In today’s fast-paced world of digital retailing, the ability to revise prices swiftly and on a large scale has emerged as a decisive differentiator for companies. Many retailers now track competitors’ prices via systems that scrape rivals’ websites and use this information as an input to set their own prices manually or automatically.
A common strategy is to charge X dollars or X percent less than a target competitor. However, retailers that use such simple heuristics miss significant opportunities to fine-tune pricing.
Some companies are now applying machine-learning models to guide their pricing decisions, but even these retailers tend to take an overly limited approach. They try to match or undercut competitors’ prices without taking into account factors such as whether rivals are out of stock or how consumers make their purchasing decisions.
In this article, the authors present a step-by-step process for dynamic pricing that focuses on building computer models that consider not just competitor pricing but also product availability and customer behavior to recommend optimal prices in real time.The ability to use AI to change prices frequently online and in physical stores has become critical to competing in retailing. But even retailers who have built such computer models take an overly limited approach.
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