Online retailers are using dynamic pricing strategies that adjust the cost of items based on a consumer's browsing history [1].
This shift in e-commerce tactics changes the relationship between buyers and sellers by turning the shopping experience into a data-driven negotiation. As algorithms determine prices in real-time, consumers may find that the cost of a product fluctuates based on their perceived willingness to pay.
Dynamic pricing allows companies to optimize revenue by analyzing user behavior. According to MSN, "Retailers are looking at your browsing history to change the price you see in your cart" [2]. These systems track how often a user views a product or how they navigate a site to determine if a price increase or decrease will most likely lead to a sale.
To counter these algorithms, some consumers are adopting a strategy of intentional cart abandonment. By adding items to a digital cart and then leaving the site without completing the purchase, shoppers can signal to the retailer's bots that the current price is too high. This often triggers the automated system to send a discount code via email to entice the customer back to the site [1].
This behavior creates a cycle where both the retailer and the consumer use automation to find a middle ground. MSN said, "Maybe you should be doing the same when looking to buy" [2]. The goal for the shopper is to force the dynamic pricing bot to lower the price through a perceived loss of interest.
While these tactics are common in the travel and hospitality industries, they are becoming more prevalent across general online retail. The use of tracking cookies and user accounts allows stores to maintain a detailed profile of a shopper's habits, which then informs the pricing they encounter during a session [1].
“Retailers are looking at your browsing history to change the price you see in your cart.”
The rise of dynamic pricing represents a move toward 'personalized pricing,' where the sticker price is no longer universal. By utilizing cart abandonment as a counter-strategy, consumers are attempting to reverse-engineer the algorithms used by retailers. This creates a technical arms race between consumer behavior and retail AI, potentially leading to more aggressive data collection by stores to better predict buyer psychology.
