The Critical Variable in Service Parts Pricing: Understanding Willingness-To-Pay

In this white paper, learn how scientific techniques using prescriptive pricing software enables companies to increase margins by identifying the WTP across their product categories, segments, and portfolios.

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THE CRITICAL VARIABLE IN SERVICE PARTS PRICING: UNDERSTANDING WILLINGNESS-TO-PAY Unlock Your Data • Unleash Your Sales WHITE PAPER By Sean Duclaux Director, Industry Marketing OPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRYOPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRY 32 DETERMINING WILLINGNESS-TO-PAY WITHIN REACH OF SERVICE PARTS COMAPNIES Although revenue generated from service parts factor into a company’s profits, few service parts suppliers take full advantage of the opportunity pricing offers. When dealing with the pricing complexities of thousands of parts, broad cost-plus pricing strategies are the industry norm, when in fact, a customer’s willingness-to-pay is independent of the production costs of the service part. The term “Willingness-to-Pay” (WTP) is frequently used to describe the spending limit of customers for products they buy. As defined by Christoph Breidert, an author and Ph.D. in business and economics: (1) Willingness-to-Pay is the highest price a buyer (e.g., individual, corporation, dealers, distribution channel, etc.) is willing to accept to pay for some good or service. It is important to understand that all of the various types of customers within the service parts market are encompassed in Dr. Breidert’s reference to a buyer. Examples of different customers in the service parts market are the airlines that fly commercial aircraft, a dealer franchise of a Detroit Big 3 automaker, a company like FedEx that owns a national fleet of trucks, traditional parts distributors like NAPA, or the many independent warehouse distributors, jobbers, and installers that purchase the part directly or from a multi-tiered channel. The WTP will vary greatly by the type of service parts customer, their position along the distribution chain, and the pricing pressures or availability of the service parts supply. The concept holds promise for service parts manufacturers and distributors when trying to estimate the right price for each of their products and markets. If you knew each buyer’s precise WTP for each product, you could simply charge that price. The likely result would be a huge increase in your profit and market share. MOVE FROM “COST-PLUS” TO VALUE PRICING Many service parts companies still use cost-plus pricing techniques as a general way to price their products. Example: price = cost plus 45 percent of manufacturing cost. Once prices are set, the effectiveness of that pricing is judged by changes in volume and the product’s P&L statement. In contrast, prescriptive pricing software technology offers a scientific, practical alternative that determines each buyer or buyer category’s WTP. Prescriptive pricing software from PROS, for example, is able to dynamically examine a history of transactions and adjust WTP as data comes in. Operating at a highly granular level, buyer by buyer, product by product, PROS makes a value-based pricing approach possible — estimating the value that buyers actually put on products versus what they might currently be paying. Such pricing insight is critical to ensuring that manufacturers and distributors gain and sustain a competitive advantage through value-based service parts pricing. OPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRYOPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRY 32 The next logical question, then, is how you can measure your buyers’ WTP. In 2006, researchers Breidert, Hahsler and Reutterer (2) reviewed the current methods for measuring WTP along with others such as Nagle and Holden (3). These authors categorize methods for determining WTP into four key approaches: experiments, direct surveys, indirect surveys and market data. Common business techniques to determine WTP, however, don’t apply very well in the service parts marketing environment. Experiments and surveys, while appropriate in business to consumer markets, don’t appear to work well in service parts business- to-business markets for a couple of reasons. First, experiments are very hard to implement through a sales force based on commissions. Second, the procurement departments are unlikely to participate in a focus group or give reliable answers to questions such as, “At what price point would you stop ordering this product?” However, indirect survey methods can be quite valuable when working with buyers at your top national accounts. With a focus on a handful of key accounts and the core products they buy, it is possible to gain insights into their perceptions of the value you provide. But, how do you determine the best price to ask of the other 98 percent of the target accounts? Most service parts manufacturers and distributors can determine WTP only by relying on market data; however, the researchers cited here give little guidance about how to actually measure WTP with market data. Nagle and Holden concede that “. . . if a researcher has a lot of historical data with enough price variation in it, useful estimates of price sensitivity are possible.” (3) Here’s the good news for companies in service parts industries: And, the key to unlocking WTP can be found in applying the science of pricing to your own service parts transactional market data. First, however, you need to understand some basic principles behind WTP. Your transaction data has a wealth of information that can help isolate and identify your buyers’ WTP across your product portfolio. OPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRYOPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRY 54 SEGMENTATION AND NORMALIZATION: START WITH YOUR OWN DATA Service parts companies typically possess plenty of data around their own products (cost, monthly volumes, product hierarchy, lifecycle stage, etc.), their customers (geography, size, industry, etc.) and their transactions, invoices and rebates (date, price, cost, discount, sales person, quantity, unit of measure, etc.). Do the supplier and customer relationships in different geographies have a different WTP? Does my purchasing agent have a discount expectation based on the quantity ordered? Do distributors react differently to a service part that costs $4,000 than to a service part that costs $20? Do jobbers notice price changes equally across all brake components, fluids and other maintenance items, or are there individual parts within each of these categories where a price increase is noticed less? The science of segmentation answers these questions clearly and decisively by using your existing service parts sales data. The process involves applying scientific algorithms, using computer software to mine your data and analyze all combinations of attributes, to determine the key variables that impact your customers’ WTP. While there are numerous attributes to consider (e.g., geography, OEM vs. aftermarket, reman vs. new, ecofriendly, etc.) determining WTP can still be challenging given certain regulatory and legal constraints. Service part manufacturers, for example, are bound by agreements with their distributors — differentiated pricing by dealer is strictly prohibited. Rebates and volume discounting may offer some opportunity to create differentiation, but, in general, service part suppliers that sell through a dealer network must set a single PRESCRIPTIVE PRICING BASED ON WTP PRODUCES RESULTS Using extensive data on tens of thousands of products, PROS pricing software was able to analyze and determine WTP for a multi-billion dollar distributor. By automating cumbersome manual processes and using statistical methods to segment its customer base into peer groups, PROS provided scientifically generated target, floor and stretch (e.g., expert) pricing guidance. Guidance increased margins of low-performing customers in each peer group, while ensuring that high performers continued to be highly profitable. The PROS pricing solution helped reduce “below floor prices” more than 10 percent while increasing the number of invoices exceeding sales targets more than 10 percent, resulting in a gross profit increase of more than 230 basis points. Revenues increased by more than two percent in individual segments. OPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRYOPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRY 54 price per service part. Without price differentiation, you may question the possibility of accurately calculating your customers’ WTP. The solution to this challenge involves more thoughtful analysis of your product attributes. Consider the following scenario: Part Manufacturer A, an American-based company, sells an environmentally friendly brake pad made of recycled materials. Not surprisingly, demand for this product is highest among the part manufacturer’s customers located in environmentally sensitive areas of the US, such as the west coast. Therefore, the part can be described in terms of its customer attributes; we would say, for example, the brake pad is “west coast centric.” Given this information, Part Manufacturer A will still set a single optimized price for all customers, but it will do so based on demand and profitability on the west coast, allowing the company to capitalize on revenues where it expects its highest sales volume. The concept of geographic centricity is not limited to environmentally friendly characteristics. Due to differing vehicle sales volumes, brand preferences and climates, you will find that most parts become centric to one region or another. And, centricity doesn’t have to be limited to geography. Parts can be “centric” in terms of other customer characteristics, such as dealership size. Therefore, even though many service parts companies cannot price differentiate across customers, they can still use customer attributes to describe how a part sells in the marketplace and determine the optimal price. Where differentiation by customer attributes is limited, pricing science provides service parts companies with the ability to differentiate by product attributes, thereby allowing them to increase their revenues and drive their overall profitability. These factors make targeted pricing a real challenge for parts manufacturers and distributors, but understanding your customers’ WTP as well as how your products differentiate WTP in the market can make a big difference in the performance of your pricing decisions. Moreover, it is important to remember that segmentation alone does not account for the dynamic nature of WTP discussed earlier. Normalization is the principle that applies a time-based approach to WTP that enhances the accuracy of pricing products. Normalization works by looking at external factors (date, producer price index, inflation, etc.) that vary over time and then adjusts historical transactions appropriately. Economists use this technique all the time. For example, gold is at an all- time high, but not when normalized against history. The early 1980s holds the price record when you adjust for inflation (4). The science of segmentation puts different customers, channels, parts and transaction environments in separate WTP buckets. Normalization changes historical data inside each bucket to account for WTP changes over time. But you must go another step further by looking inside each segment and determining the unique WTP for each product through distribution analysis. OPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRYOPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRY 76 300 0 250 200 150 100 50 $1 .00 $1 .03 $1 .05 $1 .08 $1 .11 $1 .14 $1 .18 $1 .21 $1 .25 $1 .29 $1 .33 $1 .38 $1 .43 $1 .48 $1 .54 $1 .60 $1 .67 $1 .74 $1 .82 $1 .90 $2 .00 $2 .11 $2 .22 $2 .35 $2 .50 $2 .67 $2 .86 $3 .08 $3 .33 $3 .64 $4 .00 Actual Margin Data Figure 1 - Pricing science can estimate the true Willingness-to-Pay even if loss data is unavailable. This chart shows the price distribution of a service part based on win data. OPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRYOPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRY 76 USING SALES TRANSACTION ANALYSIS TO ESTIMATE WILLINGNESS-TO-PAY Once you’ve segmented and normalized your purchasing data, the next step is to examine the distribution of prices within each specific segment. How you proceed from here depends on the availability of loss data. If you know those price points in a segment at which your customers walk away and at which they buy, a clear distribution of WTP emerges. Research by Ferguson and Agrawal (5) and Robert Phillips (6) explains how to use loss data to model win elasticity across a specific customer segment. Unfortunately, most service parts manufacturers and distributors don’t have access to their loss information. Even if you could mine the data from field sales or call center transactions, it’s almost impossible to know that someone didn’t buy your product because of price. Therefore, a realistic yet robust WTP estimation for most service parts manufacturers and distributors must come from win-only data. ESTIMATING WILLINGNESS-TO-PAY USING WIN-ONLY DATA Let’s revisit the definition of WTP: the highest price a buyer (e.g., individual, corporation, dealers, distribution channel, etc.) is willing to accept to pay for some good or service. Consider the price distribution for a service part that sells between $1.00 and $3.00 as shown in Figure 1. If one of your customers buys that product for $1.45, then their WTP was greater than or equal to that price. Since we have only win data, that means the data distribution is skewed as it relates to the WTP of that customer or segment. Take a closer look at Figure 1 — a specific segment’s win distribution. Prices cluster around $1.45. Does that mean $1.45 is your best estimate of the WTP of this segment? Answer: No. It means that your sales force, pricing desk and category managers feel comfortable charging around $1.45 per unit. The true WTP of this segment is greater than $1.45. So, what is the true WTP distribution of this segment? Prescriptive pricing technology can give you the answer. PROS pricing science analyzes myriad factors to statistically produce the best possible estimate of WTP given the winonly data typically available in service parts markets. Based on the example shown here and taking into account part velocity, lifecycle competitiveness and other key transaction attributes, the Willingness- to-Pay for this service part is best estimated at $1.67. As one of the key inputs into pricing optimization, OPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRYOPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRY 98 accurate prediction of WTP plays a large role in the quality of optimized prices. An increase in pricing accuracy can make a big impact on your balance sheet. In implementation after implementation, PROS customers consistently observe a 100–300 basis point gross profit improvement compared to their corresponding control groups by using sophisticated pricing science. DETERMINING WILLINGNESS-TO-PAY HELPS BOOST PROFITS Service parts manufacturers and distributors consistently customize and adjust their pricing for good reasons. Certain products demand a premium over others. Different regions have varying competitive landscapes. Contracts expire under varying circumstances. Whether you rely on a centralized price matrix with customized exceptions or a totally autonomous sales force, understanding the Willingness-to-Pay of your customers under various conditions can be a source of significantly increased profits. And, the use of new software technology based on scientific analytics can help you exploit this opportunity in a practical and affordable way. At PROS, we offer proven pricing technology and an unmatched track record of success in helping service parts manufacturers and distributors achieve outstanding returns through a greater understanding of their customers’ WTP. You can learn more by visiting our website at www.pros.com or by emailing us at info@pros.com. OPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRYOPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRY 98 REFERENCES 1. Breidert, C. (2005). Estimation of willingness-to-pay. Theory, measurement, and application. Vienna: Doctoral Thesis, WU Vienna University of Economics and Business. 2. Breidert, C., Hahsler, M., & Reutterer, T. (2006). A Review of Methods for Measuring Willingness- to-Pay. Innovative Marketing, 8-32. 3. Ferguson, M., & Agrawal, V. (2007). Bid-response models for customised pricing. Journal of Revenue and Pricing Management, 212–228. 4. Leonhardt, D. (2010, November 10). The Enduring Myth of Gold’s Record High. The New York Times, p. B1. 5. Nagle, T., & Holden, R. (2002). The Strategy and Tactics of Pricing. Prentice Hall. 6. Phillips, R. (2005). Pricing and Revenue Optimization. Stanford Businesson scientific analytics can help you exploit this opportunity in a practical and affordable way. OPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRYOPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRY 1110 ABOUT THE AUTHOR Sean Duclaux serves as PROS Director, Industry Marketing. He is responsible for the development of the company’s go-to-market strategy, and positioning of its Service Parts pricing and revenue management product portfolio. Prior to joining PROS, Duclaux held leadership positions in enterprise software companies, including AspenTech, BMC Software and Empirix. Throughout his career, he has worked in diverse roles, from product management, marketing, program management, R&D and operations, where he developed a strategic vision to define market-focused solutions and executed go- to-market programs. Duclaux earned an MBA from the University of Houston; an M.S. in computer science from the University of New Orleans and a B.S. from Spring Hill College. OPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRYOPTIMIZING PRICING FOR THE AUTO/EQUIPMENT PARTS INDUSTRY 1110 Copyright © 2014, PROS Inc. All rights reserved. This document is provided for information purposes only and the contents hereof are subject to change without notice. This document is not warranted to be error -free, nor subject to any other warranties or conditions, whether expressed orally or implied in law, including implied warranties and conditions of merchantability or fitness for a particular purpose. We specifically disclaim any liability with respect to this document and no contractual obligations are formed either directly or indirectly by this document. This document may not be reproduced or transmitted in any form or by any means, electronic or mechanical, for any purpose, without our prior written permission. About PROS PROS.com Copyright © 2015, PROS Inc. All rights reserved. This document is provided for information purposes only and the contents hereof are subject to change without notice. This document is not warranted to be error -free, nor subject to any other warranties r conditions, whether expresse orally or implied in l w, including implied arranties and conditions of merchantability or fitness for a particular purpose. We specifically disclaim any liability with respect to this document and o contractual obligations are formed either irectly or indirectly y this document. This document may not be reproduced or transmitted in any form or by any means, lectronic or mechanical, for any purpose, without our prior written permission. PROS Holdings, Inc. (NYSE: PRO) is a big data software company that helps customers outperform in their markets by using big data to sell more effectively. We apply years of data science experience to unlock buying patterns and preferences within transaction data to reveal which opportunities are most likely to close, which offers are most likely to sell and which prices are most likely to win. PROS offers big data solutions to optimize sales, pricing, quoting, rebates and revenue management across more than 40 industries. PROS has completed over 800 implementations of its solutions in more than 55 countries. The PROS team comprises approximately 1,000 professionals around the world. To learn more, visit pros.com.
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