Choosing the Best Pricing Techniques to Address Consumer Goods Pricing Challenges A Current Best Practices Paper

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1 CONSUMER GOODS SECTOR Choosing the Best Pricing Techniques to Address Consumer Goods Pricing Challenges A Current Best Practices Paper Curt Stenger Senior Vice President, Ipsos Marketing, Consumer Goods Sector curt.stenger@ipsos.com Research Challenge Identifying the optimal price for a new product is a critical step in the innovation process and correcting the price of an existing product is a necessary component of successful management. With the wide range of pricing research techniques practiced in the industry, it is not always clear which technique best addresses the business issue at hand. This paper describes the most common methods used for consumer goods pricing research and offers guidelines on when and when not to use specific methods. August 2008 For more Ipsos Marketing papers, visit

2 About Ipsos Marketing Ipsos Marketing is a leading global market research company. With unequalled expertise in Innovation and Brand Research, we help clients understand consumer behavior, develop and launch new products, and improve performance in their respective markets. Ipsos Marketing offers world-class solutions that help clients build their businesses throughout the new product development process from the earliest stages of innovation through maturity. Our wide range of global solutions integrate quantitative and qualitative research as well as advanced modeling and forecasting techniques. Our research is supported by sector experts who specialize in Consumer Packaged Goods, Shopper and Retail, Health and Pharmaceuticals, Durable Goods, Financial Services and other industries and services. By developing a deeper understanding of your s, consumers and marketplace, we always deliver actionable recommendations rooted in reality. About the Author Curt Stenger is Senior Vice President, Ipsos Marketing, Consumer Goods Sector and leads the Marketing Sciences group in the United States. With nearly 20 years of market research experience across a broad range of industries, Curt has specialized expertise in equity, consumer segmentation, and new product development and forecasting. Curt is particularly interested in applying non-traditional marketing research techniques to marketing problems. Curt holds a Master s degree in statistics from the University of Chicago, an MBA from the University of Pittsburgh, and a Bachelor s degree in economics from Northwestern University. This Ipsos paper is protected by copyright and may not be physically reproduced without the expressed permission of the Ipsos-Insight, Inc. Subscribing organizations may extract or synthesize data or text of their choosing for internal or limited public use. 2

3 Introduction Pricing research is an essential component in the innovation process as well as ongoing management. Because price optimization is such a significant facet of product development and management, a vast array of pricing techniques have been developed and continue to evolve. With so many techniques available, it is not always clear which technique best addresses the business issue at hand. Yet, it is imperative that the most appropriate technique is chosen to ensure the right balance of depth of research required and research efficiency. The two most common ways of understanding pricing decisions is through existing category management systems and through primary survey research. An investigation of the pricing activity in existing categories is frequently the first step in understanding the demand-side of a pricing decision. This information is often enough to make decisions for existing products and traditional line extensions in well-developed product categories. Survey research approaches provide greater consumer insight into their demand and is most helpful with more unique innovations and less developed categories. This paper describes many of the common survey research methods and places them along a concept/product development continuum. (Figure 1). This continuum helps describe how pricing methods can best be used to address particular pricing issues based on the level of competition needed to be considered and the depth of pricing knowledge needed. Custom research pricing methods fall into two main approaches: (1) pricing the total product (pricing for a complete concept/product) and (2) pricing elements of a variable concept/product offering such as ing or features. This discussion will focus on pricing the total product. We are focusing on this because pricing elements (which is largely accomplished through conjoint trade-off models) tends to focus more on the features as either equal or superior to price and often needs to be used in conjunction with another method to fully understand the impact of price on success. Specific techniques that price the total product and which are covered in this paper are Gabor-Granger, price sensitivity measurement, monadic concept, -price trade-off, and simple discrete choice. Fuller profile discrete choice will be discussed briefly along with the simple discrete choice approach. 3

4 Pricing the Total Product: Pricing the Product Individually vs. Pricing the Product Competitively The vast majority of custom research pricing research falls into the arena of pricing the total product, primarily because the vast majority of the business issues we cover involve new products or concepts. Within this arena of pricing the total product, there are two avenues that might be followed: (a) pricing the product individually; and (b) pricing the product competitively. Techniques that price the product individually are typically appropriate during the early phase of new product development, while techniques that price the product competitively are usually reserved for existing products or new products that have unique business issues, which are described below. Whether pricing the product individually or competitively, various techniques exist that offer varying levels of complexity. Based on the business challenge that needs to be met, pricing research can provide a basic pricing threshold, more refined price sensitivity, the impact of competitive action and more complex price elasticities. (See Figure 2). Figure 2: Research Techniques for Pricing the Total Product Brand A Techniques that PRICE THE PRODUCT INDIVIDUALLY Early Phase of New Product Development Brand A Brand B Brand C Techniques that PRICE THE PRODUCT COMPETITIVELY Existing Products or New Products with Unique Issues DEPTH OF RESEARCH INCREASES Gabor Granger PSM Monadic Concept Provides Pricing Threshold Provides Price Sensitivity Provides Price Threshold and Price Sensitivity BPTO Simple Discrete Choice Provides impact of competition Provides price elasticity Pricing the Product Individually As just mentioned, marketers typically test the pricing of a product individually when the pricing decision is imbedded as part of the early phases of the new product development process. To accomplish this, one of three approaches is typically followed: Gabor-Granger, price sensitivity measurement (PSM), or monadic concept cells. Gabor-Granger The Gabor-Granger method was developed in Australia in the 1960s. It asks a series of priced purchase interest questions, such as, Would you buy X at price Y for a single product or concept. The price goes up or down depending on the response to the first purchase interest question. The series continues two to three more times until the consumer will not go any higher or lower on their interest price. There are variations of the approach where the prices are provided randomly or a fixed series of price questions are asked. The result of the approach is a simple demand curve for the concept or product in question where the proportion of consumers interested in buying it at a certain price is reported. While there have been recent attempts to incorporate prior knowledge to smooth and enhance the Gabor-Granger demand curve (Conklin and Murphy, 2006), this is generally considered the simplest and roughest approach to pricing research and would only be recommended for price threshold decisions for relatively rough concepts. 4

5 Price Sensitivity Measurement Introduced in the 1970s by a Dutch economist, Peter van Westendorp, the Price Sensitivity Meter (PSM) is probably the most often used research approach when doing pricing research on an individual product or concept. 1 The premise of the PSM is to ask respondents four price-related questions and then evaluate the cumulative distributions for each question. Specifically, respondents are asked: 1. At what price would you consider the product to be so expensive that you would not consider buying it? (Too expensive). 2. At what price would you consider the product to be priced so low that you would feel the quality couldn t be very good? (Too cheap). 3. At what price would you consider the product starting to get expensive, so that it is not out of the question, but you would have to give some thought to buying it? (Expensive). 4. At what price would you consider the product to be a bargain a great buy for the money? (Cheap). With this method, the optimal price point for a product is the point at which the same number of respondents indicate that the price is too expensive as those who indicate that the price is too cheap (Figure 3). Percentage of Respondents 100% Range of Acceptable Prices: $0.01 to $0.90 Range of Recommended Prices: $0.01 to $ % 50% 25% Figure 3: Test Data Price Test Likelihood to Buy (%) Figure 4: Test Data Trial and Revenue Curves 40% Max Trial 35% = $ % 25% 20% 15% 10% Max Revenue = $0.30 $180 $160 $140 $120 $100 $80 $60 $40 Revenue Per Hundred People 5% $20 0% $0.00 $0.50 $1.00 $1.50 $2.00 $2.50 $3.00 $3.50 $4.00 Price (US$) --- Too Cheap --- Bargain --- Getting Expensive --- Too Expensive 0% $0 $0.00 $0.50 $1.00 $1.50 $2.00 $2.50 $3.00 $3.50 $4.00 Price (US$) --- Trial --- Revenue Adaptations incorporating purchase interest and category purchase frequency have also been introduced to generate rough trial and revenue curves (Figure 4). A key limitation of this approach to pricing research is that respondents ability to answer these questions is dependent upon their having a good reference price. For almost any product that is not a direct line extension, respondents will not have a good reference price. In a large sense, PSM becomes a test of price awareness rather than a measure of price sensitivity which limits the approach s ability to properly estimate premium prices. A frequently used adaptation to address the reference price issue is to ask the four PSM questions limited to a large range of prices.this does provide better results for categories where the reference price is difficult to pin down but it does lower the sensitivity of the approach and is not applicable for all products and categories. PSMs are applicable for individual product or concept evaluation when an understanding of the competitive context is limited and the pricing issues revolve more around relevant price thresholds than price sensitivity. The approach performs best in known categories with reference prices above $ s_price_sensitivity_meter 5

6 Monadic Concept Cells The last individual product approach is to include alternative price points for the same concept or product within an existing monadic study design. This approach is most common when pricing is a key ancillary issue within a validated forecast since the results are easy to understand and are easily incorporated into all major custom research forecasting models. The results can either be considered individually, essentially as separate forecasts, or, if five or more price cells are developed, a price curve similar to a Gabor-Granger demand curve can be developed and forecasts can be developed across the entire curve. The monadic cell approach is most applicable when single concepts are fully formed enough to require a simulated test market (STM) forecast and an understanding of both a concept s price threshold and price sensitivity are required. Key limitations include the cost associated with the additional cells and a lack of price sensitivity for prices within roughly 15% 20% of each other. Pricing Products Competitively Marketers typically attempt to price products competitively when assessing existing products or when the new product innovation falls outside of their typical new product development testing program. Most frequently new innovations fall outside of the program when: (1) the decision to introduce the product has been made and specific market decisions such as placement and pricing are left to be determined; (2) innovations are new enough that competitive context better defines the innovation in terms of price and usage; and (3) price is only one of a number of product features that need to be determined as an optimal offer. Brand-Price Trade-Off (BPTO) BPTO is an extension of the Gabor-Granger approach to a competitive context. In BPTO, consumers choose a or concept from a group of priced s/concepts.the consumer then makes another choice from the group of s/concepts at different prices. This generally continues from five to ten times per consumer. Figure 5 below depicts the first step where a consumer would be exposed to five concepts, selects concept B at $3.75, and then gets exposed to the same five concepts with concept B now set at $4.00 (Figure 6). Figure 5: First Exposure to Concepts Figure 6: Next Exposure to Concepts A B C D E A B C D E $3.50 $3.75 $4.15 $4.50 $4.25 $3.50 $4.00 $4.15 $4.50 $4.25 The same variations about random pricing, fixed pricing and learning algorithms that apply to Gabor-Granger apply to BPTO as well. BPTO is most applicable for competitive situations where the issues are limited to simply price and. The primary advantages of BPTO are the competitive framework and the simplicity of the task for both consumers and questionnaire developers. While practitioners of BPTO will comment on its strength for understanding price threshold and price sensitivity issues, the underlying model can make it a less robust tool for price sensitivity than discrete choice models. 6

7 Simple (Brand/ Price ONLY) Discrete Choice Models To the consumer, a simple discrete choice exercise that includes only and price usually looks identical to a BPTO exercise with the inclusion of a None of These option but that comparison limits the power and flexibility even a simple discrete choice model brings to pricing research. Discrete choice pricing approaches tend to offer a fuller suite of deliverables beyond the simple share and utility deliverables associated with BPTO. Elasticities, cross-elasticities and proximities are all deliverables provided with discrete choice that provide deeper knowledge of price sensitivity and the impact of competitive action than BPTO. Cross-elasticities and or product proximities are excellent tools to understand the relative strength of the new innovation in the context of existing products and s. These can be reported as a simple table of numbers that shows how much the share of the s in the rows changes if the in the column changes its price (Figure 7); averaged by row and column and presented as clout and vulnerability (Figure 8) to determine the strength and weakness of the innovation overall; or mapped by frequency of their preference (Figure 9) to determine which products are most often considered with the new product. Figure 7 Brand A $1.20 Brand B $1.30 Brand C $1.30 Brand D $1.20 Brand E $1.10 Figure 8 Brand A $1.20 Brand B $1.30 Brand C $1.30 Brand D $1.20 Brand E $ Figure 9 Niche Brand B 1.50L Brand A 1.50L Brand A 2.00L Brand C 1.50L Brand C 1.50L Brand C 2.00L Brand G 1.50L Brand H 1.50L Sets of competing s/skus Brand D 1.50L Brand E 1.50L Brand F 1.50L 7

8 The greatest advantage of discrete choice for competitive price situations is its flexibility. From an exposure perspective, a discrete choice price exercise can look anywhere from a BPTO to a full virtual shelf depending on the competitive context required. An interesting note is that recent research has been inconclusive regarding any accuracy improvement using virtual full shelves compared to the partial set designs (like BPTO) the industry has tended to use (Renken and Rogers, 2005). This is in contrast to the face validity that full shelf exposure has in the industry, particularly for fast-moving consumer packaged goods. From a modeling standpoint, discrete choice is also generally the best alternative for competitive pricing situations because the study design can be specified and adjusted from a simple BPTO type estimation to a design that will create interaction estimates for issues like price and product availability. No other pricing approach allows you to change the business issues so dramatically and not change the research methodology. Discrete Choice models (DCM) also provide the most comprehensive simulators to understand pricing scenario building. DCM pricing models can be used to create the simplest price-only simulators to full market share tools. The complexities that discrete choice address also form its limitation. Frequently there is not enough external information or time to find the information to allow the researcher to fully inform the pricing scenario. Situations where the competition is poorly defined, where price ranges are loosely defined, where the competitor s behaviors are not well known, where distribution and channel information is hard to gather are all frequently encountered. There is also a cost constraint associated with using discrete choice modeling for all pricing research issues. DCM modeling is a specialized skill and does cost more than other pricing methods. It also requires more cognitive cost from the client to properly define the market situations (competitive context, channel and distribution situations) than individual product approaches. Concluding Remarks In summary, the most critical take-away about pricing research is to properly frame the cost of the pricing decision being studied. This document provides a roadmap of the various pricing decisions (Figure 1) and their applicable custom research pricing techniques (Figure 2). What is inferred in both Figure 1 and the sequence of the techniques is the research cost associated with complexity which the research buyer should put in the context of the cost of the pricing decision to their firm match the sophistication and cost of the pricing research to the internal cost of pricing the product incorrectly. References Conklin, W. Michael and Thomas Murphy (2006), Extending the Power of Simple Pricing Tools, American Marketing Association Advanced Research Techniques Forum, June Monterey, CA. Renken, Tim and Greg Rogers (2005), Shelf vs. Grid Stimuli in Internet Packaged Goods Discrete Choice Studies, American Marketing Association Advanced Research Techniques Forum, June 2005, Coeur d Alene, ID. w w w. i p s o s m a r k e t i n g. c o m