Forecasting the Effect of Price Changes on Net Revenue across Different Market Segments (1997)

Price and cross price elasticity can be used to predict the effect of a price change on net revenue... shows elasticities of different service categories for NYNEX... among all service categories, local coin is most sensitive to price. Two of the principal applications of regression analysis are (1) forecasting the future values of one or more variables; and (2) identifying and quantifying interrelationships among variables. In 1970, New York Telephone (now NYNEX) initiated an ambitious project to develop a large set of regression models that would (1) quantify demand price relationships; (2) quantify demand cost relationships and; (3) forecast the future time path of revenues over six to eight quarters. By 1990, more than twentyfive models had been constructed to achieve those three objectives. The focus of this article is on the methodology and results of the demand models used to estimate price elasticity relationships. These estimates are used to determine the revenue effects of multiple price changes across service categories. The models (and data) are part of the public record (New York Public Service Commission, Case 28961). RATE MAKING AND MARKETING APPLICATIONS Price elasticity measures the relationship between quantity demanded (Q) and price (P). There are two forms of price elasticity: (a) own and (b) cross. Own price elasticity focuses on the relationship between the quantity demanded of a particular product (or service) and its price. Cross price elasticity measures the relationship between the quantity demanded of a particular product and the price of either a substitute product or a complementary product. Elasticity is defined in percentage terms (that is, the percentage change in quantity resulting from a percentage change in price). In the regulated utility industry, price elasticity measures are critical to the determination of revenue requirements, rate of return, capital planning and recovery and resolving intergenerational equity issues. In unregulated industries, price elasticity measures are needed to achieve targeted sales, market share, profit levels, proactive and reactive responses to competitors price changes and the time path of capital recovery. Marketers are particularly interested in price elasticity measures (own and cross since price is the only marketing mix variable that produces revenue. Own price elasticity estimates make it possible to calculate the revenue effects of price changes within a service category. While cross price elasticity provides a means of estimating interservice price relationships and the cannibalistic revenue reductions resulting from product line extensions. Table I provides and illustrative (and actual) calculation of the revenue effects of a $100 increase in the price of Private Line (dedicated access line between two or more points) service category. The own price elasticity effect results in only a $69.20 revenue increase in that category. On the other hand, the increase in the price of private line stimulates the demand for substitute services, namely, business access ($4.10), additional message units ($30.80) and intralata toll service ($4.50) resulting from the $100 price increase in private line. Calculating all own and cross service effects produces a bottom line net revenue effect of $87.90. DEMAND MODEL SPECIFICATION The construction of demand models involves the appropriate selection of (I) explanatory variables, (2) type of equation, (3) lag specification, (4) data, and (5) tests of evaluation. Demand models for specific services are specified with own price variables and cross (substitute and/or complement) price variables where appropriate and measurable. Table 2 presents a matrix showing most of the services that have been modeled and the demand-price interrelationships (that is, complementary or substitute) existing among the various company services. In addition to the service variables, the models are specified to reflect the systematic effects on demand of (I) prices of other goods and services (CPI or GDPD); (2) real personal income or business income; (3) market size or growth (number of households, population or a time trend); and (4) customer inertia or habit formation. …