Pricing: Segmentation and Analytics

Tudor Bodea

• Publisher: Business Expert Press
• Year: 2012
• ISBN:
  9781606492581
• Topic: Business
• Subtopic: Marketing Research
• Index: Business

Pricing analytics uses historical sales data with mathematical optimization to set and update prices offered through various channels in order to maximize profit. With this outstanding contribution to this subject, you will learn just how to identify and exploit pricing opportunities in different business contexts. Each chapter looks at pricing from an economist's viewpoint beginning with the basic concept of pricing analytics and what type of data are needed to use this powerful science; the common assumptions regarding the customer population's willingness- to-pay are discussed along with the price-response functions that result from these assumptions; examples from several industries and organizations; dynamic pricing, with a special emphasis on the most common application--markdown pricing; the new field of customized pricing analytics, where a firm responds to a request-for-bids or request-for-proposals with a customized price response; and the relevant aspects of behavioral science to pricing. Additional examples include the asymmetry of joy/pain that customers feel in response to price decreases/increases.

Chapter 1

Theory of Pricing Analytics^*

How do managers in a firm decide on the prices to charge for the products or services they sell? For some firms, the pricing decision is made at the top level of the management team with little flexibility for the sales force, marketing team, or supply chain partners (distributors or retailers) to make adjustments. Apple’s fixed pricing for its popular iPhone and iPad products is a good example of this practice. For other firms, it may appear to the customer that pricing is left entirely up to the individual sales person. This happens frequently in the business-to-business (B2B) market, such as the pricing for a major software implementation by a software vendor or consulting company. The price quoted for a new car from a dealership is an example from the business-to-consumer (B2C) market. Even in these situations, however, there is almost always some price guidance provided by a division level or corporate pricing team. Thus most large firms have some department or team whose primary responsibility is to determine the price, or price range, to charge for the firm’s products. The group may reside under many different branches of the corporate organizational structure, such as marketing, operations, or finance, and its members may or may not have pricing in their job titles. It is also common for this group of professionals to have access to some historical pricing and sales data, even if this information is not currently being used in the price setting process.

Pricing analytics involves the use of historical data to determine the best prices to set for future sales. In this chapter, our focus is on the theory behind pricing analytics. In the next chapter, we discuss how the practice of pricing analytics may sometimes differ from the theory. Our focus in both of these chapters is on the setting of a base price for a single product. An example would be the regular price to charge for a nonperishable product sold through a retail store. We cover more specific applications of pricing analytics such as dynamic pricing, markdown pricing, and customized B2B pricing in later chapters. We also save our discussion of the important topic of behavioral responses to pricing for a later chapter. While it is often difficult to apply the techniques described in this chapter directly, a good understanding of the theory behind pricing analytics is crucial to successful applications of the techniques described later in the book.

The Microeconomists’ View of Consumer-Purchasing Decisions

What makes a consumer decide to purchase a product or to choose one product over all the other alternatives? Economists use the term consumer utility to represent the value that a particular product or service provides to a customer. Utility is often represented in monetary values. For example, at a particular point in time, a consumer may derive a utility of $1 for a can of Coca-Cola and a utility of $0.90 from a can of Pepsi.

Willingness-to-Pay

Another term that is commonly used in the pricing field is the consumer’s maximum willingness-to-pay (WTP), or the maximum price at which the consumer would buy a good. Often, consumer utility and willingness-to-pay are terms that are used interchangeably. Thus if the consumer is at a store that only sells cans of Coca-Cola, microeconomic theory says that the consumer will purchase a can if the price is less than or equal to $1 (we are assuming away budget constraints in this example, i.e., the consumer is not prohibited from purchasing the product because of budgetary constraints). If the store sells cans of both Coca-Cola and Pepsi, then the consumer will purchase the brand that maximizes her remaining utility after subtracting the purchase price. For example, if a can of Coca-Cola is priced at $0.75 but a can of Pepsi is $0.50, then the customer purchases the can of Pepsi because ($0.90 – $0.50) > ($1.00 – $0.75).

Consumer Search Cost

In addition to a side-by-side comparison of the prices of different brands of a particular product, consumers are often aware that alternative brands or prices are available at other locations or through other channels. In the previous example, we assumed that the consumer will purchase one of the brands of soda from the store she is currently in. Suppose, however, that the consumer knows that the store across the street sells cans of Pepsi for $0.40. Will the consumer delay the purchase of a can of soda and cross the street to save an extra $0.10? To answer this question, economists have defined a “search cost” to represent the hassle of searching and purchasing the product from another location or source. Thus the consumer will still purchase the can of Pepsi from the store as long as ($0.90 – $0.50) > ($0.90 – $0.40 – search cost).

What makes pricing challenging is that a particular customer’s utility for products may change over time depending on factors such as the season, the weather, the overall economic climate, or the current competitive environment in our industry. In addition, different consumers typically have different WTPs and different search costs (termed heterogeneous customers in the economics literature), and we seldom have the capability to set a personalized price for each specific consumer. Even if we did have the capability to set customized prices for each specific consumer, they do not make it a practice to tell us what their utilities are for our products. This is rational; consumers recognize that firms that know their maximum WTP for a product can set a specific price for each consumer equal to that consumer’s maximum WTP. Thus the practice of pricing analytics has evolved to improve upon historical pricing performance while taking into account the realities mentioned earlier.

The Pricing Analytics Process

Pricing analytics is an iterative process using historical price/demand data to adjust the price of a product in order to maximize profits by analyzing the trade-off among price, volume, and cost. In general, the field has moved toward the term price analytics and away from the term price optimization because optimization implies that it is possible to analytically determine the single price that will maximize profits with a reasonable degree of confidence. In practice, there is always uncertainty about whether a given price is the “right” price. Still, approximating the optimal price is the goal. Determining the profit-maximizing price requires a combination of analytical rigor and managerial judgment to understand the trade-offs among prices, costs, and customer response. An overview of this process is shown in Figure 1.1.

The approach behind pricing analytics is to formulate pricing problems as constrained optimization problems that can be solved by standard techniques. The following elements are required:

• A price-response function that describes how customers are expected to respond to our pricing actions
• An objective function that specifies what we are trying to achieve (maximize profits, meet a market share target, etc.)
• A set of constraints that...

Table of contents

• Cover
• Title Page
• Copyright Page
• Contents
• Dedication
• Chapter 1
• Chapter 2
• Chapter 3
• Chapter 4
• Chapter 5
• Appendix A
• Appendix B
• Notes
• References
• Announcing the Business Expert Press Digital Library