INDUSTRY VIEW
CAN AI DEFINE A PRICING STRATEGY FOR PARTNERS?
JAMES NAWROCKI, RETAIL SOFTWARE SOLUTIONS CONSULTANT, ZEBRA TECHNOLOGIES
Discounts, markdowns, special offers, price-matching are familiar tactics to secure customer loyalty. It is a huge undertaking, which suffers from too much manual efforts and legacy software to crunch the data. Too often, pricing decisions across the product lifecycle are ill-informed by historical data or defined by simplistic practices. Is it not time channel partners turned to AI to develop a pricing strategy?
Shoppers are more price sensitive than ever, with over three-quarters, + 75 % worried inflation will drive up the prices of both everyday essentials and bigticket items and force them to reduce overall spending, according to Zebra’ s latest Global Shopper Study. Supply chain pressures and geopolitics add to the pricing challenge faced by consumers and shops alike, while 73 % of shoppers in Europe are also concerned about price raises to cover the cost of increasing theft and crime.
Discounts, markdowns, special offers, and competitive pricematching are familiar tactics to secure customer loyalty. It is a huge undertaking, which still suffers from too much manual input and legacy software to crunch the data. Too often, pricing decisions throughout the product lifecycle are illinformed by historical data or simplistic rules- based practices.
Is it not time retailers turned to AI to inform and guide pricing strategy? It offers concrete financial benefits thanks to three exclusive capabilities: granular demand forecasting, advanced price elasticity modelling, and dynamic markdown optimisation. Could a human do these things? With enough time, maybe.
But why wait that long? There are significant margin and revenue improvement opportunities right now. With AI, retailers can seize them immediately.
Too often, pricing decisions throughout the product lifecycle are ill-informed by historical data or simplistic rules-based practices.
Traditional pricing models make it nearly impossible to see gains. Integrating AI models into current systems eliminates the time it would take to get a person upskilled and positioned well enough to deliver informed pricing recommendations. Product category managers and pricing analysts can gain full visibility of retail data using AI.
One fashion retailer implemented AI pricing within 16 weeks, integrating it into existing the ERP system. The AI-powered module fed optimised pricing recommendations directly into the retailer’ s planning system, allowing planners to quickly review and approve adjustments. This approach provided a 5 % margin lift within three months without disrupting ongoing operations.
Could a human deliver the same results on the same timeline without AI assistance? Probably not. But this example is just one of many. So how can teams work with AI to drive better pricing decisions and improve margins?
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