RetailNext Blog

Faites un bond en avant avec RetailNext au salon NRF 2027

Ashton Kirsten, Global Brand Manager, RetailNext — Wed, 21 Oct 2026 07:51:27 GMT

Retail rarely waits for anyone. Shopper expectations shift with the seasons, new technology lands faster than most teams can evaluate, and the gap between retailers who adapt and those who hesitate keeps widening.

NRF has captured that pace in its call for the 2027 Big Show: "Fast forward to the future of retail." From 10 to 12 January 2027, more than 41,000 retail professionals from over 100 countries will gather at the Javits Center in New York to explore what comes next, with 175+ sessions and more than 1,000 partners on the Expo floor.

RetailNext will be there, and we're bringing something new: an all-new booth designed from the ground up to be the most interactive and engaging experience we've ever brought to the show. Here's what we're watching in the industry, why it matters for your stores, and what you can expect when you visit us.

Video 👉 How Foot Locker Ties Data To Unforgettable In-Store Moments

Three Shifts Setting The Pace For 2027

NRF's 2027 agenda is built around speed, with tracks such as "Fast forward customer experiences," "Operations at optimal speed," and "Press play on tech and innovation". Three themes stand out for leaders running physical stores.

The store remains retail's main stage. According to the US Census Bureau, e-commerce accounted for 17.1% of US retail sales in Q2 2026. That means more than four in five retail dollars still go to stores. With NRF forecasting 2026 retail sales to grow 4.4% to $5.6 trillion, the opportunity in stores is growing, and so is the cost of not understanding what happens inside them.
AI ambition is outrunning AI proof. In Deloitte's 2026 survey of 200 retail and CPG executives, 75% called AI a top strategic priority, yet only 16.5% could quantify a return. The next phase of AI in retail will belong to tools that connect directly to measurable outcomes, not experiments that stall in a pilot.
Operations have to move at the speed of the shopper. Staffing, merchandising, and loss prevention decisions can no longer wait for a monthly report. Retailers need to see what is happening across their portfolio now and act before the moment passes.

Innovation That Keeps Pace With Retail

At RetailNext, innovation isn't an annual announcement. It's how we work. Every year we look hard at what our customers need next and build toward it, because the retailers we partner with can't afford to stand still, and neither can we.

That focus shows up across our platform:

Aurora, our AI-powered sensor, captures accurate in-store traffic and shopper behavior, giving teams a trusted foundation for every decision that follows.
Pulse AI turns that data into answers. Store and regional teams can ask questions in plain language and get practical coaching on what to do next, without waiting on an analyst.
Traffic Analytics, Insights and Asset Protection connect the dots across operations, marketing, merchandising and loss prevention, so every team works from the same picture of the store.

Most importantly, the platform delivers results retailers can measure. The Vitamin Shoppe generated $6 million in additional sales in seven months, and Boggi Milano achieved a 40% gain in shopper yield alongside a five-point lift in conversion. Today, more than 600 retailers across over 100 countries rely on RetailNext to understand and improve their physical stores.

In a year when executives are being asked to prove the value of AI, that's the kind of outcome that matters.

Learn More 👉 Our AI Advantage

Introducing Our All-New Booth

This year, we're bringing that same commitment to innovation to the show floor. Our all-new booth at booth #4647 has been designed around one idea: you shouldn't just hear about retail intelligence; you should experience it.

We've made it more interactive and engaging than ever before. Here's what's waiting for you:

See Aurora in action. Watch our AI-powered sensor capture traffic and shopper behavior live, right on the booth, and see how accurate data becomes the starting point for better decisions.
Put Pulse AI to the test. Step up and ask your own questions. See how Pulse AI responds with clear answers and coaching that store teams can act on immediately.
Join a conversation. Our booth sessions bring together RetailNext experts, customers, and industry voices to share what's working in stores today. Check back for the full schedule.
Play and win. We've built in a few surprises, with games and giveaways to make your visit as memorable as it is useful.

Whether you have five minutes between sessions or want a deeper discussion with our team, the new booth is built to give you something worth taking back to your business.

NRF is asking the industry to fast-forward. For us, that means helping retailers move from knowing what happened in their stores to knowing what to do next, and doing it with confidence. We'd love to show you how.

See you in New York.

Keep Reading 👉 Pulse AI: Stop Looking For The Report. Just Ask.

Ce que l'analyse en magasin permet de voir et que les données de vente ne permettent pas de révéler

lesego.makgale@retailnext.net (Lesego Makgale) — Mon, 28 Sep 2026 08:21:12 GMT

In 1943, Abraham Wald was handed a question about bombers returning from missions over Europe. The aircraft were coming home with bullet holes scattered across their wings, fuselage, and tail, and the instinctive conclusion was straightforward: reinforce the areas taking the most damage. Wald, working with the Statistical Research Group at Columbia University, saw that the problem lay not in the analysis but in the sample. The aircraft being examined were the survivors. Any plane hit in a genuinely critical area had never returned to be measured, which meant the damage on the bombers that made it home showed only where an aircraft could be hit and still survive. The undamaged areas were not safe. They were unexamined, because the evidence about them was sitting at the bottom of the English Channel. Wald's work on aircraft survivability, later documented in the Journal of the American Statistical Association, became a classic illustration of what happens when a model is built only from the cases that survived the process being studied.

Retail has a version of the same problem, and it is hiding in plain sight inside every board report. Once a customer completes a transaction, the volume of information a retailer holds about them is extraordinary: the point of sale knows what they bought, what they paid, and when; loyalty data connects that purchase to previous ones; basket analysis reveals which products travel together; and average transaction value, units per transaction, and sales per square foot describe the commercial outcome in detail. Yet the customer who walked in, spent fifteen minutes browsing, and left with nothing disappears from that picture almost entirely, as does the person who looked through the window and kept walking, and the shopper who arrived during a rush, could not find help, and gave up.

The point of sale sees none of this. No transaction means no record. But the business still lost something: a customer who came in and left without buying. They are the planes that never came back.

Your Next Read 👉 Why Traffic Counting Alone Is No Longer Enough

Sales Data Counts Buyers. Traffic Data Counts Shoppers.

Every dataset a store generates is triggered by a purchase. Transaction records, basket composition, loyalty enrollment, and margin mix are all created at the moment someone says yes, which makes them precise descriptions of completed journeys and silent on abandoned ones. Retail has spent decades becoming exceptionally good at measuring successful customer behavior while treating unsuccessful behavior as an absence of data rather than as data in its own right.

NielsenIQ described a version of this problem in August 2026 in a report called The Invisible Shopper: What Retail Data Isn't Telling You, arguing that retail data is very good at answering what shoppers bought from us and close to silent on what shoppers did everywhere else. The in-store version of that blind spot is narrower and more immediate. It is not only that a customer bought elsewhere. It is that they stood inside your store and bought nothing, and no system recorded that they were ever there.

E-commerce never developed that blind spot, because online the denominator is impossible to miss. A digital team can see sessions, product page views, search activity, and cart abandonment, so conversion became a natural operating metric from the beginning. A physical store has always had exactly the same denominator walking through its doors. It simply has not always had a reliable way to measure it. That single difference explains far more than it appears to, because a business that can only count outcomes can only grow by producing more outcomes. It opens more doors, extends more hours, carries more inventory, and schedules more staff, because you cannot optimize a rate you have never computed.

This is precisely the gap Traffic Analytics is built to close. By measuring who passes the storefront, who enters, how many people are inside, and how those movements track against sales, it gives the transaction a denominator and turns store performance from a total into a rate that can actually be managed.

Revenue Rankings Measure Location, Not Store Performance

RetailNext's own data from Black Friday weekend 2025 shows how far apart traffic and sales can travel. Across the Friday to Monday period, store traffic fell 5.8% year over year and net sales fell 4.4%, which on its own reads as a straightforward decline. Yet average unit retail rose 4.4%, average transaction value rose 2.1%, and shopper yield, the average sale generated per person entering the store, edged up 0.3%. Fewer people came, and retailers extracted slightly more from each one who did. A revenue figure on its own cannot separate a demand problem from an execution problem, and those two call for opposite responses.

The same ambiguity sits inside every store ranking. Consider two stores: the first turns over $2.1M on 180,000 visits, the second $1.4M on 85,000. On revenue alone, the first is the flagship and the second is the problem, and the promotion and the performance plan get handed out accordingly. Divide by traffic, though, and the first extracts $11.67 from every person who enters while the second extracts $16.47, which is 41% more from each customer received. The quieter store is not the weaker operator. It is the better one, working a weaker trade area. Rank a portfolio by revenue and you learn which stores have the best locations. Rank it by how much of the available opportunity each captures, and you learn which stores are being run well.

That second ranking is only possible once traffic is measured consistently across every location, which is what the Benchmarks Hub reports from actual sensor data across thousands of stores each month rather than from surveys or modeled estimates.

Knowing that a store is converting below its potential is the beginning of the question rather than the end of it, which is where Insights takes over. Full path analytics, dwell, and heat mapping show where inside the store shoppers are engaging and where they are disengaging, so a conversion gap can be traced to the zone, display, or journey actually causing it.

Learn More 👉 How Traffic Data Accuracy Affects Labor Costs And Conversion

Only 36% Of Store Associates Say Staffing Matches Actual Traffic

In a March 2025 survey of 500 frontline retail associates across the United States, only 36% said staffing schedules consistently align with actual store traffic. More than half, 51%, reported being short-staffed during busy periods most of the time. Nearly three-quarters, 73%, had watched customers leave because nobody was available to help them, and 77% said their store regularly loses sales because of scheduling decisions. A parallel survey of 500 UK associates in July 2025 found 84% observing lost sales from poor shift scheduling, while 68% also saw overstaffing during slow periods. The problem is rarely too few people in total. It is people scheduled against the wrong hours.

That gap exists because most schedules are built from last year's sales rather than from this month's expected demand, and sales only tell you when people bought, never when they arrived. Knowing how many people visited yesterday explains yesterday. Knowing when demand will arrive changes what happens next month. RetailNext's predicted traffic forecasts demand in 15-minute intervals up to 45 days ahead using each store's own history, which puts expected traffic in front of the labor plan while the schedule is still being built rather than after it has been locked.

Forecasting only matters, of course, if the underlying count can be trusted, and traffic is rarely an endpoint. It becomes the denominator in conversion, the basis for staffing, the reference for store comparison, and an input into marketing and store planning decisions, which means a small measurement error does not stay small. It travels through the system and changes the conclusion at the other end. That is why every Aurora® sensor installation is audited against blurred video from a randomly selected date and time before its counts are used at all, reconfigured and re-audited until it clears the threshold, and re-verified annually thereafter. At the point where traffic data determines whether a store appears to have a conversion problem, or whether labor should be moved, it has stopped being a metric and become infrastructure. Infrastructure has to be trusted.

Once it is, the questions come faster than dashboards can answer them, which is what Pulse AI exists for. A regional manager who sees conversion slipping can ask which store is driving it, whether traffic changed there, and how staffing compared with last month, in plain language and in sequence, without building a report between each question.

Measuring Store Traffic Reveals The Customers You Already Have

UNTUCKit is a useful illustration of what changes, precisely because the company began online and already knew how to run a rate business. Its digital operation could see the whole journey toward a purchase. Its stores could see only the transaction at the end of one. Once store traffic was measured and connected to conversion, the company could compare traffic patterns against sales, evaluate each location against the number of shoppers it was actually receiving, and make operational decisions on something better than a final transaction count, producing a reported 6.9% increase in conversion, an 18% increase in shopper yield, and a 40% reduction in IT infrastructure costs. The UNTUCKit customer story matters because the technology did not create that traffic. The customers were already arriving. The measurement simply made their behavior visible.

This is ultimately what Wald's example has to do with retail. The problem was never that the available data was inaccurate. It was that the process generating the data determined what could be seen at all. A transaction database describes customers who bought with real precision, and explains customers who considered buying, entered and left, or approached the storefront and kept walking, not at all. Those customers have not stopped being commercially relevant. They have only become analytically invisible, and a shopper you cannot see is an opportunity you cannot assign, measure, or reward anyone for capturing.

So the most useful question is probably no longer how many people came into the store. It is what happened to everyone who came close to buying and did not. Once a retailer can answer that, traffic analytics stops being a counting exercise and becomes a way of recovering the part of the customer journey that transaction data erased.

Keep Reading 👉 Why Enterprise Retailers Choose RetailNext

RetailNext et Pine59 redéfinissent l'intelligence commerciale

RetailNext — Thu, 03 Sep 2026 07:35:31 GMT

Alexei Agratchev, CEO of RetailNext, and Thomas Walle, Partner and Co-founder of Pine59, have known each other for years. Their relationship dates back to Pine59's (then ‘Unacast’) early days, when Alexei invited Thomas to RetailNext's Executive Forum to share his vision for location intelligence with a room of retail leaders. That early conviction has since grown into a partnership between the two companies, and now, a product integration.

RetailNext and Pine59 are bringing together in-store and beyond-the-store retail intelligence into a single, connected view. For retailers, that means understanding not just what happens once a customer walks through the door, but what draws them there in the first place, and how outside market conditions shape in-store performance.

Alexei sat down with Thomas to talk about how this partnership came together, why the timing matters now, and what it means for retailers trying to make sense of an increasingly complex customer journey.

Stay Connected With Us On LinkedIn 👉 See What We're Up To

Five Minutes With Alexei & Thomas

🔵 Alexei: You and I go back a long way. I remember what this pitch sounded like at Executive Forum, back when it was still just an idea and a few slides. Retail's changed a lot since then. What's changed most about the problem you're solving?

🟢 Thomas: Back then, location intelligence basically meant counting bodies. How many people visited and at what time. What's changed is that retailers stopped asking "how many" and started asking "who, and why." They want to know where a visitor came from, where else they shop, what their demographic profile looks like, and what competition is doing. The bar moved from measurement to explanation. And honestly, retailers got more sophisticated faster than most vendors did; they'd already squeezed everything they could out of their own four walls and hit a ceiling. What they were missing wasn't more data. It was context.

🔵 Alexei: We built RetailNext on the idea that the store tells the whole story. Millions of data points, all from inside four walls. But I keep hearing the same question from retailers: the product's right, the staffing's right, so why is this location still underperforming? Why do they need to see what's happening outside the store too?

🟢 Thomas: Because in-store data tells you what happened, not why. You can see traffic dipped on a Tuesday, conversion slipped in Q2, one location lags another that looks identical on paper. The store can't tell you why, because the answer usually isn't in the store. It's who's in the trade area, where they're going instead, which competitor just opened two miles away, whether the whole catchment softened or just your part of it. When a retailer tells me "the product's right, the staffing's right, and it's still underperforming," that's almost always a signal the explanation lives outside the store. You can't fix what you can't see, and half the picture is beyond the door.

🔵 Alexei: Give me a real example. What decision does a retailer make differently with this combined view that they couldn't make before?

🟢 Thomas: Take an underperforming store. The classic question is: do we fix it or close it? Without outside data, that's a gut call, and usually an expensive one. With the combined view, you can tell the two apart. If foot traffic in the trade area is healthy but your capture rate is low, that's an execution problem - merchandising, staffing, hours - and it's fixable. If the whole catchment is drying up, people leaving the area, a competitor pulling them off, that's a location problem, and no amount of in-store tuning saves it. Same symptom, opposite decision. One keeps a store open and turns it around; the other stops you from pouring money into a location that was never going to work.

Or, say a retailer's deciding where to open next. Their instinct is to build near their best-performing stores, clone the winner. But their in-store data only tells them those stores convert well, not why, or whether that magic repeats somewhere new. With the combined view, they can see what actually makes a location work - the visitor profile in the trade area, how people move through it, and which nearby brands pull the same customer. Then they can go find that pattern somewhere they don't have a store yet. Instead of guessing off last year's sales, they're matching real market DNA. That's the difference between expanding by hope and expanding by evidence, and it's the difference between a new store that ramps in months versus one that takes years, or never does.

🔵 Alexei: There’s no shortage of companies that claim to do location intelligence. Most retailers I talk to are juggling two or three vendors already, and still missing the full picture. What's actually different here?

🟢 Thomas: Most retailers are stitching. One vendor for foot traffic, another for demographics, RetailNext for in-store, and a person whose real job becomes reconciling three datasets that don't agree. You lose time, you lose accuracy at every seam, and you still don't fully trust the answer. What's different is that this partnership between RetailNext and Pine59 is integrated. Outside and inside sit in the same view, calibrated to the same stores, so nobody's exporting CSVs and eyeballing whether the numbers line up. The integration removes the seam, and the seam was where most of the error and most of the wasted time lived.

🔵 Alexei: Let's say a retail exec only has budget for one of these: in-store or beyond-the-store data. Not both. From your perspective, what do they lose by picking just one?

🟢 Thomas: Either way, you're flying with one eye closed. In-store alone, you see execution in perfect detail and miss the market entirely - you'll optimize a store that's underperforming for reasons that might have nothing to do with the store. With beyond-the-store data alone, you understand the market but can't see whether you're actually converting it, so you can't tell a demand problem from a delivery problem. Neither half is wrong. They're just incomplete in opposite directions. The value was never in either dataset on its own. It's in the fact that one explains the other.

🔵 Alexei: Where does this go next? What does the retailer five years from now expect that they don't have today?

🟢 Thomas: Five years out, no retailer's going to accept the customer journey being split in two - outside data in one system, inside data in another, and a human trying to draw the line between them. They'll expect one continuous view, from the parking lot to the checkout counter, where what pulls someone toward a store and what happens once they're inside are read as a single story. That's the direction, and this integration is the first real piece of it. We're connecting the two halves that have always been managed separately, and once retailers see the whole journey in one place, they won't want to go back to guessing at the gap.

An Integration To Achieve The Full Picture Of Store Performance & Potential

The shift Alexei and Thomas describe plays out differently depending on where you sit.

For marketing leaders, it means finally seeing who visits your stores versus your competitors', and building campaigns around the segments you're winning or missing.
For real estate and expansion teams, it means ranking potential markets against your actual shopper profile before committing to a lease.
For operations, it means distinguishing a store-level execution problem from a market-wide shift using real data, rather than a hunch.

The common thread: decisions that once relied on partial information now have the other half of the picture.

What Alexei and Thomas describe isn't necessarily just a bigger dataset... It's a more honest one, one that doesn't ask retailers to choose between understanding their stores and understanding their market. As the integration rolls out, that's the bet both companies are making: that retailers have been operating with half a picture for long enough, and that closing the gap is worth doing together.

La rentrée scolaire est un comportement, pas une date : repenser le calendrier saisonnier

Evan Gold — Tue, 25 Aug 2026 04:00:00 GMT

Many retailers experience a notable increase in store traffic surrounding the first day of school. In preparation for this, retail merchandising calendars often treat back-to-school like a fixed appointment. Planners circle a date — early August in the Sun Belt, late August in the Midwest, after Labor Day in parts of the Northeast — and build promotions, staffing, and inventory flow around it.

But ask any store manager in Dallas why the polo shirts and backpacks moved slower than planned during a week that hit 88°F, or why a "cooler" pocket of weather in Minneapolis pulled fleece and light jackets forward on the calendar, and you'll hear the real story. Back-to-school demand doesn't follow the school bell. It follows the thermometer, or more specifically, how the shopper ‘feels’.

Additionally, shopping is more than just which items are in demand — it's about whether people come into the store at all. A heat wave can shift shoppers from multiple store visits into one heavy trip, timed to the coolest hours. A surprise cold front can pull an entire week of shopping forward before it even arrives. Store traffic and category mix often move together, but not always for the same reason, and a plan built only around what's in the cart misses half of what weather does to the store.

The Calendar Gives You the Week. The Weather Gives You the Week That Matters.

School start dates tell you roughly when a category should begin moving. They don't tell you how fast, which items will move first, or which regions will lag or lead. That gap is expensive: Cold weather apparel and outerwear sit on shelves in a heatwave while beverages, late summer products, and sun-related SKUs outperform. Staffing a store without understanding the weather impact runs the risk of low customer service levels when weather drives increases in foot traffic. Conversely, you run the risk of being overstaffed when weather suppresses traffic during what was supposed to be peak week.

This is where Planalytics' WeatherSmart demand data becomes an essential overlay — it's the layer that converts a static calendar date into a dynamic demand signal. Combined with RetailNext’s real-time traffic data, retailers can capitalize on demand signals that specifically account for how much foot traffic will increase or decrease based on the weather. Aligning this demand signal with operational plans helps to drive sales, increase service levels, and optimize profits.

What WeatherSmart Demand Data Actually Shows

WeatherSmart's Demand Data quantifies how much actual and forecasted weather pulls a category's sales above or below what the calendar alone would predict, expressed as a percentage lift or drag against a baseline of normal seasonal weather or last year's comparable week. It's a translation of temperature, precipitation, and seasonal patterns into a demand adjustment retailers can proactively action.

Consider a real example: In mid-August of last year's back-to-school run, national Children's Apparel store traffic demand ran 3% above "normal" seasonal patterns solely due to the weather, even though nothing about the calendar had changed. Underneath that national number, the regional weather told a more useful story. Children’s apparel store traffic was +19% above normal in Buffalo, +12% in Detroit, +6% in both New York City, and Chicago. Conversely, weather negatively impacted store traffic at Children’s Apparel retailers by -13% below normal in Kansas City, -8% in Salt Lake City, and -4% in Boise. The same week on the retail calendar resulted in dramatically different store traffic impacts.

That's the core insight: the calendar tells you it's back-to-school season, but the weather impacts are the signals to pay attention to across local markets.

Two Signals, Not One: Category Mix and Foot Traffic

It's tempting to treat weather as purely a merchandising question — hot weather sells shorts, cool weather sells jackets. But weather moves two dials at once, and conflating them produces plans that get the trip volume right and the register mix wrong, or the reverse.

Category mix is what people buy once they've decided to shop. Traffic is whether — and when — they show up at all. Extended heat can suppress discretionary trips, compressing shopping into fewer, larger visits timed to the coolest part of the day. A sudden cool break can also trigger a short-term traffic spike, as households who'd been putting off a trip finally go. Rain adds a third pattern: sharp suppression while it's happening (or projected to happen), followed by a rebound surge.

A plan built only on category-level demand can nail the assortment and still misjudge the store traffic impacts. WeatherSmart demand analytics captures both dimensions for the same market and week: how much weather moves the category, and how much it moves the trip itself.

Explore 👉 RetailNext Traffic Analytics

From Insights to Actions: Promotions

Retailers using WeatherSmart demand analytics alongside RetailNext Traffic data can time back-to-school promotional pushes to when heat (or its absence) is actually driving category demand, rather than promoting every market on the same national trigger date.

Front-load promotions into the window when weather-driven trips will happen rather than spreading it evenly. The promotional calendar stops being one national event with regional variations bolted on and becomes a set of regional events that cluster around a similar time of year. For those who operate an online channel, weather can shift physical traffic in a store to digital traffic online.

From Insight to Action: Staffing

Labor is the other half of the equation, and often the more expensive one to get wrong. Back-to-school staffing has traditionally been scheduled off historical patterns tied to school calendars. WeatherSmart demand signals let planners adjust that baseline before it becomes a problem on the floor:

Anticipate weather-driven traffic surges. Extreme weather can compress shopping into fewer, more intense trips. WeatherSmart demand flags when a market will see concentrated rather than steady traffic, so schedules shift toward fewer, better-staffed shifts at the hours people will actually come in.
Prepare for the weather-driven rebounds. When weather normalizes, stores often see a sharp rebound as deferred trips land at once. Plan for extra coverage to meet increases in traffic following a lull.
Coordinate replenishment accordingly. Look out longer term. If weather is going to pull foot traffic and product demand forward in a region or market, store labor and replenishment plans can proactively plan for this spike instead of reacting to a stockout.

The Bigger Shift

None of this replaces the school calendar — it still tells you the season is coming. What WeatherSmart Demand analytics adds is precision about when, where, and how strongly that season shows up. This demand signal helps understand what people will buy, how much more (or less) will be purchased, and whether they show up to your store to buy it. A single national plan off one calendar date is a guess dressed up as a schedule. Layering in signals like WeatherSmart demand analytics turns it into a rolling, highly localized forecast that sharpens as the season gets closer.

The school bell rings on the same date every year. The weather doesn't. Retailers who plan for both walk into back-to-school with the right product, in the right place, staffed by the right people at the right hours -- all aligned with the right weather!

Learn more about Planalytics 👉 Measuring The Weather's Impact

RetailNext Point de vue du directeur marketing : les deux prochaines années de l'analyse des données dans le secteur de la distribution

Sergio Gutierrez, CRO, RetailNext — Fri, 21 Aug 2026 08:00:00 GMT

Earlier this year, we hosted our annual Executive Forum. Close to 100 retail leaders spent three days with us, spanning breakouts, dinners, and unscheduled hallway conversations. One conversation came up in every one of those settings. It started with AI. It pivoted, almost always, to a harder question: what does any of this mean for the analytics infrastructure we already have?

Watch The Session 👉 How Foot Locker Is Using Store Data To Drive Conversion

The Conversation Retail Leaders Are Actually Having

The board-level AI conversation in retail has matured fast. Two years ago it was, "should we have an AI strategy?" One year ago it was, "where are we piloting?" Today it is, "which parts of our operating stack hold up under what we are trying to do, and which parts do not?"

That last conversation is harder than the first two. It forces leaders to look at infrastructure they bought years ago, often through procurement, and ask whether it is still fit for purpose. For most physical retailers, traffic and store analytics are exactly the kind of infrastructure that sits in that category. Bought once. Largely working. Quietly limiting.

The money tells you how serious this has gotten. Gartner forecasts that global AI spending will hit $2.52 trillion in 2026, a 44% year-over-year increase. IBM surveyed 1,500 global retail and consumer products executives and found that 81% are using AI to a moderate or significant extent, with their internal teams running even further ahead. These are not pilots anymore. These are operating budgets.

Why Legacy Counting Infrastructure Cannot Carry It

The legacy traffic counters that have anchored this category for more than three decades were built to answer one question: how many people walked in the door? At the time, that was useful. Conversion rates, staffing ratios, and hourly trends gave operators a real edge.

But the ceiling of that architecture is hard. Hardware that reports a number cannot become software that learns from patterns. AI capabilities bolted onto a counting platform are a structural mismatch, not a product update. The data foundation is not there.

The retailers who recognize this early will modernize their analytics infrastructure as part of their AI strategy, not after it. The ones who do not will spend the next two years pouring AI investment into a foundation that cannot support the weight.

I have watched this pattern play out in adjacent categories. The companies that get to AI native operations first establish a structural advantage. The companies that retrofit AI onto legacy stacks spend more, move slower, and produce thinner results. The gap is real, and it widens fast.

There is a deeper question behind all of this. The analytics platform a retailer chooses for the next decade needs to support AI use cases that have not been invented yet. The only way to deliver that flexibility is to architect machine learning into the platform from the early years, not bolt it on once the market demands it. Retailers should be asking their providers a different question. Not just what does the platform do today. But rather: How was it built? Legacy traffic counters were not built for this. The platforms that win the next ten years will be.

What AI-Native Operations Actually Look Like

Most of the category is still selling and buying weekly reports. A smaller set of retailers has already moved past that. Here is what that move looks like in practice.

A US department store has RetailNext device health signals auto-feeding ServiceNow, so incident tickets open and close without human intervention. Operational alerts that used to require someone to notice and route are now handled by the workflow itself.
A Canadian premium apparel retailer is building its own analytical models on top of RetailNext data and distributing the outputs across digital, marketing, real estate, and operations. The platform is the foundation. The intelligence layer they have built sits above it and crosses every function in the business.
A US athletic retailer is using AI to generate tasks. When a metric is off in a given store, the system flags it, generates the action for the district manager to take, and tracks whether it gets resolved. The team is treating store performance as a continuous closed loop, not a weekly readout.
A UK specialty athletic retailer is replacing a quarterly manual labor rota process, run by one person across hundreds of stores, with continuously updated, predicted traffic-driven labor allocation. The planning cadence is moving from once a quarter to once a week.
A US home furnishings retailer is replacing an inherited people counting system with a live API feed and AI-assisted forecasting, with their internal analysts brought directly onto the platform.

Five different retailers. Five different categories. The pattern is consistent. Continuous instead of weekly. Integrated instead of isolated. Forward-looking instead of backward-looking. The retailers operating this way are not waiting for the category to catch up. They are pulling ahead while their competitors are still in evaluation cycles.

What The Next 24 Months Will Look Like

Three things will be true two years from now that are only partially true today.

Legacy traffic counting infrastructure will be visibly obsolete at the enterprise tier. The retailers who have not modernized by then will be operating with categorically less information than the ones who have. Not slightly less. Categorically less.
AI-native platforms will have moved from a competitive advantage to a baseline expectation. The retailers who built early will compound the lead. The retailers who waited will spend the next two years catching up to a position the leaders are already moving on from.
The cost of getting this wrong will become visible in financial results. Labor productivity, conversion performance, real estate decisions, marketing ROI. The gap between AI-native operators and everyone else will be measurable on the P&L. That is the moment when most boards stop debating the timeline.

The Decision That Cannot Wait

If you are a retail leader reading this, the question to take into your next strategy review is not "are we doing enough on AI?" That question is too abstract to be useful. The question is: does the analytics infrastructure we have today support the operating model we want to be running in two years?

If the answer is yes, you are ahead of most of the category. If the answer is no, the cost of waiting compounds every quarter. The retailers moving fastest right now have already decided.

The category is not standing still. The question is whether your infrastructure is moving with it.

Another Session? 👉 Kendra Scott & Marine Layer Are Turning Stores Into Community Hubs

Pulse AI : Ne cherchez plus le rapport. Demandez-le simplement.

RetailNext — Thu, 20 Aug 2026 07:25:18 GMT

Pulse AI is a conversational layer built into the RetailNext platform. Here is what it does, where it lives, and what changes when anyone on your team can question their own store data in plain language.

Most retail teams are not short of data. They are short of time between the question and the answer.

Consider a district manager who notices that conversion slipped across four stores last week. Everything needed to explain it already exists in the platform. Getting to it means remembering which report holds the answer, setting the date range, filtering to the right locations, then comparing it against the weeks on either side. That is a few minutes for someone who uses RetailNext every day. For someone who does not, it is a message to the analytics team and a wait.

Multiply that by every question a retail organization asks in a week, and the real cost becomes clear. It is not missing data. It is decisions that arrive later than they needed to, and the questions nobody bothers to ask at all because the effort outweighs the curiosity.

Pulse AI closes that gap. It is a conversational layer built into the RetailNext platform. Someone asks a question in ordinary language, in the language they work in, and gets an answer in seconds, drawn from the same measured data that sits behind their dashboards. No report to locate. No training required. No queue.

Pulse AI Sits Where The Question Actually Occurs

The most useful thing about Pulse AI is not that it can answer a question… Rather, it’s where it can answer one.

Pulse AI is now available across effectively the whole platform: Reports, Video, Security Events, Point of Sale, every dashboard, and every Admin page. The Benchmarks Dashboard is the one remaining exception, and it is coming shortly.

That matters more than it might sound. Questions do not arrive at a convenient moment on a convenient screen. They arrive while someone is already looking at something and thinking, "that does not look right." Pulse AI reads the page you are on, so a question as vague as "why did this drop?" is answered in the context of what is currently in front of you. The thought does not have to travel to another part of the platform to become an answer.

What That Looks Like In Practice

A store manager, mid-shift. Traffic looks healthy but the day feels quiet. Instead of waiting for tomorrow's report, they ask how conversion is tracking against the same day last week, and get the comparison in seconds. If it is down, they can act while the shift is still running.
A regional lead, preparing for a Monday call. Rather than pulling each location individually, they ask which stores in their region underperformed on conversion last week and by how much. The comparison arrives already assembled, and the twenty minutes that would have gone into building it goes into deciding what to do about it.
An operations manager who does not live in the platform. They need a weekly performance summary sent to eight people every Monday. They ask how to set it up and get step-by-step guidance drawn from the official RetailNext Knowledge Base, with a link to the underlying article. No ticket raised, no waiting for a reply.

That last example is worth dwelling on, because Pulse AI does two distinct jobs. It answers questions about your performance and about the product itself: how to configure something, where a setting lives, and why a feature behaves the way it does. Setup and troubleshooting questions are answered from our official documentation, with a link to the source in the response, so anyone can check the full article if they want the details.

Answers You Can Act On

An assistant is only useful if the people using it trust what comes back. Three things make that possible.

Pulse AI answers from your own measured data, not from inference. It queries your metrics through RetailNext APIs and reports what was actually recorded in your stores. Where the data does not support an answer, it says so rather than filling the gap.
It respects the permissions your teams already have. Pulse AI uses each person's own access rights, so it cannot show anyone something they were not already able to see. If a user has no access to point-of-sale data in the platform, Pulse AI has none on their behalf.
We are direct about how it works. Pulse AI is powered by Anthropic. What reaches the model is the aggregated, dashboard-level metrics that already appear in your reports, not raw or sensitive business data. We do not permit our AI providers to train their models on customer data without explicit permission. Like any AI assistant, Pulse AI can be imperfect, so a reminder to verify sits in the panel, and every answer can be rated, which is how we know what to improve next.

The Quieter Benefit: More People Asking

The obvious gain is speed. The more valuable one is reach. RetailNext has always worked towards being fully self-serve, but self-serve rewards fluency. Those who knew the platform well got the most from it, while store managers, regional leaders, and executives who logged in occasionally got less. Pulse AI removes the fluency requirement. Someone can ask a useful question on their first session, without a training course and without knowing which widget to open.

That is what changes what a team can deliver. Not one person working faster, but far more people able to interrogate their own data at the moment it matters, and to act within the same day rather than the following week. Administrators still decide who has access, so the reach is up to you to set.

Or Bring Your Own AI

Some organizations have already standardized on an AI tool of their own. RetailNext is also available as a Model Context Protocol (MCP) connector, so your data can be securely queried from the environment your team already uses, authenticated against each person's RetailNext login, and scoped to the permissions they already hold. It works independently of Pulse AI and carries no additional cost.

We are straightforward about the trade-off. Through the connector, the reasoning is done by your tool rather than ours. RetailNext provides the secure interface to the data, and the retail judgment that makes Pulse AI behave as an analyst comes from how we have tuned it.

💡 Pulse AI is included in every RetailNext subscription at no additional cost. If your teams have not tried it yet, the fastest way to understand it is to ask it something you have been meaning to look up.

Commerce de détail Asset Protection : réduire les pertes grâce au contexte

lesego.makgale@retailnext.net (Lesego Makgale) — Mon, 17 Aug 2026 10:07:53 GMT

Modern retail asset protection is built around a contradiction.

Retailers can see more of their stores than ever before. Cameras capture activity from multiple angles, transactions are recorded in real time, and customer traffic can be measured with remarkable precision. Yet despite all of that visibility, shrink remains one of the industry's most persistent challenges. According to Appriss Retail's 2026 Total Retail Loss Benchmark Report, U.S. retailers lost $90 billion to shrink in 2025, with an estimated $66 billion considered preventable.

At the same time, operational complexity continues to grow. According to the Self-Checkout Loss Report 2026, shrink rises by an average of 22% in the first year after a retailer introduces self-checkout, while a separate Grabango study found a 3.5% shrink rate at self-checkout compared with 0.21% at staffed lanes.

At first glance, those realities seem difficult to reconcile. If retailers have access to more information than ever before, why does so much preventable loss continue to occur? The answer may have less to do with visibility itself and more to do with what happens after an incident occurs. In many cases, the challenge is not whether a retailer can see what happened. The challenge is where the investigation begins.

The Cost of Starting Too Late

Consider two retailers operating at a similar scale. Both generate approximately $1.5 million in annual sales, both welcome a steady flow of customers throughout the day, and both have invested in many of the tools retailers rely on to understand what is happening across their stores, from video surveillance and transaction data to operational reporting. Then something goes missing.

What happens next is probably familiar to most asset protection professionals. A team begins reviewing footage, retracing timelines, and trying to reconstruct a sequence of events from the evidence available to them. Questions emerge quickly. Was the store unusually busy at the time? Was activity concentrated in a particular area? Was this an isolated incident or part of a broader pattern that had simply gone unnoticed?

None of those questions are unreasonable. In fact, they are often exactly the right questions to ask. The difficulty is that they only come into focus once there is already a reason to investigate.

Using a 1.6% shrink rate as a benchmark, a retailer generating $1.5 million in annual sales could be looking at roughly $24,000 in annual losses. The financial impact is significant, but what often receives less attention is the amount of time required to understand what happened in the first place. By the time an investigation begins, teams are frequently working backward, attempting to piece together a story from the information left behind.

Now imagine approaching the same challenge from a different starting point.

Instead of viewing an incident in isolation, the team can understand it within the broader context of what was happening across the store. They can see how traffic was moving, where activity was concentrated, and when events occurred relative to one another. Rather than beginning with a loss and then searching for context, they begin with context and use it to determine whether further investigation is needed.

That distinction may appear subtle at first, but it fundamentally changes the way an investigation unfolds. It also helps answer three practical questions that sit at the center of most asset protection investigations: What happened? What was happening around it? And where should attention be focused next?

This is the principle behind RetailNext Asset Protection. By bringing together traffic insights and loss prevention workflows, it helps teams understand potential exceptions within the broader context of store activity, making it easier to prioritize investigations and focus resources where they can have the greatest impact. A closer look at how the platform compares to point solutions shows why that combination matters.

Success increasingly depends on understanding the conditions surrounding a potential exception, not simply the exception itself. The better retailers understand what is happening across the store, the better positioned they are to identify issues earlier and respond more effectively.

Your Next Read 👉 RetailNext Asset Protection Solution Comparison

Visibility Is Not the Same as Context

One of the more persistent assumptions in retail asset protection is that more visibility naturally leads to better outcomes. In practice, the relationship is often more complicated.

Most retailers are not operating with a shortage of information. If anything, many are navigating the opposite challenge. Teams already have access to video footage, transaction records, exception reports, and operational data, yet understanding how those signals relate to one another remains a persistent challenge.

An incident rarely exists in isolation. The amount of traffic moving through a store, the area in which an event occurs, and the timing of that activity can all influence how a situation is understood. A transaction that appears ordinary on its own may look very different when viewed alongside unusual traffic patterns or activity concentrated in a specific zone.

This is where context begins to matter. Understanding what happened will always be important. Understanding what was happening around it is often what helps teams determine whether an event deserves closer attention and where they should focus their efforts first.

When traffic insights, operational data, and loss prevention workflows are considered together, investigations become more focused. Teams spend less time determining where to begin and more time evaluating where risk may be emerging.

From Visibility to Prioritization

For many asset protection professionals, the harder task begins after an exception appears: deciding which signals deserve attention first and where limited investigative time will have the greatest impact.

An investigation that begins without context often requires teams to search through hours of footage, review multiple data sources, and identify patterns before they can determine whether they are even looking in the right place. The process can be time-consuming, particularly for organizations managing multiple stores and competing priorities.

When context is available from the outset, the nature of that work changes. Instead of beginning with uncertainty, teams can begin with a clearer understanding of where attention may be needed and why. That shift is not simply about speed, although speed certainly matters.

UNTUCKit was able to save approximately 40% by consolidating separate systems for traffic counting and loss prevention onto a single platform. "We are able to save ~40% versus using separate systems for traffic counting and loss prevention," notes Brent Paulsen, Managing Director and Head of Retail at UNTUCKit.

Retailers using RetailNext Asset Protection can also investigate incidents up to 75% faster, while capabilities such as ranking stores and cashiers by risk and exporting case evidence as watermarked video help teams move from identifying potential issues to taking meaningful action.

Ultimately, the value lies in clarity. When teams spend less time determining where to look, they can spend more time evaluating risk, making decisions, and acting on what they find.

Learn More 👉 RetailNext Customer Success Stories

Why This Matters More Than Ever

The pressures facing asset protection teams today extend beyond shrink alone.

Retail operations are becoming increasingly complex, with self-checkout, changing customer behaviors and evolving store formats introducing new variables into an already challenging environment. Risk is not always concentrated in a single event, department, or process. It can emerge through customer traffic patterns, operational changes, staffing pressures and store layouts that appear unrelated when viewed independently.

As that complexity grows, identifying incidents becomes only one part of the job. Asset protection teams are also expected to recognize which signals warrant attention, prioritize competing risks and determine where limited time and resources will have the greatest impact.

Where You Start Changes Everything

For years, conversations about asset protection have centered on visibility. Better cameras, more footage and greater access to operational data have all helped retailers understand more about what is happening inside their stores. Those investments matter, but visibility alone does not create clarity.

The retailers best positioned to reduce loss are not necessarily the ones with the most information. They are the ones that can place information in context, identify where attention is needed, and make informed, agile decisions about where to focus their efforts.

That shift from visibility to context is where many asset protection teams are now focusing their attention. The ability to investigate incidents faster, prioritize stores and cashiers by risk, and understand exceptions within the broader flow of store activity gives teams a clearer starting point for every investigation.

As retail environments continue to evolve, the question facing asset protection leaders is not whether they can see what happened. The more important question is whether they know where to begin. And where an investigation begins can shape everything that follows.

Explore 👉 RetailNext Asset Protection

Randall Meadows – Réflexions d'experts : La prévention des pertes dans le commerce de détail aujourd'hui

Randall Meadows — Tue, 21 Jul 2026 08:00:27 GMT

Retail loss prevention has never been more complex. Omnichannel fulfillment, rising theft, and pressure to keep the shopping experience frictionless have all converged, forcing loss prevention leaders to rethink strategies that worked even five years ago.

To unpack where the industry stands today, we sat down with Randall Meadows, a loss prevention and security executive whose career spans leadership roles at Kohl's, Nike, Hollywood Entertainment, Stage Stores, and Mervyn's (now Target). Randall has built and scaled loss prevention programs from the ground up, including launching Nike's program from inception, and has consistently delivered shrink results at or below 1%.

In this interview, Randall shares his perspective on how the loss prevention landscape has evolved, what separates top-performing retailers from the rest, where technology like AI and computer vision is genuinely moving the needle, and where the industry still has work to do. He also offers practical advice for loss prevention leaders looking to modernize their programs in today's environment.

Read on for his full take on the current state of play.

The Current State of Retail Loss Prevention

🔵 RetailNext: You've led loss prevention programs across several major retail formats, from mass merchants to specialty apparel. How would you describe the current state of retail loss prevention compared to five or ten years ago? What's changed the most, and what's stayed surprisingly the same?

🟡 Randall: Retail loss prevention is significantly more challenging today than it was five or ten years ago. Key developments such as the rise of gift cards, coupons, omni-channel retail, and third-party resellers—alongside a sharp increase in theft—have complicated the landscape. Furthermore, loss prevention executives are now tasked with addressing these issues while maintaining a frictionless customer experience, often with fewer staff members than in previous years.

Despite these changes, legacy solutions such as EAS security tags and alarm systems have remained largely the same. While somewhat effective, these technologies are lagging and failing to keep pace with today's challenges. I believe these areas are overdue for technological advancement to better support today's loss prevention needs.

DISCOVER 👉 Why You Should Consolidate Your Store's Analytics

Shrink Benchmarks and What "Good" Looks Like

🔵 RetailNext: Industry shrink benchmarks typically range from 2% to 5%, yet you've achieved results at 1% or better. What separates retailers who consistently outperform the industry average from those who struggle to move the needle?

🟡 Randall: It’s been my experience that retailers that consistently deliver industry-leading results share several key drivers.

First, these organizations have top-down support from the C-suite. Senior leadership is aligned on the fact that inventory shrink is a primary indicator of how well a company is managed, rather than just a theft issue. This alignment fosters essential collaboration between internal business partners and solution providers, allowing for a holistic approach to the problem.

Second, successful retailers invest in technology to identify and address root causes. By using tools such as RFID and computer vision, these companies gather data to make informed decisions. Programs that leverage this data into actionable insights consistently outperform their peer groups.

Finally, your associates are the most important piece of the equation. Hiring and retaining top talent is crucial, but industry leaders distinguish themselves by combining that talent with strong executive support and the right technology and tools.

Providing associates with the necessary resources and leadership backing remains the most effective way to outperform the industry.

The Technology Question

🔵 RetailNext: Loss prevention has increasingly become a technology-driven discipline, from source tagging to AI-powered analytics. Where do you see technology genuinely changing outcomes for retailers today, and where do you think it's overhyped relative to fundamentals like training and process?

🟡 Randall: AI-powered analytics is the technology that will redefine outcomes for retailers. As previously discussed, the volume of data retailers must digest is immense; those capable of analyzing and reacting to this information will see meaningful results, and AI is the key to achieving that.

From personal experience with overhyped solutions, I believe many solution providers often oversell their capabilities. For instance, several computer vision AI providers execute specific tasks well but claim to excel at everything. This has unfortunately negatively influenced opinions within the industry when results fall short.

Regarding fundamentals like training and process, these remain core principles that require constant focus. Training and best practices are the necessary starting points and are the foundation; technology such as AI should be viewed as a tool to amplify employee productivity rather than a replacement for these foundations.

Balancing Incident Prevention With Store Experience

🔵 RetailNext: You've spoken about prioritizing employee and customer safety alongside shrink reduction. As retailers face pressure to reduce friction in the shopping experience, how should loss prevention leaders think about the tradeoff between security measures and customer experience?

🟡 Randall: Creating a safe working and shopping environment will always be a priority. I believe the aspirational goal should always be to create a great shopping experience and eliminate as much friction as possible. For loss prevention leaders and senior executives, these decisions should be data-driven to find the balance. The goal is to determine how the company can increase sales and profit, and data will be key to making decisions. For example, should all stores or all products follow the same product protection guidelines?

It’s been my experience that what is necessary in one store may not be applicable to another. In the end, the financial data and customer feedback should guide the decision-making process. I believe that if you take this informed approach, you can strike a balance between reducing friction and maintaining or increasing sales and profits, while creating a safe working and shopping environment.

Where the Biggest Opportunities Are

🔵 RetailNext: Given your work on programs like source tag reuse and return optimization, both of which delivered significant savings, what areas of loss prevention do you think are still underexploited by most retailers? Where is the low-hanging fruit that teams are missing?

🟡 Randall: With the advancements in RFID, AI, and Computer Vision, I believe there are significant opportunities to add value in their respective vertical markets. Each of these technologies can provide actionable insights that improve overall efficiency and effectiveness.

In my view, the most immediate opportunity lies in correlating the data from each of these solutions to drive reliable, targeted action.

It’s important that Loss Prevention teams are consistently evaluating and testing emerging technology.

Advice For the Next Generation of LP Leaders

🔵 RetailNext: You built and scaled programs from the ground up, including launching Nike's loss prevention function from inception. What advice would you give to a loss prevention leader today who's trying to build or modernize a program in the current retail environment?

🟡 Randall: If I were to narrow my advice down to three key tips, they would be the following:

Identify challenges and opportunities: Build both a short-term and long-term roadmap to solve the problem.
Align your organization: Ensure your organizational structure supports your roadmap and strategic goals.
Leverage technology: Consistently identify tools and technologies to support your team and initiatives.

Ultimately, your goal should be to define the objective, establish an organizational structure to address those needs, and provide the tools your team needs to succeed.

YOUR NEXT READ 👉 Learn More About Asset Protection By RetailNext

L'IA agentique dans le commerce de détail : pourquoi les magasins physiques revêtent une importance croissante

Ashton Kirsten, Global Brand Manager, RetailNext — Tue, 14 Jul 2026 13:40:35 GMT

Something quietly historic happened over the 2025 holiday season. According to Salesforce's Cyber Week analysis of more than 1.5 billion shoppers, AI and AI agents influenced 20% of all global orders, driving an estimated $67 billion in sales through personalized recommendations and conversational service. One in five purchases is shaped by software acting on a shopper's behalf.

That figure marks the arrival of what the industry now calls agentic commerce: AI systems that don't just answer questions, but compare options, narrow choices, and increasingly complete purchases for the consumer. In October 2025, Walmart announced a partnership with OpenAI to let customers shop through ChatGPT, with CEO Doug McMillon declaring that the era of the search bar and the long list of results was about to change. Target, Sephora, Nordstrom, Lowe's, Best Buy, The Home Depot and Wayfair have since integrated with OpenAI's Agentic Commerce Protocol. Deloitte's 2026 retail outlook describes AI in commerce as moving from experimentation to execution, and the executives I speak to would agree. This is no longer a pilot program conversation. It is a strategy conversation.

So here is the question every retail leader should be sitting with: if an agent can research, compare and buy on a shopper's behalf, what is the store for?

I'd argue the answer is becoming clearer, and it is the opposite of what the headlines suggest.

OUR BENCHMARKS HUB 👉 Real Monthly Traffic Data From North America, EMEA, and APAC

What The Walmart Pivot Really Tells Us

The most instructive agentic commerce story so far is not a launch. It is a course correction.

By March 2026, OpenAI announced it was rethinking Instant Checkout, acknowledging that the initial version lacked the flexibility it had aspired to, and shifting its focus to product discovery while letting merchants run their own checkout experiences. Walmart, for its part, restructured the partnership to bring its own Sparky assistant into ChatGPT. As Walmart's Daniel Danker put it, shopping can start anywhere now, but customers still expect the same trusted Walmart experience wherever it begins.

Read that carefully. The world's largest retailer tested handing the transaction to an AI platform, learned that customers want consistency and trust from the brand itself, and took the experience back. Even in the most automated corner of retail, the differentiator turned out to be the relationship, not the transaction.

That is the pattern I expect to define the next five years. Agents will compress the middle of the shopping journey. The research, the comparison, the tab-hopping: much of that will be delegated. What cannot be delegated is how a brand makes a person feel, and there is no more powerful place to shape that than a physical store staffed by capable humans.

Why The Store Matters More, Not Less

Speaking on NRF's Retail Gets Real podcast from the Big Show in New York, PwC's Ali Furman and Stripe's Barbara O'Beirne described a future where physical locations serve as experiential hubs, showrooms and brand touchpoints, while some transactions happen elsewhere, sometimes invisibly through AI-driven replenishment. I read this as a promotion, not demotion, of the store.

When routine transactions migrate to agents, the purchases that remain in-store are the ones shoppers actively choose to make in person. They come for fit, for touch, for advice, for the pleasure of discovery, for a knowledgeable associate who can solve a problem no chatbot has context for. Location intelligence firm Foursquare observed heading into the 2026 back-to-school season that shoppers are visiting fewer stores with significantly higher intent, making every visit a must-win moment. Fewer visits, each carrying more weight. That is not a decline in relevance; instead, it’s a concentration of it.

There is a human truth underneath the technology story. An agent can find you the cheapest running shoe in the right size. It cannot watch you walk, notice your gait, and hand you the pair you didn't know you needed. It cannot turn a frustrated customer into a loyal one with a well-handled exchange. The more automated the rest of the journey becomes, the more those human moments stand out, and the more commercial value they carry. If agents commoditize convenience, humans differentiate on connection.

NEXT UP 👉 CRO Perspective: Nineteen Years. One Insight. Still Right.

The Store Measurement Mandate

Here is the uncomfortable part for retail leadership teams: If the store's role is shifting from transaction machine to the place where brand preference is built, then the metrics that prove store value need to shift too.

Same-store sales alone won't capture a store that inspired a purchase an agent later completed. Boards will increasingly ask harder questions: How many people came in? What did they engage with? How long did they stay? How many converted, and where did the rest fall out of the journey? What is the store's measurable contribution to demand that lands in other channels?

These have always been useful operational questions. In an agentic world, they become board-level ones, because they are the evidence that the store network is doing its new job. Retailers who can quantify traffic, engagement, and conversion with the same rigor they apply to e-commerce funnels will be the ones who can invest in stores with confidence while competitors guess.

Agentic AI is real; it is moving fast, and retailers should absolutely be building for it. But the lesson from its first year at scale, including from Walmart's very public recalibration, is that the technology amplifies the value of what it cannot replicate. Trust. Experience. Human judgment. Physical presence.

The shopper of 2026 may let an agent handle the errands. They will still walk into stores for the moments that matter, and they will reward the retailers who make those moments count. The store is not being written out of the retail story. It is being handed a bigger role, a higher bar, and a genuine opportunity for retailers ready to measure up.

MAKE YOUR STORES MORE IMPACTFUL 👉 The RetailNext Advantage

Pourquoi la planification des horaires et la gestion des tâches dans le secteur de la vente au détail doivent converger

bflikkema@storeforce.com (Ben Flikkema) — Fri, 03 Jul 2026 13:00:01 GMT

For years, retailers have treated scheduling and task management as separate functions.

Scheduling determines who is working. Task management determines what needs to get done.

Historically, that separation made sense. Stores were simpler, operational demands were easier to predict, and managers had more time to coordinate work manually.

Today's retail environment is fundamentally different.

The modern store is no longer just a place where customers browse and make purchases. It has become a fulfillment center, a customer service hub, a returns center, a merchandising execution point, and an increasingly important touchpoint in the overall brand experience.

At the same time, the volume of work flowing into stores continues to grow. Retailers are asking store teams to support omnichannel fulfillment, inventory initiatives, merchandising updates, promotional launches, loyalty programs, and operational compliance requirements, often while working within tight labor budgets.

The challenge is that while retail operations have evolved, the way many retailers plan work has not.

When Scheduling and Tasks Operate Independently

Most retailers have invested heavily in labor planning.

Traffic forecasts, sales projections, labor budgets, and workforce management systems help determine how many associates should be working and when. These tools are designed to align staffing levels with customer demand and help retailers manage one of their largest operating expenses.

At the same time, task management systems help distribute work across stores, communicate priorities, and track execution.

The problem is that these two processes often operate independently of one another.

A store may receive a major merchandising reset during a period of peak customer traffic. A promotional campaign may require several hours of execution despite already tight staffing levels. An inventory initiative may arrive after schedules have been finalized, forcing managers to find time that simply does not exist.

When workload and staffing are planned separately, stores are left to bridge the gap.

Managers spend their days reprioritizing tasks. Customer service competes with operational work. Corporate initiatives compete with local realities. Important work gets delayed, rushed, or completed inconsistently.

The issue is not poor scheduling. The issue is not poor task management.

The issue is that neither process has a complete picture of the other.

The Hidden Cost of Disconnected Planning

The consequences of this disconnect rarely appear as a single operational failure.

Instead, they emerge through dozens of small execution gaps that compound over time.

A promotional display is installed a day late. A merchandising update is only partially completed. Inventory tasks are pushed into the following week. Managers spend hours reorganizing priorities rather than coaching associates or engaging with customers.

Viewed individually, these situations may seem minor. Across hundreds or thousands of locations, they become a meaningful business problem.

Consider a retailer launching a major promotional event across its store network.

Corporate teams spend weeks planning the campaign. They develop signage, create merchandising instructions, forecast demand, and establish execution timelines. Significant resources are invested to ensure the promotion generates the expected return.

The success of that investment ultimately depends on what happens in stores.

But what happens when the work arrives after schedules have already been finalized?

The task gets assigned, but the labor required to complete it does not.

Managers are forced to pull associates away from other responsibilities, delay existing work, or accept that some tasks simply will not be completed on time. The result is inconsistent execution across the store network. Some locations execute flawlessly. Others complete only part of the work. Others miss deadlines entirely.

The cost is not simply an unfinished task.

The cost is missed sales opportunities, reduced promotional effectiveness, and an inconsistent customer experience.

A promotion executed three days late does more than create operational headaches. It reduces the time customers are exposed to the offer. A delayed merchandising update can impact product visibility and limit the return on marketing investments designed to drive traffic into stores. Inventory activities that are repeatedly postponed can create availability issues that affect both sales and customer satisfaction.

Many of the tasks assigned to stores are not operational activities at all. They are business activities tied directly to revenue, customer experience, and brand performance.

When execution suffers, business results often suffer with it.

From Labor Planning to Business Performance

For most retailers, labor remains one of the largest controllable expenses in the business. As a result, labor conversations have traditionally focused on cost. How many hours should be allocated? How closely are stores adhering to budget? Where can efficiencies be found?

Those questions still matter, but they no longer tell the full story.

The challenge facing retailers today is not simply managing labor costs. It is ensuring labor is aligned with the work stores are expected to perform. A schedule may meet every labor target and still leave a store unable to execute key priorities because the workload attached to those priorities was never considered during the planning process.

This is where many retailers are beginning to rethink how they approach store operations.

Rather than treating scheduling and task management as separate disciplines, leading organizations are working to connect them. They recognize that every task carries a labor requirement, whether it is a merchandising reset, a promotional launch, an inventory count, or a new operational initiative. If the labor required to execute that work is not considered during scheduling, execution becomes dependent on managers finding ways to make it work after the fact.

That approach is becoming increasingly difficult to sustain.

As stores take on more responsibilities and labor budgets remain under pressure, retailers need a clearer understanding of whether stores have the capacity to execute the work being assigned to them. The question is no longer simply whether enough associates are scheduled. The question is whether enough time has been allocated to successfully deliver the outcomes the business expects.

This shift represents a move from labor planning to work planning. It recognizes that staffing levels and workload are not separate operational considerations. They are two sides of the same equation.

Retailers that connect scheduling and task management gain greater visibility into execution risk before it becomes a problem. They can identify when stores are overloaded, adjust priorities more effectively, and allocate labor where it will have the greatest impact. Just as importantly, they can improve consistency across their store network, ensuring initiatives are executed as intended rather than varying from location to location.

The retailers that execute best in the years ahead will not necessarily be those with the most labor or the most tasks. They will be the ones that most effectively align labor with the work that drives sales, customer experience, and operational performance.

Retailers have spent years becoming more sophisticated in how they forecast customer demand. The next opportunity is applying that same discipline to workload demand.

The future of retail scheduling is not simply about putting the right number of people in the building.

It is about ensuring the right work gets completed at the right time.

Next on your reading list 👉 The Store Experience Crisis: Data-Driven Staffing Wins