Revealing the Future of Retail: Revolutionizing Insights Live from NRF

Fit technology, digital signage and store analytics share a practical challenge: turning data into an experience that works for shoppers and associates. In this Retail Refined report from NRF 2023, Melissa Gonzalez and a MarketScale colleague compare show-floor conversations about the relationship between physical and digital retail.

Their discussion is interspersed with exhibitor demonstrations and interviews. The episode covers footwear recommendations, content on store screens and computer vision that distinguishes associates from customers. Across those examples, Gonzalez emphasizes the full customer journey and the partnerships needed to connect separate retail tools.

NRF’s “breakthrough” shows up as a return to the whole journey

Gonzalez describes an arc she felt on the floor: conversation once centered on balancing physical and digital, then swung heavily toward digital, sometimes at the expense of the in-store experience. At NRF 2023, she sees the balance returning—more talk about the customer “as a whole” across online and offline, and more attention on fixing pain points across the complete journey rather than optimizing one touchpoint in isolation.

That holistic view also shapes her reaction to last year’s metaverse-heavy predictions. She says she was fatigued by how definitive those claims sounded, and she appreciates that the conversation now acknowledges multiple connected experiences without treating any single channel as the only future. Her emphasis is that customers will move across touchpoints fluidly, so retail planning has to treat it as one journey.

Fit technology: from skepticism to data-driven confidence-building

When her colleague asks what has been most impactful, Gonzalez quickly points to fit technology as an area that has long been promised but historically underdelivered because shoppers didn’t trust the data. This year, she senses movement: companies have more “critical mass” of data, and some approaches are shifting from broad brand matching toward comparing a shopper to a “fit twin”—a way of describing similarity to other bodies and preferences rather than relying on labels alone.

She also ties fit to where it appears in the store journey. Instead of living only online, she notes fit showing up in dressing rooms and even on the sales floor, with footwear as a concrete example. The implication is that fit guidance becomes more useful when it’s embedded where decisions are made—at try-on moments and in-aisle exploration—rather than being a detached pre-shopping feature.

A separate NRF conversation in the episode digs into the mechanics of why footwear fit is hard: the speaker argues that shoes are made differently and that size labels are unreliable even within the same brand, let alone across brands. Their demo flow involves selecting a category (such as road running) and receiving recommendations that can vary by brand—paired with a visualization indicating where a particular option might feel tight or loose.

Connecting fit, personalization and store technology

Gonzalez describes vendors combining capabilities such as style recommendations, fit guidance and clienteling. She is interested in how those partnerships could carry a shopper from discovery through purchase and into later interactions. Her focus is on linking useful capabilities into a coherent experience, rather than treating each store technology demonstration as a separate destination.

Personalization becomes a concrete example through Gonzalez’s NRF experience with PerfectCorp. She describes completing a face scan that produced an immersive skin analysis broken out by facial zones—an experience she found humbling. Her point isn’t novelty; it’s specificity. Two shoppers can appear similar yet require different recommendations for different reasons (she gives hydration as one possible reason), which underscores why insight-level personalization can differentiate the experience when it serves both retailer understanding and consumer guidance.

In-store content and computer vision: making physical spaces measurable again

Her colleague’s own NRF takeaway is that more “back-of-store” exhibitors are visible, shining light on the operational systems that make stores run. Gonzalez connects that operational backbone to flexible fulfillment models such as ship-from-store and buy online, pick up in store, arguing that logistics and back-of-house execution are critical if retailers want those experiences to feel seamless. The implication is that store experience improvements aren’t only front-of-house design—they depend on operational readiness.

Another show-floor segment focuses on digital signage as an in-store content problem, not just a screen problem. The speaker describes clients wanting more interactive content that’s relevant to what the shopper is doing in that moment—for example, pairing a product like a sweater with context about who is wearing it. They also describe using checkout lines as an opportunity: a video wall behind the cashier can surface content and products shoppers may have missed while browsing, potentially prompting add-on consideration.

But they also call out the downside retailers are already living with: stores that invested in signage systems that “don’t work,” resulting in black screens. In that scenario, the screen becomes “dead real estate” and can even degrade the experience rather than upgrade it. Their future-facing challenge is targeting—showing the right content to the right person in the right spot—because what most stores show today is still broad, generalized messaging.

A separate exhibitor conversation centers on computer vision for stores. The speaker describes their company as using machine learning and “assisted learning,” including training the system on an employee uniform so video can distinguish associates from customers. With that distinction, they say the system can measure customer engagement and the associate-to-customer ratio on the floor, and can even flag whether an associate is in an unauthorized area such as a back room.

That speaker frames the value in terms store teams recognize: e-commerce has near-constant data visibility, while physical stores often operate without comparable real-time insight. They argue that computer vision can provide similar data to brick-and-mortar locations and, in doing so, empower teams to understand what’s happening, allocate labor more efficiently, and respond to productivity pressure with better information rather than guesswork. They also note hearing renewed NRF interest in tools that raise awareness and support associates—something they say has been missing amid dot-com focus.