Emotional Intelligence: The Next Step In Retail Dynamic Pricing
Anton Timashev, co-founder and CEO at Wayvee Analytics – emotion recognition technology for capturing direct customer feedback in retail.
You've probably checked the price of a flight, found a good deal and returned later—only to see the price has gone up. Or you've seen an online product's price change depending on when and how often you checked it. This is dynamic pricing in action—where prices shift in real time based on demand, competitor pricing and customer behavior.
For online retailers, this is standard practice. Prices adjust constantly, helping businesses maximize revenue and quickly respond to demand. In physical stores, however, pricing has traditionally been fixed, with changes happening only through scheduled markdowns, seasonal sales or promotions.
That's beginning to change. Electronic shelf labels (ESLs), AI-driven analytics and real-time inventory tracking are allowing brick-and-mortar stores to experiment with dynamic pricing, adjusting prices throughout the day based on factors like stock levels, demand or external conditions like weather.
However, even with these advancements, pricing strategies still rely on past sales data, inventory movement and competitor trends. One critical piece is still missing: understanding how pricing influences customer decisions in real time—at the shelf.
While demand, competitor pricing and past sales data are useful indicators, they don't provide direct insight into how customers actually perceive pricing at the moment of decision making. Retailers can track whether a price adjustment increases or decreases sales, but they don't know why. Did customers see the price as fair? Did they hesitate? Were they expecting a discount that didn't appear?
Most purchasing decisions happen in front of the shelf, but retailers lack real-time feedback on how pricing influences customer intent. Unlike e-commerce, where metrics like abandoned carts or time spent on a product page provide clues about price perception, physical stores have no equivalent indicators. Instead, retailers rely on after-the-fact sales reports or surveys, which don't capture missed opportunities or hesitations.
This missing feedback loop means pricing decisions are made without fully understanding how customers react in real time—leaving a gap in dynamic pricing optimization. This is where emotions play a key role, as they drive purchasing decisions far more than rational price comparisons. Most purchases aren't made through a purely logical process but are influenced by how customers feel in the moment—whether a price excites them, creates hesitation or triggers an impulse buy.
What exactly happens in those few moments before a purchase decision is made?
When it comes to consumer behavior, the brain plays a pivotal role in how we make decisions—especially emotional decisions. This is key when we talk about pricing.
Research by Dr. Brian Knutson, a neuroscientist in consumer behavior, suggests neural activity can signal whether a customer will buy a product before they consciously make the decision.
The moment when people are weighing the product's value against their emotional response to its price is where purchase intent truly forms. For retailers, understanding this subconscious process opens up new ways to refine pricing and marketing strategies to better align with how customers actually make decisions.
During decision making, the portion of the brain called the medial prefrontal cortex (mPFC), a region involved in evaluating choices and regulating emotions, helps us assess options by drawing on past experiences, personal preferences and emotional responses.
When we're considering a product in a store, our mPFC is actively weighing its value. Is it worth the price? Should I buy it or walk away? This evaluation happens largely on a subconscious level, with emotions playing a key role in shaping our decision—often before we're fully aware of it.
Most pricing models rely on macroeconomic trends and competitor pricing, making them more about the retailer than the customer. When you bring emotional intelligence into the mix, the focus shifts to how people actually perceive prices—creating a model that's built around the customer, not just the market.
Retailers have tested different ways to make pricing more customer-driven. For example, Kroger tried ESLs with facial recognition to show targeted ads and adjust prices based on who was shopping. However, this method used biometric data like age and gender, which raised concerns about bias in pricing. Emotional intelligence takes a different approach; it's about understanding how customers feel in the moment and shaping their experience accordingly.
How can retailers actually use these insights? Advanced AI-powered emotional analytics can be integrated with ESL systems to measure how customers perceive different prices in real time. Since emotional response is a key indicator of purchase intent—the moment when a customer decides whether to buy—retailers can experiment with different price ranges to see what resonates best.
Another possible application is A/B testing of different price points. Unlike traditional A/B testing, which relies on sales data after a purchase is made, this approach provides immediate feedback on price perception. Retailers can understand how different pricing strategies affect customer sentiment before the point of sale.
While emotional analytics is opening up new possibilities for pricing strategies, there are still a few reasons why it's not yet mainstream. Many early solutions have relied on camera-based systems or biometric data, which raised understandable concerns about privacy and transparency.
Newer technologies now offer less intrusive, more respectful approaches—but as with any emerging tool, it takes time to build trust and prove consistency in real-world environments. As these solutions continue to evolve and more pilot programs demonstrate their value, emotional intelligence is poised to become a more integrated part of how retailers fine-tune pricing—not just based on market logic but on customer response.
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