Whether on retailers’ own platforms or through third-party price tracking services, today’s consumers often have access to detailed information regarding changes in a product’s price over time. But how does this visibility influence their purchasing decisions?
Whether you’re looking to buy a plane ticket or a pair of socks, more and more online shopping platforms now offer consumers a detailed look into products’ historical prices. But how does this information influence buying decisions?
To explore this question, we conducted a series of experiments with a total of more than 5,000 business school students and working adults across the U.S. and Europe. We measured the impact of different kinds of price shifts on people’s interest in purchasing products such as plane tickets, a new TV, Bluetooth speakers, or a reusable water bottle. In each study, we showed participants identical product information, but we varied both the direction (i.e., whether the price increased or decreased) and frequency (i.e., the number of increases or decreases) of the past price data they were shown. We then asked them whether they wanted to buy the product — and we identified several consistent trends:
First, when consumers saw that the price today was lower than it had been in the past, they were more likely to buy now, because the current price seemed like a good deal. Similarly, when consumers saw that the price today was higher than it had been in the past, they were less likely to buy now, because the current price seemed like a bad deal. For example, all else being equal, if a consumer sees that a product currently priced at $100 was $200 last week, they’re likely to buy now, since the current price is much more appealing than the previous price. Conversely, if they see that the same product was $50 last week, they’re likely to hold off on purchasing, since the current price is less appealing than the previous price.
However, the picture gets more complicated when you consider the frequency of historical price shifts: In our studies, we found that if consumers were shown at least three changes in the same direction, they were likely to assume the price would continue to move in the same direction, whereas if they were only shown one or two changes in the same direction, they expected the price to change in the opposite direction. In other words, if a product that’s currently priced at $100 was $200 two weeks ago, $150 last week, and $125 yesterday, consumers will expect the price to continue to fall, making them likely to hold off on purchasing. But if they only see that the current price is $100 and the price two weeks ago was $200 — or if they see that it was $200 two weeks ago, $50 last week, and $200 yesterday — then consumers are more likely to expect that the price will go back up again, pushing them to buy now.
So what does this mean for sellers? If your goal is to encourage consumers to buy now, our research suggests that either a single large price decrease or a series of smaller price increases will be most effective. It may be tempting to slowly lower the price over time, but our data shows that this can lead consumers to assume that the price will continue to fall, making them hesitant to buy. But if they just see a single price drop, they’re more likely to expect a reversal (i.e., a single large increase) in the near future, pushing them to buy the product now. Similarly, if you just hike the price once, consumers may expect it to fall back down again — but if you repeatedly hike the price in smaller increments (assuming you can do so without pricing out buyers entirely), consumers are likely to expect the price to continue to rise, and so they’ll be more likely to buy before it does.
On the flip side, our findings can also help buyers make more informed decisions about whether and when they choose to make a purchase. As with any irrational bias, awareness of the natural tendency to expect streaks to continue and single large changes to reverse can help consumers question this assumption before acting on it. Instead of letting this arbitrary expectation guide buying decisions, consumers may benefit by doing a bit more research around the underlying factors driving price shifts. For example, a predictable, one-off event like Black Friday is likely to impact prices differently than a geopolitical event such as Russia’s invasion of Ukraine last winter or a macroeconomic trend such as rising inflation. Buyers may also benefit from learning more about a product’s longer-term price history, fluctuations, and typical industry-wide price ranges, to avoid being disproportionately influenced by near-term price changes. It’s also always a good idea to think about both how urgent your need for a given product is, and your own risk tolerance for a potential price increase, as this can affect whether it’s worth it for you to wait and see if the price falls.
Of course, there are countless factors that influence both consumers’ decisions around whether and when to buy and sellers’ decisions around how to price their products. But it’s important for both sides to recognize the key role that expectations play in influencing these decisions. As historical price data becomes increasingly available through both retailers’ own platforms and third-party price tracking services, managers and consumers alike will benefit from acknowledging how both the direction and frequency of historical price shifts influence our assumptions regarding how the price is likely to change going forward — and whether or not it’s worth buying now.
Copyright 2023 Harvard Business School Publishing Corporation. Distributed by The New York Times Syndicate.
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