Pricing Optimization

Basic economic theory states that the optimal price of a product or services is driven by supply and demand. It is easy for a supplier to establish minimal price - by calculating the cost of providing the product, and adding a certain level of profit margin. However, it is much more difficult for a supplier to ascertain what price the market would bear, and how it would react to a product's pricing within its competitive set. Pricing research can fill in that information gap.

The 4 most common methods used to conduct research to determine pricing optimization are the following:

Monadic price testing:

In this type of research, respondents are presented with a product and are asked their likelihood of purchasing the product at a specific price, which is set in advance during the questionnaire design. The scale of the answer should preferably be either a Likert scale or a numeric scale.

Since respondents understand that their answers to surveys can affect future marketing decisions, they tend to respond in ways that they believe would depress future prices of the product being studies. Moreover, the researcher often needs to study several price points to measure consumers’ level of price sensitivity. Consequently, monadic price testing research often splits the sample to several cells, where each cell is asked one specific price point. It is important that each cell is the same sample size, and that fairly large samples be employed in each cell. Otherwise, the variability of answers at each price point can lead to unusual or non-sensical price sensitivity findings, such as respondents being more likely to buy at higher prices. During analysis, the optimal price point is determined to be the point where the proportion of respondents likely to buy the product is equal to the proportion of respondents that are not likely to buy the product.

Monadic price testing is ideal to use when: the product tested is basic in nature (i.e. lacking many different adjustable components); there are few price points to study; respondents are not experts on the product class; the universe is large and easy to recruit (such as the general population); the price range on which the product would be sold is already known; the supplier needs a superficial understanding of the market.

Price laddering testing:

Price laddering testing is somewhat similar to monadic price testing, as respondents are presented with a product and are asked their likelihood of purchasing the product at a specific price. However, whereas in monadic price testing research respondents are only asked one price point, respondents in price laddering testing research are asked about multiple price points.

Typically, the sample is split into two cells, where one cell is first asked about the lowest price point (pre-set during the questionnaire design), while the other cell is first asked about the highest price point. Those in the first cell who indicated a high likelihood of purchase are asked again at successively higher price points. Meanwhile, those in the second cell who indicated a low likelihood of purchase are asked again at successively lower price points. For the same reasons described with monadic price testing, both cells need to have a large enough sample size to reduce the chance of unstable findings. However, since price laddering testing only requires two cells, it does not require as large a sample size as monadic price testing. A disadvantage of the price laddering technique is that answers for subsequent price points may be biased by their exposure to the first price point. Therefore, the number of levels to test with each respondent must be limited. As with price monadic testing, the optimal price point is determined to be the point where the proportion of respondents likely to buy the product is equal to the proportion of respondents that are not likely to buy the product.

Price laddering testing is ideal to use when: the product tested is basic in nature (i.e. lacking many different adjustable components); there are many price points to study; respondents are not experts on the product class; the universe is either smaller or difficult to recruit; the price range on which the product would be sold is already known; the supplier needs a superficial understanding of the market.

The Van Westendorp price sensitivity meter (PSM) model:

Whereas the first two pricing research techniques asks for respondents’ feedback to pre-set questions, the PSM model uses the four following open-ended questions:

  • At what price would you begin to think the product is too expensive to consider?
  • At what price would you begin to think the product is so expensive that you would question the quality and not consider it?
  • At what price would you begin to think the product is getting expensive, but you might still consider it?
  • At what price would you think the product is a bargain - a great buy for the money?

Since the questions are asked open-ended, respondents need to have enough knowledge of the product class to provide credible answers. However, there is no need to split the sample into different cells, and there is therefore no need for a large total sample size. PSM is ideal to use when: Respondents are experts on the product class; the universe is either smaller or difficult to recruit; the price range on which the product would be sold is unknown; budget to run the pricing research is limited; the supplier needs a superficial understanding of the market.

Choice-based conjoint (CBC):

CBC is considered the most accurate pricing research method, as it most closely simulates respondents’ purchasing decision at the point of sale. However, CBC can also be the most difficult to set up and analyze, and they are most time-consuming type of survey for respondents to complete.

In a CBC exercise, respondents are provided with a set of imaginary products, each of them described by a variety of attributes. Within each attribute, a range of alternative levels is defined so that each level spans the realistic range of realistic offerings of the product. For example, an attribute may be “colour”, and its levels may be “red”, “green”, “blue”, and so on. Of course, price would be a critical attribute to include, and the levels would constitute of the pre-set price points.

By analyzing which items are chosen or preferred from the product profiles offered to the respondent, it is possible to work out both what is driving the preference from the attributes and levels shown, but more importantly, give an implicit price valuation for each attribute and level. The resulting analysis provides a detailed picture of how customers make decisions. This can include a brand equity valuation (i.e. how much a brand name is worth in relation to other brand names). More importantly, the analysis derived from CBC research can provide a simulator that predicts the preferences of consumers in existing or new competitive market conditions, and which tests the impact of any product change in consumer preferences. CBC requires sample sizes of at least 300, but an alternative form (A-CBC) can be used for smaller sample sizes.

CBC is ideal to use when: the product tested is complex in nature (i.e. with many different adjustable components); there are 4 to 5 price points to study; respondents are not experts on the product class; the price range on which the product would be sold is already known; the supplier is looking to introduce a new product into the market; the supplier needs an in-depth understanding of the market.

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