How to give the best discount

Discounts galore! (Photo used only for representational purpose)

Make markdowns large enough to attract new customers, yet just small enough so that they don’t wait too long

Will your customers buy an item immediately? Or hold out for a sale?

If they’re waiting, what can you do to spur a purchase?

My new research reveals that retailers can grow profits by cutting prices sooner and more deeply than conventional modelling suggests.

We can look at a model which I call dPTT (discount, probability and time trade-off) to account for how consumers’ buying habits are influenced by “psychological distance”, a measure of how sensitive a consumer is to delays and risks in product availability, whether perceived or real. In a series of experiments, this model helps understand that the optimal markdown using dPTT could be about twice the discount advocated by the standard markdown model (for example, 20 per cent instead of 10 per cent in their base cases situation). Further, because it better approximates the ways humans actually behave, dPTT has the potential to increase markdown revenue 20 to 25 per cent, and overall revenues by 1.5 per cent. A typical retailer operates with a net margin of 3 per cent, which means that each per cent of extra markdown revenues translates into major profit increases.

Effectively, every time a customer enters the store, he or she mentally ‘solves’ a buy-or-wait problem. The idea then is to set a markdown that is large enough to attract new customers, yet small enough to control strategic waiting.

Markdown mania

Given the prevalence of markdowns and the complexity in manging them, retailers are always looking for more effective strategies. According to the research, nearly one-third of items are sold at discounted prices, generating about 20 per cent of retail revenues.

In the past, retailers would slash prices — on average 50 per cent — hoping to attract new customers.

Price discounts were seen as a form to expand the market by selling to consumers that are not willing to pay the full price. But the cannibalisation effect — the loss of sales at full price — was difficult to calculate and often ignored. The implicit assumption here is that consumers were myopic, and would ignore the possibility of waiting and buy at a discounted price.

Of course, this backfired: Customers became accustomed to waiting for sales.

Researchers came up with a new model: Discount expected utility, or DEU, which advocated either an “everyday low price” strategy or very modest (e.g. 10 per cent) discounts. DEU has multiple strengths and is directionally correct. It holds under a broad set of circumstances, yields exact formulas for the price discounts, and predicts how many consumers will buy now and how many will wait. Its main weakness, however, is that it assumes consumers behave rationally at all times.

Rational, but not completely

In reality, consumers fall into a grey area. Actual consumers are not irrational, but they are not 100 per cent rational either. dPTT is, in essence, a modification of DEU that better approximates how individuals feel about time and risk trade-offs.

For instance, the DEU model assumes that consumers treat changes in time and availability in a consistent, linear way. For instance, under DEU, a one-week delay in getting a product is a consistent level of “penalty,” whether it is a new delay that takes away immediate availability, or an extension of an already existing wait.

In reality, the typical consumers will be reluctant to wait for one week instead of getting the product today, but may not mind waiting for four weeks instead of three.

Additionally, a customer is going to feel a risk more keenly in a product selling out when it was previously perceived as fully available than when the chances of getting it looked remote to begin with.

In summary, the buy-or-wait decision is a multidimensional trade-off and by factoring in how consumers weigh risks and delays, dPTT creates a pricing model that can be more credible and useful.

Manel Baucells is UVA’s Darden School of Business - Associate Professor of Business Administration

Published on November 03, 2016

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