave differently. They can be updated centrally, scheduled by store or region, and—most importantly—measured.
Here’s the commercial context that makes this worth doing:
- 82% of purchase decisions happen in-store.
- 62% of shoppers make impulse buys.
When most decisions are made at the shelf, the shelf becomes your highest-stakes “landing page.” And unlike a landing page, you can’t A/B test a cardboard stand in 40 locations by Friday. Digital displays can.
The metric retailers should obsess over: “intent moments”
If you only track impressions (“people walked by”), you’ll optimise for noise. The better unit is what I call an intent moment: a shopper stops, touches, lifts, compares, or dwells.
Interactive displays—especially lift-and-learn setups—create measurable intent moments. That data helps you answer practical questions store teams argue about every week:
- Which SKU gets picked up most but doesn’t convert?
- Which message drives people to ask staff for help?
- What time of day needs a different offer?
- Which store layout increases dwell time on your hero products?
This is where AI in retail and e-commerce stops being a buzzword and starts being operational. Your store becomes an environment you can tune based on behaviour, not gut feel.
Where AI fits: personalization without making it creepy
Answer first: AI makes experiential displays more profitable by deciding what to show and when to show it based on real-time context—without needing to know a shopper’s name.
A lot of retailers hear “personalization” and immediately think of loyalty IDs, apps, or facial recognition. You don’t need any of that to get most of the value.
The simplest, highest-ROI form of AI-driven personalization is contextual decisioning, such as:
- Time-based: commuter hours vs. lunchtime vs. evening browsing
- Store-based: city centre vs. retail park vs. tourist-heavy locations
- Inventory-based: push items you actually have in-stock today
- Weather-based: cold snap? rotate to winter essentials or hot drinks
When AI and customer data are layered into display systems, conversion rates can reach up to 2.8x higher than static signage (as reported in the source article). The reason is straightforward: static signage is built for an “average shopper.” Your store doesn’t have an average shopper. It has situations.
A practical example: returns season in late December
Post-Christmas, shoppers aren’t browsing like they did in November. They’re returning, exchanging, spending vouchers, and hunting promotions.
A smart display strategy in this period might look like:
- At the returns desk: dynamic messaging that routes customers to “exchange-and-save” bundles
- Near gifting categories: “voucher-friendly” recommendations with price anchors
- In high-traffic aisles: short, silent video loops that explain product benefits in under 8 seconds
AI helps by prioritizing which creative runs where, based on what’s working that day—then feeding those learnings back into January campaigns.
Three display technologies worth betting on (and what to use them for)
Answer first: The best experiential tech is the kind that either pulls people into the store, helps them choose faster at the shelf, or captures interaction data you can act on.
Below are three options highlighted in the source content, with a more tactical “use-case first” lens.
1) Transparent Mesh LED for windows that pull people inside
Transparent mesh LED is built for one job: earning footfall.
The article cites that 80% of shoppers step inside after seeing a digital sign. Even if the real number varies by location, the directional truth holds: motion and brightness in a window outperform a printed poster.
What I like about transparent mesh LED is that it keeps products visible (the source cites up to 72% visibility). So you can run animated storytelling without turning your window into a blackout screen.
Best use cases:
- Flagship storefronts on busy streets
- Seasonal campaigns where you need fast creative swaps
- “Proof” moments: reviews, awards, limited drops, event reminders
AI angle: Use location and time-of-day models to rotate content (e.g., commuter messaging before 10am, gifting bundles after 4pm).
2) Transparent OLED for premium, high-consideration products
Transparent OLED works when shoppers want confidence. The source notes retailers have seen sales lift up to 33% compared to traditional signage.
That lift makes sense in categories where customers want to compare features, see a demo, or understand craftsmanship. Think beauty, electronics, luxury accessories, or even specialty homewares.
Best use cases:
- “Compare models” experiences at the shelf
- Guided demos without needing staff every time
- Premium storytelling where design matters
AI angle: Pair with a recommendation engine that uses local best-sellers and basket affinity (“people who buy X often buy Y”)—the same logic you’d use online, but delivered at the shelf.
3) Lift & Learn for measurable engagement (and smarter merchandising)
Lift-and-learn is the clearest bridge between physical retail and AI-driven e-commerce.
A shopper lifts a product. A nearby screen responds instantly with the exact content for that item. Each lift can be logged as an interaction event, and those events can be tied to sales to understand what engagement actually means.
The source also notes digital displays can draw 400% more views than static signage. If you can prove attention, you can justify spend—and even sell that attention.
Best use cases:
- New product discovery zones
- “Build-your-routine” setups (beauty, health, coffee, kitchen tools)
- Staff-light environments where self-education boosts conversion
AI angle: Use interaction data to:
- identify “high interest, low conversion” products (fix pricing, messaging, or placement)
- train content selection (which video length converts best?)
- personalize flows by daypart (quick specs during rush hours; deeper storytelling on weekends)
The operational side: CMS + analytics is the real product
Answer first: Hardware gets attention, but content operations determine whether digital signage becomes a profit centre or a maintenance headache.
Retailers tend to overspend on screens and underspend on the boring parts: a content management system (CMS), governance, measurement, and a repeatable testing cadence.
The source points out that nearly 89% of digital signage systems are managed remotely. Remote control isn’t a nice extra—it’s the only way multi-site retail works.
What “good” looks like in practice
If you’re serious about AI in retail and e-commerce, your in-store screens should behave like a performance marketing channel:
- A single content calendar shared across e-commerce, CRM, and stores
- Templates so store teams aren’t reinventing creative every week
- Experiment design (even simple A/B): message A vs message B
- Clear KPIs, such as:
- dwell time
- lift rate (for lift-and-learn)
- conversion rate by displayed message
- average basket size change
- impulse purchase rate
The goal isn’t to track everything. It’s to track the few signals that help you make better decisions next Tuesday.
Choosing a digital signage partner: the checklist I’d actually use
Answer first: Pick partners who can run a program, not just install screens—because the ROI comes from iteration, content, and measurement.
The source article stresses fit, creativity, CMS strength, insights, and scalability. I agree—and I’d tighten it into a decision checklist you can use in procurement.
The 10 questions to ask before you sign
- Can you show examples of multi-location rollouts with ongoing optimisation (not just a launch)?
- What analytics do you provide by default (dwell, interactions, content performance)?
- How do you handle privacy and data governance, especially with sensors?
- Who owns the data: you, them, or both?
- Can the CMS support dayparting, regionalization, and emergency swaps?
- How do you create content: in-house, outsourced, or your team?
- What’s your process for creative testing and performance reporting?
- How do you monitor screen uptime and device health?
- What’s the plan for scaling from 5 stores to 50 without chaos?
- How do you integrate with retail systems (POS, inventory, product feeds) if needed?
If a partner can’t answer these cleanly, you’re likely buying expensive TVs.
A useful litmus test: if a signage vendor can’t talk about measurement and iteration, they’re selling décor.
Next steps: a 30-day plan to prove ROI (without a full rollout)
Answer first: You can validate AI-powered digital signage with a small pilot: two stores, two categories, two KPIs, four creative variants.
Here’s a practical pilot structure that keeps costs and complexity under control:
- Pick one category with real margin and shopper hesitation (beauty, small electronics, premium food).
- Choose two store types (e.g., city centre vs suburban).
- Deploy one interaction model (lift-and-learn or interactive OLED).
- Run four creative variants (two messages Ă— two formats).
- Measure two outcomes:
- conversion rate change for the featured SKUs
- basket attachment rate (how often a related add-on sells)
After 30 days, you’ll know whether this is a scaling opportunity or a “nice-looking” distraction.
Retail is heading toward environments that respond, measure, and adapt—the same direction e-commerce has moved for years. Experiential displays are the physical layer of that strategy, and AI is the decision layer that keeps it profitable.
If you’re building an omnichannel roadmap for 2026, here’s the real question: will your stores behave like static showrooms, or like learning systems that improve every week?