NRF 2025 Recap: Retailers Need Pricing Solutions

NRF’s Big Show is a perfect contradiction. Full of energy but leaves you absolutely drained when done. The show floor is dominated by a handful of massive booths that are in the same spot year-over-year, but new innovations and other vendors are found throughout. If you want to try to find out what is happening in retail, go to NRF. And try because no one can get through it all and give a complete picture. There is too much going on with too many conversations. It’s called the Retail’s Big Show for a reason. With that context in mind, I’ll share my thoughts on NRF from my conversations and experience.

Let’s start with AI, and most notably Gen AI. Everyone was talking about it, and because of that, it creates both excitement and confusion. Yet, AI is a massive definition and conglomerate of capabilities. The most mentioned ones are Predictive AI, Gen AI, and Agentic AI. So what’s the difference? Well, I asked ChatGPT (a Gen AI solution).

What is Predictive AI? (in 50 words)

Predictive AI uses data, statistical algorithms, and machine learning techniques to forecast future outcomes based on historical data. It identifies patterns and trends to make predictions, enabling applications like demand forecasting, risk assessment, customer behavior analysis, and personalized recommendations, helping businesses make informed, proactive decisions and optimize operations efficiently.

What is Gen AI?

Generative AI (Gen AI) is a subset of artificial intelligence that focuses on creating new content, such as text, images, music, videos, or code, based on input data. Powered by advanced models like GPT (language) or GANs (visuals), it enables applications like chatbots, content creation, design generation, and personalized user experiences.

What is Agentic AI?

Agentic AI refers to artificial intelligence systems designed to act autonomously and make decisions on behalf of users or organizations. These systems exhibit goal-directed behavior, adapt to dynamic environments, and execute tasks with minimal human intervention. Agentic AI can include applications like autonomous robots, virtual assistants, and decision-making agents in complex systems.

Crystal clear, right? If those definitions didn’t muddy the waters for anyone, then let’s further define these by how they are used in retail. Of these three, the most prominent AI is Predictive AI. It processes data and to make predictions and recommendations for demand forecasting, price optimization, replenishment, customer segmentation, assortment optimization, order management, and many other areas.

While these AI-driven tools are less flashy than ChatGPT, they are undeniably effective at solving practical, everyday business problems. Even with the explosion of Gen AI, IHL predicts that through 2029, 47% of AI’s benefits in retail will come from traditional AI/ML (like Predictive AI), 48% from emerging Gen AI, and the final 5% from AI advancements yet to be fully realized.

Generative AI (Gen AI) is often associated with processing large language models. What that means is it can understand human language and program language. In retail, customer experience and communication are the most significant beneficiaries of early Gen AI work (Blog support helps too).

I’ll also do a short plug for the Retail Orphan Initiative (RetailROI), a charitable organization that aims to help orphaned and vulnerable children. (Check them out.) At their event, which they do on Saturday every year before NRF, IHL shared some of their latest research on where the best results from Gen AI are coming from. Product Search / Merch Support was #1, with 54% of retailers seeing significant positive results. Personalization, Customer Segmentation, and Customer Service Chatbots round out the top 4 with 33%, 32%, and 30% respectively. It drops off fairly significantly past that point. On the low end is Dynamic Pricing, with only 4%.  The product search can be very helpful. Instead of digging through a catalog (or the drill down on the left) on a retail site to find what you want, you can type in what you are looking for and it will bring results back for you.

Agentic AI has even more transformative potential due to driving business efficiency across multiple systems. Imagine a human prompt or action to ask your system(s) to do something. In response, it crosses numerous systems to consistently execute a task. What’s an example?  Dynamic Pricing. But remember above for its proven ability.

Here is the idea: an Agentic AI could monitor market trends, analyze competitor pricing, get your current inventory levels, calculate demand, adjust your prices, and send them to the POS system or digital shelf labels in real-time or at a scheduled time. Additionally, it could measure the ongoing results and send you reports and updates to show your sales, profitability, and competitive positions. All done from a simple prompt instead of having a user cross systems crossing systems. The efficiency and time savings for business users are immense. Cool, yes. Ready for prime time? Absolutely not.

Yet, NRF2025 was much more than AI. Given our specialty, many of our conversations revolved around pricing, promotions, and competitive intelligence. To no one’s surprise, people didn’t want to dig into AI; they dug into solving their everyday challenges. I summarized some of these below.

Retailers are Improving Price Execution, But What Should the Price Be Now?

Electronic Shelf Labels (ESLs), aka Digital Shelf Labels, are gaining momentum, especially with Walmart accelerating its digital signage rollout with Vusion. Europe is far ahead of the US in their adoption; however, as adoption is ramping up, many retailers and ESL providers have asked us, “Now that they can quickly change the price, what should it be?”

ESLs enable retailers to be more agile in pricing, but flexibility only matters if paired with strategy. Several retailers and vendors understand that digital signage allows them to adjust their prices more quickly and with less labor. However, the value and return on investment come from setting prices and rapidly responding to market and competitive price pressures.

The Need for Better Competitive Intelligence

Shoppers are frustrated by high prices, prompting them to visit a record number of grocery stores in search of better value. Retailers feel the impact. Basket sizes and shopping trips are shrinking, even as overall purchase amounts have grown. Finding prices and assortments that increase market share, is an ongoing challenge, and at times can feel like a losing battle against rising national competition.

This raises multiple questions beyond what the best price to give is. Some of those I summarized below:

• How can I promote the right items to profitably attract shoppers and increase basket size?
• Where should I adjust prices to align with shopper expectations without disappointing them, especially as major competitors announce cuts?
• What promotions and pricing strategies are my competitors using, and how should I respond?
• How can I effectively measure the success of my pricing strategy?
• How does my assortment compare to competitors, and how can I confidently decide which items to add or drop?
• And how can you reverse engineer your competitor’s pricing strategy so you can adequately counter it?

Answers can be quite extensive, but there are answers for them. And that is the exciting part. Retailers are no longer just taking simple markups and performing competitive price matches. They are using technology to find answers to these complicated questions.

The Private Label Challenge

Managing private label pricing, especially with the rise of premium private labels, has become a complex puzzle. Maintaining “Good,” “Better,” and “Best” pricing tiers between private label and national brands alone is the baseline expectation but insufficient for today’s needs. Retailers can have multiple national and private label brands while competing with other retailers who have the same.

Let’s assume that you are competitively matching a National Cheese Brand, which lowers its price. As a result, you want to increase the price of your private label brand to either capture margin or have shoppers buy up to the national brand. But you don’t want your private label brands to become more expensive. Or you don’t want to maintain a sufficient price gap between the national and the private label. So you shouldn’t raise the price, and maybe you have to lower that too, knowingly sacrificing more margin. Or you’re trying to show a low-cost image, and your primary competitors in a region are low-cost retailers with primarily private label selections. There start to be some many variables.

So when do you raise and lower prices? Where do you maintain brand parity?  How do you manage all the sizes, assortment options, and competition against your price strategies and business goals? What if you have no private label in your assortment but are competing against those with extensive amounts?

Tariffs and Costs

It’s there, under the surface, but people are cautious to talk about them – tariffs. Retailers assume it’s coming but don’t know when or what the impacts will be. But one thing is agreed upon. Retailers know they will have to mitigate any potential cost increases from tariffs. Simply passing along the cost to consumers will rupture any remaining trust.

Already, 88% of shoppers are frustrated with rising prices, and past inflation has shown us that consumers are just as likely – if not more so – to blame the retailer setting the price as they are the underlying source of the cost increase. Thus, the key element moving forward is using data to strategically absorb costs and maintain customer loyalty without relying on blanket price increases that alienate shoppers. For this, you need to understand how your competition is managing prices due to tariffs, and be able to set a plan accordingly to capture customers and market share without destroying your profits.

Curtain Call: NRF 2025

And that’s a wrap for my blog! From the bustling floors of the Javits Center to thoughtful conversations, NRF’s Big Show delivers a mix of energy, innovation, inspiration, and sore feet from walking. As the lights dim on this year’s event, the focus shifts to actionable next steps for 2025. AI may be the headline act, but success lies in leveraging tools to solve fundamental problems, deliver value, and create lasting growth. See you next year.