Author: Dean Tindall
Often when it comes to setting pricing for products businesses can be faced with a large number of unknowns. New products, new markets, new competitors, even new prices can mean that the information which companies hold on actual market responses to previous price changes is sub-optimal.
You could conduct in-market pricing tests to vary your price and capture sales data from real paying customers, but this can be an expensive and risky route. Plus, what your competitors do during your in-market test could skew your pricing experiment.
You could analyse past sales data to calibrate the optimal price if we have a database with a history of prices and sales volume for your product and competitors.
But it is not that simple. Your existing data might not have enough independent price changes to stabilize the kinds of predictive models needed to pinpoint optimal price points and optimize revenue or profit.
When faced with sub-optimal internal data and a series of unknowns, it can often be useful to de-risk any decisions made on the pricing of products by undertaking market research, and specifically using survey-based pricing techniques.
In our session we will talk about the pros and cons of four of the main techniques used by practitioners today.
Survey Research Methods for Measuring Price Sensitivity
Survey research enables you to evaluate a range of products at different prices, measuring the price sensitivity for consumers and key market segments—before you go to market. However, pricing tasks in your survey need to be realistic, and your respondents need to be representative of the target market in order to gain accurate data about how price impacts their likely choices.
Consider the following popular traditional pricing research methods that have appeared over the years.
Monadic Price Experiments
In Monadic Price Experiments respondents are presented with a product, or a series of products, at one of the price points you are interested in testing, and asked to state how likely they would be to purchase that product at that price.
Different pricing scenarios are presented to different respondents, and we can identify how price drove a difference in their reported behaviours. This isolates the reactions you are seeing to respondents who only saw a single price point for your product.
By combining the reactions to each of the pricing scenarios, we can draw a demand curve and identify any price sensitivities which may exist.
Van Westendorp’s Price Sensitivity Meter
VWD analysis involves asking four key questions about your product. Introducing them to the product, before asking them to state when they thought the price would be “Too Cheap,” “A Bargain,” “Expensive” and “Too Expensive.” The cumulative frequencies of these price points are charted across all respondents, with the resulting chart yielding what is said to be an acceptable range of pricing, alongside specific points of interest such as the “Indifference Price Point” and the “Optimal Price Point.”
Gabor-Granger Pricing Method
This approach to pricing research involves asking respondents if they would be likely to buy a product at a given price. If they say “yes,” then we ask the question again at a higher price. If they say “no,” then we ask the question again at a lower price. The initial price point can be set at the mid-point of the pricing range or seeded randomly to avoid anchoring effects.
Gabor-Granger techniques allow you to easily create a demand curve based on respondent reactions to the varying prices which were shown.
Choice Based Conjoint Analysis
Conjoint analysis is an advanced technique that creates questions which systematically vary a range of products and prices before presenting them to respondents. Here respondents can pick which product they would be most likely to choose in each of the carefully rotated scenarios.
Based on how respondents react to price and other feature changes, you are able to generate a model that reveals the price sensitivity and willingness to pay which each respondent exhibited across the tasks.
This model can then be used to create a “market simulator” which can help you to understand what the level of demand which is likely to be generated across a range of pricing and competitive scenarios.
Understanding the strengths and weaknesses of each of these approaches is vital to ensuring that you are capturing data which is going to a net benefit to any pricing decision you make.
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