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How to Conduct Pricing Research

 pricing research

By Rajan Sambadnam, PhD, TRC's Chief Research OfficerQuirks featured author

“How much should I charge for my product?”

This is one of the most important and difficult questions facing a marketer. Charging too much (thus attracting too few customers) could be as costly as charging too little (leaving money on the table). The good news is research can help understand consumers’ willingness to pay (WTP) for a product. But a practical issue researchers face, is deciding on the right pricing approach to use. So many survey-based approaches are available – monadic, sequential monadic, Van Westendorp, conjoint analysis, etc. – that it is easy to be confused. What would be useful for a practical researcher is a simple framework for thinking about pricing research.

At its core, what a researcher is really trying to understand is what a consumer is willing to pay for a given product. Broadly speaking, this can be approached in two ways – direct and indirect elicitation. In the direct approach a product description of some kind is provided (without a price) and consumers are asked what price they are willing to pay for that product. In the indirect approach a product is provided (with a price) and consumers are asked about their likelihood to purchase that product. Pretty much all survey-based pricing approaches are some variation of these two approaches.

Pricing Research new product research

Which one should a researcher use and when? There is a bit more nuance and detail involved in that decision, so let’s take a closer look.

Direct Elicitation Pricing

In the simplest variant of direct elicitation, a single product (or service) description is provided and consumers are asked (in an open-ended manner) for the price they are willing to pay. Though the implication is that they are willing to pay a certain price to purchase the product, it is not stated explicitly, hence the focus is squarely on the price. This can subtly place more emphasis on price rather than the value provided by the product. But an advantage of this method is that it requires very little effort from the consumer. Since an open-ended question is asked, a wide range of responses are possible. Calculating the WTP as the average price is straightforward. Alternatively, guidance could be provided in the form of a range of prices for the consumer to choose from.

There is research to show that direct elicitation tends to provide biased responses (perhaps because of the focus on price). So, the situations where this approach is used should be chosen with care. The most obvious case is early in the product development process. A company may have developed a new product concept, and is interested in the price the market would pay. The concept may not be fully developed and hence the features would be ill-defined. Trying to get a precise measure of WTP would not be appropriate, so a method that gets at ballpark pricing would be sufficient. In such cases, direct elicitation of WTP would be simple to administer and also efficient from a research cost perspective.

There are more complex direct elicitation approaches possible, and one that is used in practice is called the Van Westendorp Price Sensitivity Meter. Rather than simply ask about WTP, in this approach a series (of four) questions is asked, regarding prices at which the product seems (too) cheap and expensive. The intersection of the various curves is used to determine an optimal price for the market as a whole. While multiple questions are asked, the focus is again on the price. Though the method is more complex than simple elicitation, it is not clear that it produces a clearly superior answer.

Since the direct approaches focus on the price of the product, there is no information on what happens when features change, the impact of competition, or how WTP translates to sales. Given these drawbacks, the direct approaches are more useful in the early exploratory stage of product development where the priority is to get a ballpark price range.                       

Indirect Elicitation Pricing

The most straightforward change that would make an approach indirect is to attach a price to the product description and ask how likely a consumer is to purchase that product. This small change shifts the focus from price to the value inherent in the product. Further, it provides a better (though still biased) view of sales this product will likely garner, and therefore is a more useful metric for the marketer. Asking about likelihood to purchase makes less sense early in the product development cycle. The more clearly defined the features are (i.e. the further it is in development), the easier it will be for the consumer to provide a realistic answer.

Though it moves the focus from price to purchase (while still providing pricing information for the researcher), this method still suffers from the other flaws mentioned before. But it is at least possible to get at the issue of price sensitivity by using a monadic approach (sometimes called A/B testing). Here, the same product is shown to two similar groups of consumers at different prices and demand is estimated. When more than two groups are used it can produce a nice (downward sloping) demand curve with a useful property – identification of potential kinks or non-linearities that can suggest interesting price points. The downside is that it comes at a cost in terms of the sample size required across all the groups in order to get robust results. One variant used in practice is called sequential monadic or price laddering (or contingent valuation in the academic literature). Here a single cell is used and if a respondent indicates a low willingness to buy at the given price, a lower price (or two) is offered. The increase in demand across the prices indicates sensitivity, though of course, the later price estimates are biased because of prior exposure.        

None of the methods mentioned so far get at the root of the problem – which is the relative realism of the pricing research. Direct elicitation of WTP is the least realistic and provides the least information. Use of purchase likelihood is somewhat better, but does not take competition into account (as happens in real life). To get over this hurdle one could place the target product in a competitive setting (such as a simulated grocery shelf) and record how often it is chosen. But now there are several additional variables introduced into the mix and we cannot be certain about their impact on the demand for our product. What is really needed is an approach that maintains this realistic setting, but still provides pricing information in a systematic and effective manner.

That is exactly what conjoint analysis does.

Conjoint Analysis and Pricing

This is really a family of techniques, but the most popular variant is called Discrete Choice. As the name implies, consumers are shown sets of products and asked to choose the one they are most likely to buy. This is quite similar to the behavior they would exhibit in a real buying situation. To make the process even more realistic, the choice task usually includes a “None” or no-choice option, which can help increase the accuracy of demand and hence price estimation.

In a typical conjoint exercise, products are described by attributes (often including brand and price), each with two or more levels. By combining various attribute levels, products can be formed and displayed to consumers as choice options. Choices made by consumers provide information on what is important to them. The choice tasks are created using an experimental design so as to extract maximum information. For example, a high-quality high-price product might be shown with a low-quality low-price product. There is no obvious “right” answer and hence the choice made by the consumer provides information on what she values. But if the choice had been between a high-quality low-price product and a low-quality high-price product, then the information value of the choice is minimal (as everyone would choose the former). By providing the consumer with a series of such choices and forcing her to think and trade-off between features, conjoint analysis is able to gather information on what is truly important to consumers.

Price is one of the features included in the exercise, but not the only one. It is combined with other features and together they are displayed as a set of complete products, thus reducing the focus on price as compared to the direct elicitation methods. Thus the demand estimated at various price points through this approach tends to be more accurate. The output is usually provided in the form of product shares which can be easily understood by all constituents. A simulator can generate what-if scenarios when product features and prices are changed, thus providing the kind of market place simulation not possible with any other pricing approach.

The conjoint approach does have some disadvantages. Multiple screens of products need to be shown to respondents and if the number of attributes is large it can make the exercise tedious. Though the method is robust and is shown to have practical value, it is complex in terms of design and analysis and usually requires specialized support. Hence it is not as simple as using direct elicitation and reporting a single WTP number.    

Recommendations for Pricing Research

We started with this question:

How much should I charge for my product?

To identify the appropriate research needed to answer this question, we first need to understand where the product is in the development process. Early in development, when the product is mostly conceptual and fuzzy, accurate pricing information is neither attainable nor desirable. So, a direct elicitation approach may be best, while being cognizant of the ballpark nature of the pricing. This also has the advantage of keeping the research simple, quick and economical.

If it is later in product development when the features are firmed up, conjoint analysis (generally discrete choice) would not only provide good information on pricing, but also identify attractiveness of various product features. In fact, there are could be two ways in which conjoint analysis can be used, if we can consider the middle and final stages of product development to be distinct.

In the middle stage, conjoint analysis can be focused on the features and price of the target product and not on brand name and competitive dynamics. Survey respondents make trade-offs and identify important features, indicating their willingness to pay. If the company is planning on introducing a new or modified feature, this stage can identify whether it has inherent value and how much. Longer lists of features may be more appropriate in this stage.

A second conjoint analysis can then be used in the final stage of product development. The focus here is on market dynamics rather than understanding the feature and price trade-offs. Hence, appropriate competitive brand names, features and prices are included while keeping the list of features short. Once data are collected, market simulation becomes very important and can identify (through share changes), what optimal price to charge under various scenarios. The market simulator can not only identify the price that can maximize share, but can also reveal the price at which revenue and even profit can be maximized. This is one of the greatest advantages of using conjoint analysis in pricing research.

If the product is fully baked and the only objective is to get a good sense of its market appeal indirect elicitation can also be used here. A single product fully described (including price) is shown to respondents and purchase likelihood is measured. The score is easier to interpret if normative data are available for comparison.    

Ultimately, there are many ways of doing pricing research using surveys, but thinking about it systematically, using the product development framework can help a researcher choose the right approach.

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