Power Pricing In The Age Of AI And Analytics | Forbes

Recent advancements in technology have made dynamic pricing strategies even more accurate and effective. A large retail bank using pricing algorithms is now able to tailor deposit rate offers by observing a customer’s checking account activities. The algorithm, detecting deposit flows between the bank and a secondary savings bank, can take into account the nature of the customer’s relationship with the bank, predicted revenues and the rate at the competing secondary bank to make an attractive rate offer. By personalizing promotional offers in this way, the bank is able to get a higher rate of response from promotions.

Meanwhile, disruptors like ridesharing service Uber and online retailer Amazon are collecting and tapping into new data sources to improve the effectiveness of their pricing algorithms to maximize revenues. According to a case study of Uber drivers, the company’s algorithms leverage drivers’ sensitivities to various nudges, including heat maps, incentives and messaging, to influence and increase supply during peak demand periods. According to Amazon’s Privacy Notice page, the retail giant collects and analyzes everything from purchase histories and products viewed or searched for to reviews, wish lists and length of visits to certain pages. This huge pool of data on its customers’ shopping habits can help Amazon better understand what shoppers are looking for, what they buy and what prices they are willing to pay.

Increasingly, company leaders are recognizing that a dynamic pricing strategy supported by big data and artificial intelligence (AI) can help them gain a competitive pricing advantage over rivals. However, when companies enter the new field of dynamic pricing, there are some key best practices they must follow to ensure success.

Man And Machine

It is not uncommon to find pricing decisions rendered by sophisticated techniques and algorithms running the risk of not being implemented faithfully by the human decision makers and sales staff. This is especially the case if employees are not fully on board with the analytical pricing approach. For example, at Simon-Kucher & Partners, we worked with a well-known U.S. department store, which implemented markdown pricing algorithms to better manage markdowns of slow-selling styles. However, its human employees were not fully compliant with the pricing recommendations generated by the pricing algorithms. While the algorithms still delivered a lift in revenues, had the human employees been fully compliant, the total revenue lift could have been much more. The key is to engage frontline sales staff early in the process when implementing a sophisticated pricing analytical algorithm.