There are few levers as powerful as pricing when it comes to driving immediate impact to your bottom line. While most new revenue initiatives take months before generating a positive ROI, marginal price increases can boost profits and market share within weeks, or even days, of implementation. Yet, too often, marketers remain cautious about price, hesitant in pursuing these low-hanging opportunities for fear of turning away prospective customers. The result is suboptimal, with companies overestimating price sensitivities and leaving significant money on the table.
According to a recent Bain & Co survey of more than 1,700 companies, roughly 85 percent of respondents believe there is significant room to improve their pricing decisions. With the rise of aggressive pricing innovation from companies like Amazon and Uber, and Adobe and Spotify, price sensitivity is increasingly a myth that companies cannot afford to bear. As consumers embrace new dynamic pricing and subscription models, marketers can no longer simply extrapolate historical trends to predict what customers may be willing to pay in the future.
Fortunately, there is an array of market research tools that can help companies move beyond guesswork or trial and error when it comes to price. Whether your company is launching a new product, or looking for a more profitable way to reset your current customer relationships, these methods have a vital role to play in making sure your company captures the full value of your product and service offerings.
Gabor-Granger Pricing Method
First developed by Andrew Gabor and Clive Granger in the 1960s, this classic pricing technique helps marketers discover the optimal price point that will maximize revenue from any given product or service. The test uses a sequence of “yes/no” questions to gauge respondents’ likelihood of purchasing a product or service at various price points. Since respondents are expressing a clear intent to buy, the resulting data offers a strong indicator of how customers value a product or service, and how sales might fluctuate as price increases.
The Gabor-Granger method allows marketers to:
- Verify customers’ willingness to pay more for a product or service
- Determine how changes in price might affect demand elasticity
- Identify revenue-maximizing price points
Due to its binary structure, the Gabor-Granger method is most helpful for products or services with fixed attributes. Marketers should also keep in mind that these are directional findings, which may not fully reflect the potential impact of competitors’ pricing on actual behavior. For that reason, researchers often combine this method with other tests, such as conjoint analysis (mentioned below).
Van Westendorp Price Sensitivity Meter (PSM)
Named after Dutch psychologist Peter van Westendorp, who developed his model in 1976, the Price Sensitivity Meter uncovers the full range of prices that customers may be willing to pay for your product. The test asks respondents to self-identify the prices at which they would consider a product or service: too cheap, a bargain, pricey (but not unreasonable), or too expensive. This data reveals a more sophisticated picture of consumer preferences, though one that is not as explicit as the Gabor-Granger purchase meter.
PSM data helps marketers:
- Assess the acceptable price range for a product or service
- Identify outer bounds of marginal cheapness and expensiveness
- Approximate the optimal price point given current market preferences
The major assumption underlying PSM is that respondents possess a certain level of knowledge about the market and its various offerings, which is true in the vast majority of situations, but may not be the case with a new product introduction. Overall, PSM reveals opportunities where customers may be receptive to higher price points or premium alternatives.
Conjoint analysis has become an essential research tool that informs everything from advertising messages to product development to supply chain optimization — and it also has a key role to play in pricing. The technique uses a variety of multiple- or discrete-choice surveys to gauge how respondents value different product attributes or features. In contrast to simpler surveys that ask respondents about specific items, this multi-attribute model approximates real-world decision making and tradeoffs.
When applied to pricing, conjoint analysis can help companies:
- Assess how pricing strategies perform within a competitive set
- Uncover new or unacknowledged drivers of customer preferences
- Identify which features correspond most closely to higher or lower prices
This advanced statistical method can be more complex to design, but new services like Conjoint.ly have accelerated the speed and ease of running such tests. Sometimes marketers pair the analysis with a qualitative component, which can help round out the picture as to why respondents made the choices they did.
While conjoint analysis offers a realistic scenario of real-world tradeoffs, some marketers may prefer to additionally verify their findings with live, in-market A/B tests. These tests circumvent the problem of customers not always behaving in accordance with their expressed preferences in surveys or focus groups. Real-world trials can be expensive to execute, but the rise of digital marketplaces and cloud-based research platforms have greatly lowered the cost and improved the speed at which marketers can test and verify their pricing hypotheses.
Each of these methods have their unique use case, but if implemented correctly can provide the most precise and direct data to correctly price products and maximize revenue. Industries who want to capture the market share must know that only through on-the-ground-research will they grow to understand just exactly what their customers want and need.
To find out how you can test and implement pricing methods into your research scope, request a demo with Fuel Cycle today!