According to a recent study, consumers are more price-sensitive today than ever: more than three-quarters of them (75%) fear that inflation will drive up the prices of essential goods and expensive items, forcing them to cut back on overall spending. Supply chain pressures and geopolitical tensions exacerbate this challenge for both consumers and retailers, with 73% of European shoppers fearing price increases aimed at offsetting rising theft and crime.
Discounts, markdowns, special offers, and price matching with competitors are well-known methods to retain consumers. However, managing these represents a colossal task still requiring too much manual intervention and outdated software to process data. Too often, pricing decisions throughout the product lifecycle rely on incomplete historical data or simplified methods.
This is why it is now necessary for retailers to adopt artificial intelligence (AI) to optimize and guide their pricing strategy. AI offers concrete financial advantages through three key functionalities: detailed demand forecasting, advanced price elasticity modeling, and real-time markdown optimization. These are tasks that could be performed by a human but would require infinitely more time and money.
AI offers significant opportunities to improve retailers' margins and revenues. Indeed, while traditional pricing models make it difficult to achieve tangible gains, integrating AI models into existing systems eliminates the time needed for training and upskilling individuals to provide informed pricing recommendations. Thanks to AI, product category managers and pricing analysts gain complete visibility into retail industry data.
A fashion retailer implemented an AI-based pricing solution in just 16 weeks, seamlessly integrating it with its existing ERP. The AI module automatically incorporated pricing recommendations into the retailer's planning system, enabling teams to quickly analyze and validate them. This approach led to a 5% increase in margins within three months without affecting ongoing operations.
This example illustrates how AI can become a true optimization lever for teams. And this is just the beginning. So, how can retailers collaborate with AI to refine their pricing strategy and improve their margins?
Three Essential AI Models
Three AI models can help define and implement effective pricing strategies: generative models (GenAI), machine learning algorithms, and cognitive models. Well-trained GenAI models have proven effective in anticipating pricing trends, proposing alternative pricing strategies, and simulating the impact of various promotional campaigns on consumer behavior. They have also convinced many retailers of their ability to generate and test alternative pricing scenarios upfront, preventing teams from engaging in strategies without tangible proof of their effectiveness on purchase decisions. For example, a fashion company used generative AI to design personalized discounts during Black Friday, increasing its revenue by 12% and margins by 9%.
Some retailers use machine learning algorithms to continuously optimize their pricing models, taking into account historical sales, past promotions, and changes in consumer demand. One of the main advantages of machine learning-based pricing models is their ability to improve over time through continuous analysis of large datasets to refine pricing decisions. A sneaker brand adjusted real-time prices of its most in-demand and limited-edition products, increasing sales by 15% and total revenue by 10% through progressive price reductions.
Finally, the use of cognitive AI becomes essential due to its ability to integrate external factors, such as economic conditions, competitive prices, local events, and consumption trends, to optimize pricing decisions. Without cognitive AI, these essential external factors for understanding purchasing behavior cannot be effectively integrated into the pricing strategy. A winter coat retailer used cognitive AI to adjust prices based on weather forecasts, extending full-price sales by three weeks and improving its gross margin by 7%.
Retailers have a wealth of high-value data that an appropriate AI model can turn into commercial growth levers. Data visibility is improved, and intelligent automation is realized through a pricing strategy perfectly aligned with revenue goals and customer demands.