usinesses often must set prices and forecast demand without a shred of historical data or relevant comparables — in everything from consumer goods in emerging markets to innovative high-tech products for global markets.
Confronted with these challenges, product and marketing professionals frequently turn to research techniques that add considerable bias when probing a customer’s willingness to pay. The bias rushes in when the research doesn’t simulate actual purchase decisions. Because of its speed and low cost, one of the most common research techniques that companies use is contingent valuation. With this approach, researchers simply ask customers what they would be willing to pay based on a detailed product description and/or list of features. The results, however, assume that customers know what they are willing to pay for a product absent any comparables or other context. But customers often don’t know. When Xerox first tested the idea of a photocopier, for example, it found that customers placed little or no value on the new idea.
In this article, we look at how discrete choice analysis can address two common high-stakes pricing decisions.
Understanding context is critical. A single feature may capture a customer’s fancy and cause potential purchasers to give it a higher value than they would in an actual purchase decision. For instance, consumers may say they value a certain feature of a pain killer such as how quickly it takes effect or its lack of side effects. But the equation can change completely when the context moves from a simple headache to severe arthritis. By the same token, customers may be very enthused about new features in a smartphone but balk at the time of purchase when they discover the company doesn’t provide temporary replacements when the phone needs servicing. In order to reliably discover what a customer is willing to pay, the research must simulate customer decisions in the context customers make them.
Discrete choice analysis does precisely that. With this approach, respondents must choose between competitive products and/or features in a series of actual purchase scenarios that would be confronted in real life. Customers are asked to look at each scenario and answer the basic question: If these were all the choices available, which would you choose, if any? With this knowledge, companies can avoid the costly mistakes of over- and undervaluing products and features. But more than that, marketers can gauge price elasticity and simulate market response even in the absence of historical data or comparables.
In this article, we look at how discrete choice analysis can address two common high-stakes pricing decisions: when a feature is deeply embedded in a complex product or bundle; and when an incumbent product faces substitutes that threaten its revenue stream.
PRICING IN COMPLEX PRODUCTS AND BUNDLES
When product features are embedded in complex offerings, it can be immensely difficult to ferret out how one feature affects the overall value. Be it a new channel in a cable package or extended battery life in a smartphone, the interrelationships among features, attributes and the context of the customer’s decision are intricately interwoven. The UK’s Public Performance Limited (PPL) faced a particularly complicated version of this challenge: It needed to know how much value nightclub patrons placed on recorded music.
PPL licenses copyrighted material and collects royalties on behalf of copyright owners. For recorded music, PPL had based its rates for nightclubs on past license agreements that weren’t grounded in an understanding of how much customers were willing to pay for music. Although customers often pay a cover charge to enter and then buy drinks, they aren’t asked to pay specifically for the recorded music being played — even though it is a key decision factor. As a result, PPL had no historical data or comparable to examine. PPL had set rather low hourly licensing fees at approximately £0.03 (U.S. $0.05) per patron. To determine if these fees truly reflected what customers were willing to pay, the organization turned to discrete choice analysis.
To begin, the research defined a clear set of contexts to simulate the purchase decision. Participants were asked to imagine that they were on a normal night out with friends and had decided to go to a nightclub. Each was paying their own way. In the survey, participants were told to pretend that they and their friends were standing on a street with three nightclub choices. The only differences among the clubs were the attributes tested. There were no additional special promotions or guest appearances. To eliminate bias from latent attitudes about other entertainment selections, participants could opt for none of the options.
The research then offered carefully crafted choices to hone in on a consumer’s willingness to pay for the recorded music. When asking about the type of music, for example, the options included “music you like” vs. “music you don’t mind.” (The second choice eliminated the possibility that respondents rejected an option because they had specific music in mind.) The scenarios included the presence of a DJ to determine whether a customer valued music as more than background. The choices also spanned a range of cover charges to further zero in on the music’s value.
By testing these variables in the context of actual purchase decisions, PPL discovered that the license fees it had set were only a fraction of what customers were prepared to spend. The median nightclub customer was willing to pay £ 2.01 per hour (U.S. $ 3.15) more to enter a nightclub if there was a DJ playing music they liked vs. the prevailing fee of only pennies per hour.
THE SPECIFIC ATTRIBUTES TESTED WERE
TYPE OF MUSIC
The choice of music included “music you like” and “music you don’t mind,” the latter eliminating the possibility of rejecting an option because of the specific music
PRESENCE OF A DJ
The use of a DJ indicates the value of music is more than background — someone at the establishment is curating a recorded musical experience..
The cost of admission is called a cover charge.
WHEN PRODUCTS FACE DISRUPTION
Products and services continually are under the threat of disruption. When such an event does occur, pricing is a vital tool to maximize the revenue stream of an incumbent product as the company forges a path to a new business model. A major music publisher confronted this reality in the wake of illegal peer-to-peer file exchange services. It needed to know if reducing the price of its products, both at retail and online, would improve the revenue picture as it navigated major changes in the music industry.
Management’s instincts were to reduce — even slash — prices as many consumers started to share free downloads. The instinct seemed intuitive enough, and traditional pricing research, the company assumed, probably would have validated it. Customers likely would report that as the price goes down, their purchase intent goes up. Misreading price elasticity, however, would result in dire consequences for the company’s survival. To answer the pricing question, the publisher turned to discrete choice analysis.
The global study included Internet users and non-users. Format choices encompassed physical CDs bought in stores, downloaded music files and the right to make a copy of someone else’s CD. The scenarios also tested the number of musical tracks included at different price points, as well as the inclusion of bonus materials. After ranking the value of these features, participants were asked to choose between a few different options. Perhaps most important, the prices tested included $0.
Management’s instincts turned out to be dangerously wrong. Although there was evidence that customers would purchase more at lower prices — not all would take advantage of illegal file sharing — simulating the purchase decision revealed a fairly inelastic price demand: The increase in unit sales at lower prices would not outweigh the decline in unit price. Had the company acted on its instincts instead of executing discrete choice analysis, revenues would have plummeted.
HOW WILL THE MARKET REACT?
In many high-stakes pricing decisions, the rubber hits the road when companies must translate pricing research into sales and market share forecasts. Imagine (before mobile phone service became commoditized) that a telecom provider made pricing decisions and simulated market behavior based on what customers said they might pay — absent the actual context of that purchase decision. Markets were fast moving, competition was fierce and the possible scenarios to beat competitors were nearly endless. Companies were trying a variety of price plans, including a myriad of special incentives. On top of that, new mobile phone features were continually hitting the market, creating an even broader range of options. Discrete choice analysis was a key tool: Companies were able to use pricing research to simulate market response in a competitive reality that left little room for error.
The music publisher also had little room for error. By using pricing research grounded in actual decision contexts, management could estimate the market share of different music products and services under various scenarios. Most important, the company could model the future evolution of the physical and digital music industry down to individual customer segments.
Many markets continue to be competitive and fast moving. Cable providers must develop product strategies, determine the optimum channel packages and set pricing in the face of new entrants; e.g., Netflix and Hulu. Airlines are competing for premium cabin passengers and must understand the consequences of adding premium touches to existing economy offerings. The list could go on. When the stakes are high and room for error is low, pricing research grounded in actual purchase decisions is an invaluable tool in the marketing arsenal.