Comparing Price Methodologies – aytm Help Center

Pricing research is an approach that seeks to determine consumers’ willingness to pay for a product or service. The goal is to determine the optimal price point to maximize profit, revenue, and market share. However, we recommend against straight “willingness to pay” questions as consumers tend to underestimate how much they would pay and there are many more sophisticated pricing research methodologies that are relatively easy to set up and interpret.

Here are a few common pricing methodologies, and some of the pros and cons of using each.

Van Westendorp/Van Konan

aytm's Van Konan Price Optimization Model layers revenue and profit views onto the traditional Van Westendorp model, if one can provide assumptions for cost of production, TAM, etc.

In a van Westendorp exercise, survey respondents are asked four questions:

At what price do you think the product/service is priced so low that it makes you question its quality?
At what price do you think the product/service is a bargain?
At what price do you think the product/service begins to seem expensive?
At what price do you think the product/service is too expensive?

Responses to each question are plotted as cumulative percent line graphs and, by looking at where specific lines intersect, we can identify an Optimum Price Point as well as a Range of Acceptable Prices.

Pros	Cons
VW is pretty simple to execute. VW is a good option to consider when the you do not have existing intel/good starting point with regard to potential price points. VW works well for established and frequently purchased categories (e.g., food/beverage/other CPGs). Does not require a special sample size considerations as all respondents provide data for all questions.	Assumes that the category has a price below which it is so cheap you would question quality; this is not necessarily a fair assumption for every category. For emerging categories, luxury, and tech, VW can yield low-ball estimates. Consumers do not always have the best sense of what they would actually be willing to pay for emerging products AND they may vastly underprice expensive products.

Monadic Price Test

In a monadic price test, a range of prices are tested (usually 5 or 6 price points, evenly spaced) and respondents are randomly assigned to evaluate only one, in the form of purchase intent (scaled) or purchase choice (between multiple products – statically priced competitors + the product of interest). By charting differences in a) purchase intent/purchase choice and b) estimated revenue per XX (e.g., per 100 gen pop consumers) for each price point tested, we can identify the price point that maximizes market share and/or revenue, and examine price elasticity. Typically a range of price points are tested in this type of study.

Pros	Cons
Low respondent bias - monadic design eliminates respondent bias towards pricing because respondents only evaluate ONE price point. They are not aware of the other prices being tested and evaluated by other respondents. Results of monadic studies are often pretty clear cut and provide strategic direction without ambiguity.	Requires a larger sample size than other approaches because of the monadic design that necessitates a minimum number of cases per cell. As a result of the larger sample, monadic studies can be more expensive.

Price Ladder, Gabor Granger

Similar to a monadic price test, conceptually, the primary difference with Price Ladder and Gabor Granger is that respondents don’t evaluate just ONE price point. In both of these exercises, respondents are asked to indicate their purchase intent (scaled) or purchase choice (between multiple products – statically priced competitors + the product of interest) at various price points. By charting changes in a) purchase intent/purchase choice and b) estimated revenue per XX (e.g., per 100 category purchasers) for each price point tested, we can identify the price point that maximizes market share and/or revenue, and examine price elasticity. Like monadic price tests, a range of price points are tested.

Price ladder: Respondent starts by evaluating the highest price point (e.g., “How interested would you be in purchasing this product, if it cost <highest price point>?“). If they are willing to purchase, they are discontinued from the exercise, but coded as a “YES” for each subsequent lower price point. If they are not willing to purchase at the highest price point, they are shown the next highest price point and asked to provide PI again. (Rinse, repeat through full range.)
Gabor Granger: This exercise is a variation on a standard price ladder (which starts at the top of the range and goes down). In Gabor Granger, respondents are randomly assigned to evaluate one price point (e.g., “How interested would you be in purchasing this product, if it cost <randomly assigned price point>?“). If they are willing to purchase, they are next shown a higher price point; if they are not willing to purchase, they are next shown a lower price point. (rinse, repeat through range).

Both Price Ladder and Gabor Granger are variations on a monadic price test and are generally considered inferior to the more “pure” monadic test, but require a far smaller sample size as each respondent ends up with a value for each price point tested. If sample costs/feasibility are issues, Price Ladder is recommended over Gabor Granger.

Pros

Cons

Requires a much smaller sample size than monadic price testing, while allowing you to produce the same share, revenue, price elasticity metrics that a monadic test does.

Price Ladder: Respondents may “figure out” the test after a few price points and may eventually indicate that they would purchase simply to exit the ladder (e.g., not because they would actually purchase the product at the lower price point). To combat this, consider fielding a price ladder among category purchasers, those who show a baseline interest in the product, etc., as opposed to a general population sample.
Gabor Granger: Evaluating a lower price point may “spoil” a respondent for higher price points. Respondents may rationally be inclined not to endorse a higher price point after seeing a lower one, as they would prefer the lower price point. However, it is possible that if they were only presented with the higher price point, their purchase interest would still be high. In other words, Gabor Granger may invite price comparisons that can impact the data in undesirable ways.

Conjoint

The most flexible option for price testing – allows you to assess market share at various price points (any price point in a range), as well as revenue per XX (e.g., per 100 gen pop consumers) and price elasticity. Conjoint also allows you to assess: A) the importance of price relative to other qualities of a product or service (e.g., for something like a computer, the importance of price relative to operating system, memory, processor speed, etc.) and B) the actual value of specific features. To conduct a successful study, a range of price points should be tested. In addition to price, at least one other product variable should be tested (conjoint requires a minimum of 2 variables of interest – price is only one variable).