The Critical Variable in Business-to-Business Marketing: Understanding Willingness-to-Pay

The CriTiCal Variable in business-To-business MarkeTing: undersTanding Willingness-To-Pay

unlock your data • unleash your sales

By Neil Biehn, Ph.D.,Vice President, Science, PROS

Copyright © 2013, 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 tness for a particular purpose. We specically 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.

Business-to-Business Marketing: Understanding Willingness-to-Pay

1

determining Willingness-To-Pay Within reach of Most b2b CompaniesThe term ”Willingness-to-Pay” (WTP) is frequently used by pricing professionals 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 an individual is willing to accept to pay for some good or service.

The concept holds promise for business-to-business manufacturers and distributors when trying to estimate the right price for each of their products and markets. If you knew each customer’s precise WTP for each product, you could simply charge that price. The result would likely be a huge increase in your profit and market share.

However, common business techniques to determine Willingness-to-Pay don’t apply very well in the business-to-business environment. 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.

Experiments and surveys, while appropriate in business-to-consumer markets, don’t appear to work well in business-to-business markets for a couple of reasons. First, experiments are very hard to implement through a sales force based on commissions. Second, your customers’ 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 individuals at your top national accounts. With a focus on a handful of key customers 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 your customers?

Most business-to-business manufacturers and distributors can determine WTP only by relying on market data. But the researchers cited here give little guidance about how to actually measure WTP with market data.

ExpErimEnts and survEys, whilE

appropriatE for pricing in businEss

to-consumEr markEts, don’t

appEar to work wEll in businEss-

to businEss markEts.

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 business-to-business markets:

Your transaction data has a wealth of information that can help isolate and identify your customer’s WTP across your

product portfolio.

And, the key to unlocking WTP can be found in applying the science of pricing to your market data. But first you need to understand some basic principles behind Willingness-to-Pay.

Willingness-to-pay: a constantly moving targetWTP in the business-to-business sales environment is an ever-changing, moving target. Consequently, it isn’t simple oreasy to quantify. Take this example: If you lose your car key, how do you put a price on its replacement? Your car dealer has a battery-powered key fob that can detect when you are in proximity and automatically unlock your car. Alternatively, you can ask for a mechanical key that opens only the driver side door. What is your willingness to pay for the added technology? My father would buy the mechanical key under all circumstances. I am much more of a technology/gadget geek and would pay the price premium for the key fob convenience.


2

tEchnology kEy to Evolution from

“cost-plus” to valuE pricing

Many manufacturers today still use cost plus pricing techniques as a general way to price products in a business-to-business environment. Example: price = cost plus 25 percent of cost. Once prices are set, the effectiveness of that pricing is judged by the product’s P&L statement. In contrast, prescriptive pricing software technology offers a scientific, practical alternative that determines each customer or customer category’s Willingness-to-Pay.

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, customer by customer, product by product, PROS makes a value-based pricing approach possible – estimating the value that customers 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 pricing.


3

Consider another example: air conditioning. Is your willingness to pay different if you are replacing your old air conditioner during the winter months, compared to a breakdown during the dog days of summer? What if you live in Houston, Texas or Seattle, Washington?

Here’s the point:

WTP changes depending on the type of customer, product, and transaction environment of the sale being executed.

With customer behavior, timing and environments constantly changing, a customer’s WTP for a given product can be difficult to pinpoint --- but certainly not impossible. The first step on the road to achieving WTP is the segmentation and normalization of market data.

segmentation and normalization: start with your own dataBusiness-to-business companies typically possess plenty of data around their own products (cost, lifecycle, hierarchy, 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 customers in different geographies have a different WTP? Does my

customer base have a discount expectation based on the quantity ordered? Do customers react differently to a product that costs $4,000 than to a product that costs $20? Does my newest innovation get a value premium over its older version?

The science of segmentation answers these questions clearly and decisively by using your existing 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. However, 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 hold the price record when you adjust for inflation (4).

transaction data has a wEalth of

information that can hElp isolatE and

idEntify your customEr’s willingnEss-to-pay

The science of segmentation puts different customers, products 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.

using sales transaction analysis to estimate WTPOnce you’ve segmented and normalized your customer, product and transactional

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 where your customers walk away and where 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 business-to-business manufacturers and distributors don’t have access to their loss information. Even if you could mine the data from RFPs 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 business-to-business manufacturers and distributors must come from win-only data.


4

Figure 1 - Price distribution for individual product.

Pricing Science can estimate the true Willingness-to-Pay even if loss data is unavailable


5

estimating WTP using win-only dataLet’s revisit the definition of WTP: the highest price an individual is willing to accept to pay for some good or service. Consider the price distribution for a specific product 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.

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 hi-tech electronics 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 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” by more than 10 percent, while also 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.

The ability of prescriptive pricing technology to unlock WTP depends on two key factors:

1) Lots of sales win information – there must be enough data in each individual customer or segment to statistically support WTP estimates.

2) Flexibility in pricing – Pricing cannot be uniform across all customers in the sense that contract, agreement, subscription or spot pricing decisions are made by an autonomous sales force, or price is determined through some form of price approval process.

Pricing Science analyzes myriad of factors to statistically produce the best possible estimate of Willingness-to-Pay given the win-only data typically available in B2B markets. As one of the key inputs into pricing optimization, accurate prediction of Willingness-to-Pay plays a large role in the quality of optimized prices. An increase in pricing accuracy, for example, can make a big impact on your balance sheet. In implementation after implementation, PROS’ customers consistently observe a 100 – 300 basis point improvement as compared to their corresponding control groups by using sophisticated pricing science. (See left sidebar for customer case study.)

determining WTP for competitive advantageBusiness-to-business 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 gives you a significant competitive advantage. This opportunity is more broadly available than most companies realize, and can be a source of significantly increased profits

At PROS, we offer proven pricing technology and an unmatched track record of success in helping Fortune 500 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 [email protected].

willingnEss-to pay in thE businEss-to-businEss salEs

EnvironmEnt is an EvEr-changing, moving targEt.

consEquEntly, it isn’t simplE or Easy to quantify.

references1. 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 Business

Neil Biehn is the senior director of science and research at PROS, a leading provider of pricing and margin optimization software products. At PROS, Dr. Biehn specializes in the research, implementation and innovation of pricing optimization in the B2B and travel and transportation industries. Dr. Biehn received his Ph.D. in operations research from North Carolina State University.

neil biehn

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 27 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 30 industries. PROS has implemented more than 600 solutions in more than 55 countries. The PROS team comprises more than 700 professionals around the world.

To learn more, visit www.pros.com.

about Pros

Pros.com

Copyright © 2013, 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 tness for a particular purpose. We specically 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.

Technology

The Critical Variable in Business-to-Business Marketing: Understanding Willingness-to-Pay