Alex Galkin is CEO & Co-founder of Competera, an AI-driven pricing software for brick&click enterprise retailers globally.
A loyal shopper notices the price of their usual olive oil has risen three times in as many months. This feels arbitrary and unpredictable, eroding trust. They wonder: Will it be higher next week? Should I shop elsewhere?
In today’s climate of inflation, tariffs and supply chain instability, pricing changes are frequent and emotionally charged. Shoppers are responding: Over half (55%) of U.S. consumers now prioritize price, according to an April 2025 Rakuten study. Research from Bain & Company shows consumers trading down to cheaper alternatives and relying on promotions.
The reality: The price tag is no longer just a number—it’s a message. Prices signal a retailer’s commitment to fairness and consistency. While volatile pricing undermines trust, customer-centric pricing reinforces stability, value and loyalty, even as costs rise.
The Problem: When Product-Centric Pricing Fails Retailers
Many retailers rely on a product-centric model, driven by internal costs, margins and competitor pricing. This overlooks external reality: Customers assess value through their unique needs, finances and shopping context. A budget-conscious shopper values price differently from someone buying a gift.
When pricing is reactive and disconnected from customer value, it erodes trust and profitability. Cost-plus pricing, flash promotions and blanket markdowns offer short-term bumps but distort value perception and train shoppers to wait for discounts.
Worse, these tactics ignore vital context: demand variation, product competition and how segments interpret price. Add opaque algorithms, and the result is arbitrary volatility for consumers, undermining loyalty, eroding margins and weakening brand equity.
The bottom line is, today’s pricing must reflect how real people shop, not just costs, inventory and basic sales data.
The Solution: Strategic Reorientation Toward Customer Value
Customer-centric pricing begins with a mindset shift. Pricing is not just a mathematical function of costs and market benchmarks; it’s a reflection of how customers perceive and assign value in real-world buying contexts.
This approach demands a strategic realignment, putting the customer’s point of view at the center of every pricing decision. What feels fair or compelling can vary widely depending on the shopper, where they’re shopping, what they’re buying and even when they’re buying it. Understanding how product scarcity and availability shape willingness to pay is also crucial.
Until recently, truly customer-centric pricing was aspirational. Retailers often lacked the tools to scale it. Legacy systems struggled to account for cross-product relationships, store-level trends or the nonlinear ways customers assign value. That’s changing fast with advancements in AI.
How Contextual AI Enables More Customer-Centric Pricing
Traditional dynamic pricing approaches remain fundamentally product-centric. They adjust prices based on limited variables like cost or competitor pricing, failing to capture the full complexity of customer purchasing decisions.
Contextual AI, in contrast, enables truly customer-centric pricing by leveraging neural networks to weigh dozens of interdependent factors simultaneously. Unlike conventional approaches that treat each product in isolation, these advanced models can:
• Analyze cross-product relationships and basket composition.
• Incorporate regional preferences, store formats and seasonal patterns for location-specific recommendations.
• Consider promotional fatigue, competitive elasticity and price perception thresholds.
• Adapt continuously as customer preferences evolve.
This multidimensional approach allows retailers to move beyond rigid rules to understand the actual context of purchasing decisions, not just whether a price is high or low, but whether it aligns with how the customer feels about the product.
What This Looks Like In Practice
Consider a specialty outdoor retailer facing new tariffs on imported hiking boots. A simplistic response might be a flat price increase. But a customer-centric pricing model utilizing contextual AI offers nuanced recommendations.
Sales patterns might show that serious hikers—the retailer’s core segment—value durability and performance and are less price-sensitive for premium boots. In contrast, casual buyers of entry-level hiking shoes are far more price-sensitive. An AI model could suggest that a 7% increase in premium boots might result in only a 3% drop in sales, while the same 7% hike in entry-level shoes could lead to a 15% drop and potential customer churn.
Instead of a blanket increase, the optimal strategy might involve raising the premium boots’ price while emphasizing their features, and absorbing or offsetting costs on the entry-level shoe to maintain volume and loyalty. Layering in additional context, such as location, seasonality and correlated product behavior, allows the model to become even more powerful, guiding decisions like preserving price on one item while slightly increasing prices on a complementary product that customers view as a bargain.
Your Action Plan: Five Steps To Get Started
Shifting to customer-centric pricing means fundamentally rethinking how your business understands and responds to demand. Here’s how modern retail leaders can make the leap:
1. Rethink pricing logic. Move from merely “setting the price” to “modeling the outcome.” Pricing failures often stem from flawed assumptions about static elasticity or uniform rules.
2. Map profit and perception. Go beyond sales and costs. Use AI to identify high-leverage price points that significantly influence shopper behavior or contribution margin.
3. Operationalize contextual elasticity. Recognize that elasticity isn’t a fixed number; it’s dynamic, shaped by product role, store format, basket affinity and fulfillment timing. Build systems that adapt continuously.
4. Run full-spread simulations. Before changing a price, ask: What will this do to my margin, volume and customer retention across the entire product range? AI can enable modeling thousands of scenarios to find optimal price sets.
5. Create a fast, measurable learning loop. Customer-centric pricing requires short-cycle optimization—weekly or faster—to respond to market volatility without overcorrecting. Success depends on transparent KPIs, control groups and continuous discovery.
Why The Time To Act Is Now
The retailers who will thrive in the coming years won’t be those who simply react to volatility. They’ll be those who recognize that pricing is about more than numbers.
The first step is acknowledging that the old approach of reactive, cost-driven pricing can’t deliver the stability your customers crave or the results your business needs. The second is recognizing that with today’s AI capabilities, customer-centric pricing is an achievable reality that delivers measurable results. Retailers that embrace customer-centric pricing won’t just weather economic uncertainty; they’ll convert it into a competitive edge.
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