Assistant Professor Ty Henderson investigates the factors that influence consumers’ buying decisions. This article, the second in a two-part set, focuses on how sellers can use his findings to their advantage. The first article explores the issue from the buyer’s perspective.
Understanding a customer’s decision-making process can go a long way in helping sellers target the market, set appropriate prices and make sales more efficiently. In a recent study, McCombs School researcher Ty Henderson outlines how incorporating “must-have” and “can’t have” thresholds in the demand model gives companies a more accurate sense of how their customers will respond to different prices and product features.
Accounting for Deal-Breakers
Sellers consider many different variables when they analyze the market’s appetite for their products, and many conduct research to determine which features consumers want most. However, Henderson says, sellers often think of these attributes in terms of tradeoffs. If a product has some features that a particular customer finds very desirable, the reasoning goes, that customer might buy it even if it also has some features they don’t like all that much.
While that is often the case, Henderson says that assumption ignores the possibility that some features are absolute deal-breakers to certain shoppers. In these cases, customers won’t buy the product no matter how good the tradeoff is.
In the study, Henderson and his coauthors apply a model that accounts for people who will reject any product that doesn’t have their “must-haves” or one that does include any of their “can’t-haves.” This understanding allows sellers to more accurately pinpoint dead spots in the market and shift their priorities accordingly.
“The traditional model assumes that everything can be compensated for, that everything can be traded off,” Henderson says. “Once you identify the must-haves and can’t haves, you realize that you can’t compensate for their absence.”
For example, if a subset of potential customers is looking to buy 55-inch televisions and will not consider any other size, Henderson’s model identifies the market’s appetite for that product and gauges whether it would be worthwhile for a seller to try to accommodate that need.
“If you don’t account for these screens” — can’t haves and must haves — “you [might] miss out on unmet demand,” Henderson says. “So you learn something about market opportunity.”
Price is one of the biggest considerations involved in buying decisions, because it is usually not a matter of preference but one of practicality. The traditional model for gauging how customers will react to different price points assumes that demand decreases as price increases. But Henderson says that model doesn’t tell the full story.
Many customers shop with a specific maximum price in mind, and if the cost of the product goes beyond that ceiling, the customer will simply refuse to buy it. So in reality, demand drops off sharply at that price point — yet the old model depicts it as a continuous curve.
“There’s a traditional view that demand is somewhat linear,” Henderson says. “You set a price and get quantity demanded. As price rises, quantity demanded drops. What this model says is that it’s not a nice little slope; at some point it just drops off.”
By providing a more realistic measure of demand, the enhanced model is better at pinpointing the product’s optimal price point — the price that will result in the highest possible revenue for the seller.
Sellers who follow the traditional model run the risk of underestimating demand for a product, which could cause them to set prices that are too low, Henderson says. That might give the product a higher market share, but it could also generate less overall revenue relative to the model applied in Henderson’s study.