Price determination – methods for optimal pricing decisions

As one of the most important value-added processes in companies, considerable attention should be paid to the process of pricing and thus to optimal pricing decisions. Seven essential pieces of information have to be included (7C of the pricing process), which are described in the article Price optimization – The role of modern methods for market success. There are different methods to determine the optimal price. In the article Optimal pricing – The best methods for the pricing process the methods around the topic observation are deepened, while in this article we will take a closer look at different methods around the topic surveys for price determination.

There are various approaches to price determination through surveys, including

• Direct surveys (including Price Sensitivity Measurement)
• Conjoint Measurement
• Focus groups
• Expert assessment

For pricing decisions based on observations, the following methods are described in a separate article:

• Price experiments
• Econometric analysis of market data (including online auctions)
• Social Listening (Voice of consumer analytics)

Direct price survey

The direct price survey is a simple method to determine the willingness to pay in a customer interview. In the simplest variant, consumers are asked directly how they react to certain prices or price changes. A common questioning technique is: What is the maximum amount you are prepared to spend on the product? The interviewee only indicates his willingness to pay the price. Price sales functions are derived from these answers. Due to its serious disadvantages, the direct survey approach is only suitable for price management to a limited extent. There is the risk of wrong decisions regarding price. An exclusive use of the direct method is therefore not recommended.

Open Line Pricing (OLP)

Advanced methods such as Open Line Pricing are based on the basic principle of direct price surveys. They are modified to some extent to significantly increase the validity of the data collected. Open Line Pricing (OLP) is a very simple market research method for determining willingness to pay for price determination. The survey refers to price ranges that potential customers expect for a product. The advantage over direct price queries is the differentiated indication of two price points. The reference to a price range results in a more intensive examination of the question of maximum willingness to pay.

Aided Open Line Pricing (AOLP) represents an extension of OLP. The respondent is additionally shown the prices of competitors for a comparable product to support pricing decisions.

Gabor-Granger method for price determination

The Gabor-Granger method is another approach to price determination and identification of willingness to pay by means of direct price enquiry. Similar to the OLP method, this method also deals with price thresholds. However, instead of asking about expectations, the system asks about purchase probabilities. The survey participants indicate the probability with which they would buy a certain product at a specified price. A typical question is: Assuming you are faced with the decision to buy a new smartphone. What is the probability of buying the iPhone X from Apple at a price of 1100 EUR? What is the probability to buy the iPhone X from Apple at the price of 1200 EUR etc. The respondent indicates the probability on a scale of 1 (very unlikely) to 5 (very likely). For all price points within a realistic range of the product, the question is repeated structurally. Purchase probabilities and price sensitivities can be derived from the answers. The Gabor-Granger method does not establish a direct correlation with the competitive environment. The major disadvantages of all direct price queries also apply to this approach.

Price Sensitivity Meter

The van Westendorp method (Price-Sensitivity-Meter, PSM) is a further development of the direct price survey. Current or potential customers are confronted with four questions about a specific offer. A survey sequence within an interview can be as follows: You are about to make the decision to buy a new smartphone:

• At what price would you rate the offer as “too low” so that you doubt the quality?
• Up to what price would you consider the offer to be reasonable (as a good deal)?
• At what price would you describe the offer as expensive but still consider buying it?
• At what price would you describe the offer as too expensive and not consider buying it?

A special focus is on the graphical evaluation of the four price questions. The answers of the respondents are visualized in a two-dimensional diagram. The original method refers to the points of intersection of the different curves. The main disadvantages of the PSM are:

• No consideration of the competitive situation
• No inclusion of product characteristics
• Strong focus on price (overvaluation of willingness to pay)
• Lack of scientific basis for the price recommendations.

The intersection points of the curves are not relevant for an optimization with regard to target variables such as quantity, turnover or profit. A price optimization is related to the best possible achievement of goals such as sales, market share, turnover or profit. In PSM, however, there is no connection between the intersections of the curves and the achievement of economic goals.

Adaptive PSM

An adapted version of the PSM has proven itself in project practice. This variation uses all four original questions in the data collection phase. As a result, a greater validity of the data is achieved compared to the single price query. In the data evaluation, the adaptive approach concentrates on the answers to those price questions that aim for maximum price readiness: Question 3 (purchase is still being considered) and question 4 (no purchase because maximum willingness to pay has been exceeded). Of highest relevance for price management is the maximum acceptable price threshold of each individual customer. From the detailed information given in questions 3 and 4, a price threshold per person can therefore be identified which should not be exceeded. From this, an> aggregated price sales function can be derived for a segment. A profit-optimized price can be determined from the sales curve, taking costs into account. The adapted version of the PSM technique should always be used parallel to other methods as part of an integrated optimization approach. In the further course of the series of articles, the combination of different methods within the framework of an integrated approach will be shown (cf. Digital Pricing, Frank Frohmann, 2018). Further price-psychological correlations and budget restrictions must also be taken into account. The four outlined questions alone are not sufficient.

Conjoint Measurement

The basis of professional pricing decisions is the knowledge of customer needs. Pricing should not be considered separately, but always in relation to the value drivers. In the real purchase situation, a customer never makes a decision based solely on financial aspects – he weighs price and perceived benefit against each other. Conjoint Measurement carries out this trade-off. The decision-making process of buyers is depicted realistically.

The special characteristic of the method is the survey technique. The survey participants are repeatedly presented with selection decisions. The test subjects are confronted with alternative product-price profiles. These profiles correspond to combinations of different characteristic values including different prices. The effects of different pricing decisions on the company’s goals can be deduced from the customer’s preferences for the various product-price alternatives.

The most important steps in measuring price effects are:

• Defining the characteristics and the values for each characteristic
• Design of the interview
• Conduct of the survey
• Analysis of the part worth
• Estimation of the importance of the characteristics
• Calculation of preferential shares
• Forecast of the effects of price measures on sales, turnover and profits

The data is collected using computer-assisted surveys. The significance of each characteristic value for the overall preference is determined from the respondents’ answers. These partial benefit values allow well-founded statements to be made about which value a customer associates with changes in product price. This can be an improvement of features such as weight, range or charging time of the battery when buying an e-bike. The result is an estimate of the willingness to pay for a concrete offer. This derivation is possible because the benefit values of all characteristics and attributes are directly comparable. They are measured consistently on an interval scale during the interview and can thus be put in relation to each other. On this basis it is possible to determine, for example, which loss of benefit in the course of a price increase ( decreasing partial benefit worth) is compensated by the corresponding added value of an improvement in the offer (increasing partial benefit worth). In the course of this analysis, the significance of the characteristic values within the design becomes obvious. The specific question is: How strong must the offer improvement be in order to compensate for a specific price increase (in percent or Euro). Offer improvement can mean for five exemplary industries and product categories, among others:

1. Reduction of the weight of the components of an e-bike
2. Increasing the memory capacity of a smartphone
3. Increasing the horsepower for car purchase
4. Extension of passenger seat spacing in an upgraded booking class for holiday flights
5. Reduction of travel time on a Deutsche Bahn connection

The major advantage of the conjoint method is the quantification of these tradeoff considerations of the customers. The benefit functions usually vary depending on the segments to be served. This means in relation to the example: All five characteristics mentioned above are valued differently by different segments. With the differences in the appreciation, the willingness to pay also varies. The relative importance of the characteristics of a conjoint study is derived directly from the partial benefit values of the individual characteristics. Partial benefit values and importance serve as a data basis for the following core processes of price management:

1. Performance of simulation calculations
2. Estimation of price selling functions
3. Derivation of strategic recommendations for product development and pricing decisions

In the manner in which the product-price alternatives are combined, the conjoint method is very similar to the digital offer configuration on online portals. In both cases the product-price versions are deduced automatically. In the conjoint interview, the variants presented online result directly from the customer’s answers. They are dynamically adapted and correspond to the preferences of each individual user. The alternatives presented in the further course of the interview are increasingly reduced to price-performance variants that best match the respondent’s priorities. The analyses are refined with each change in design.

The design of a conjoint study includes a selection of the current product range or possible future product price variations. In contrast, digital offer configurators reflect the current product range and resulting price points. These can be list prices or customer-specific discounts. Configurators thus also refer to individual variants of a company’s product and price portfolio that cannot be meaningfully depicted in a conjoint study.

The conclusion can be summed up: Conjoint Measurement is very versatile. The method can be used for pricing industrial and consumer goods as well as services and digital products. It has worked equally well for new products and established offerings.

Focus group interviews for price determination

Focus groups are excellent for testing newly developed concepts and pricing models. It is about the structured exploration of chances and barriers of innovative offers including important customers. Alternatively, potential users can also be interviewed.

Structured group discussions are particularly useful for gathering detailed information about budgets, maximum prices and preferences for price architectures. Creative ideas for innovative pricing models and new discount structures can also be developed in this context. Psychological aspects behind rational argumentation, emotional backgrounds of purchase decisions as well as hidden assumptions in the context of trade-offs – all these criteria relevant to pricing can be analysed in depth with the help of a group discussion.

Brainstorming sessions are an excellent complement to the quantitative methods described above. Quantitative surveys can be validated by means of parallel group discussions, especially when exploring new price ranges and the possible exceeding of certain price thresholds. Price psychological aspects can be explicitly addressed in focus group interviews.

Expert assessment

The “Price-Volume-Assessment” is a technique to derive profit-optimal prices without direct involvement of users. The method is based on subjective estimates of sales potential at different prices by internal market and sales experts. An aggregated price sales function is determined from the experts’ individual estimations at different price points. It is possible to link the expert assessment with the method of target prioritization. The target setting (step 1) is followed by price optimization (step 2).

Expert Judgement is a very pragmatic approach to determine an optimal pricing. Its use is recommended for both new and established products. In an unexpected situation – e.g. if a competitor is about to enter the market – expert judgement is particularly suitable. This is because the method can be carried out quickly and without much preparation. The costs for implementation are very low. Especially in the B2B environment, the Price Volume Assessment has successfully proven its predictive power. The expert survey is generally recommended as a supplement to other methods, especially for direct user surveys based on the adapted PSM method.