Why can you sometimes find the same item for sale at different prices from Target, Walmart and Amazon in a 10-minute Google search?
While you might expect item prices to stabilize when consumers can easily compare among sellers, there’s often a surprising disparity, a phenomenon called “price dispersion.” Theories abound as to why, but a new model developed by BYU economics professor Brennan Platt highlights one very significant factor: buyers’ search timelines.
Platt constructed a model predicting that time-constrained buyers will pay higher prices over time and tested the hypothesis using eBay auctions. Although it’s intuitive that buyers are willing to pay more when they have less time, Platt’s complex model and original data show in detail how the single factor of a deadline creates ripple effects in consumer behaviors.
Research on price shopping typically assumes that buyers will search indefinitely for a good bargain. Platt thought this was a false assumption, since most buyers eventually run up against either a definite deadline (“I need this gift for a party tomorrow!”) or a perceived one (“I’m tired of shopping for this!”).
“Effectively these different companies like Target, Walmart and Amazon are targeting people at different points in their search,” said Platt. “Those offering really low prices know that some people have lots of time on their hand, and those offering really high prices are catching the chumps who get on the scene close to their deadline.”
Until now, it’s been nearly impossible for researchers to get hard data on how buyers adjust expectations as they shop—they could see only the final price consumers settle for.
“But on eBay, there’s a large class of things being sold that are new-in-box, same stuff you get at Target or Walmart,” Platt said. “With these identical goods being sold over and over, each time a person bids on them and loses, we have a record of what they were willing to pay at a given time.”
While he was developing the theory, Platt captured rudimentary data from eBay bidders on LEGO sets.
“With the Death Star, it worked great,” he recalled. “There was a very consistent rise in price offers from the people I followed.”
A couple of months later, he hit the jackpot when he bumped into former BYU undergraduate Bradley Larsen at a conference. Larsen had just finished a one-year postgraduate position at eBay.
Platt brought in Larsen and his colleague Dominic Coey, who gathered data on bidders across multiple eBay auctions on one million brand-new goods. “The match between model and data popped out very cleanly,” Platt said.
The results indicated that bidders who lose one auction are more likely to increase their bids in the next. They are also more likely to eventually purchase the item at the more expensive posted price, usually within a single day of their last failed bid. Shoppers who search the longest are the most likely to win auctions. The model’s implications can shape how businesses market goods.
“For instance,” Platt said, “any policy eBay changes that alter the structure or timing of an auction will affect consumers’ willingness to participate and pay certain prices.”
For example, eBay roughly doubled its listing fees in 2010. While the change applied equally to auction and posted prices, sellers shifted dramatically toward posted prices.
“Our model explains the lopsided response, as consumers had fewer opportunities to win a deal; running out of time, they were forced to use posted price listings.”
Other businesses can also use the model to modify their approach to consumers. “The big push now is for tech firms to understand their customers better through big data so they can strategically adjust prices,” Platt noted. “Our study shows the value of attending to subtle shifts that occur as shoppers face deadline pressures.”
The study was published in The American Economic Review.