Companies in competitive industries routinely collect information about their customers through a variety of sources (em including surveys, national census, and government and private sources. Such customer information and its applications are jealously guarded secrets, rarely shared with others in the industry. Customer information is not limited to expenditure on a company's products or services, but usually includes a customer profile. A customer profile attempts to segment groups of "typical" customers by, for example, the production structure of a company or the socioeconomic background of residential customers. Along with other economic variables, customer profiles are often used to predict the reaction of customers to new and existing products and to project future growth for a product's market.
Unfortunately, there is a dearth of information on utility customers, with the possible exception of large industrial customers. A captive market creates no imperative to know the customers. Traditionally, the only information utilities collected on their customers came from customer surveys in compliance with mandatory demand-side management (DSM) or least-cost pricing requirements. In a many of such surveys, the aim was not to "know thy customer," but to fulfill the stipulations of the DSM or integrated resource planning programs.
The emerging competitive market calls for a much greater level of customer information as well as other intelligence. Thanks to regulatory requirements at the federal and state levels, a wealth of information is available about the cost structure of electric utilities. Information (em from balance sheets to plant-level costs (em is readily available through mandatory filings made by utilities to state and federal agencies as well as through voluntary information provided to trade organizations such as the Edison Electric Institute (EEI). Indeed, several enterprising information companies have packaged this information with slick user interface, analytical capabilities, and report-writing modules on CD-ROM.
Take National Data . . .
The WEFA Group has developed models that provide information on product expenditure based on an industry's production profile and consumers' socioeconomic profile. Recently, we applied one of our models to develop market information for electric and gas utilities on commercial and industrial customers.
The core of this model is the national input-output table. Input-output accounts provide a comprehensive set of data that records the deliveries of industrial outputs among industries and to final users, and the purchases of inputs from industries and suppliers of primary factors. These accounts are commonly presented as a table in which each industry is recorded twice: 1) as a row showing the distribution of output, and 2) as a column showing the purchases of inputs.
The table distinguishes between intermediate and final users of industrial outputs and purchases of intermediate (produced) and primary inputs. Specifically:
s deliveries of sectoral outputs to final demand (The sum of these deliveries is equal to the gross national product.)
s deliveries of outputs among all producing sectors in the economy
s purchases of primary inputs by each producing sector (These transactions are sometimes disaggregated into component categories such as employee compensation, depreciation payments, indirect business taxes, and profit-type income.)
s payments for primary factors by final expenditure categories.
Including these transactions is necessary to make the totals of an input-output table consistent with national income and product aggregates, but alternative accounting procedures are usually employed in empirical input-output tables, which identify these transactions with value added and final expenditures for sectoral outputs.
The current benchmark U.S. input-output table was projected using a modified version of the RAS technique.1 We used historical data and projections of gross output, intermediate sales, and intermediate demand from our U.S. macroeconomic and regional models to balance the row sums (intermediate sales) and column sums (intermediate demand) using an iterative procedure. By repeating the procedure for each historical year and over the forecast period, we projected the table out to 2000.
Turn it into
Regional Data . . .
We then converted the national table to reflect a regional economy, assuming that production technology for each industry in the region matches that of the nation as a whole. County and state models provide estimates and projections of employment for all 4-digit Standard Industrial Classification (SIC) codes. These models use data reported by the Department of Commerce in County Business Patterns, as well as data published by the Bureau of Economic Analysis and Bureau of Labor Statistics (BLS). The resulting data set replicates all data reported by the BLS, and then supplements the data with WEFA estimates. Through-out the process all adding-up
consistency is maintained (em i.e., all three-digits sum to two-digits, and all state data sums to national data by industry. Where there is a discrepancy due to nondisclosure of data (e.g., where there is only one company in that 4-digit SIC in the region and, therefore, the sum of employment at the 4-digit SIC level does not equal the employment reported at the 2-digit level), we use alternate company information databases to fill the "hole." In the absence of regional output measures, we use employment (as a proxy for output) along with regional industry productivity growth measures to calibrate the table.
The regional input-output table is a detailed accounting of flows of goods and services in the region and provides a wealth of information about the local economy. The columns of the regional table provide a detailed profile of (4-digit SIC) local industries. An individual column in the table enumerates what that industry purchases in goods and services from other industries. From this, market researchers can estimate an industry's expenditure on various inputs that consume electricity.
Getting to sales (quantity) of electricity from industry expenditure data is far more complex and labor intensive. We use a variety of sources to obtain rate information by customer class and size for each IOU in the country. At best, these rates are averages across customer classes and size. The rates are then applied uniformly to customers based on class and size in the utility's service territory, resulting in estimates of sales of electricity to each of the 4-digit SIC industries in the region. The projected input-output tables furnish the growth in electricity expenditure for each industry in the region.
And Apply it to Your Market
For our discussion here we will focus on California's computer equipment industry. California is the leader in deregulating electric utilities, and the computer
equipment industry is at the cutting edge of technology. This makes trends in electricity consumption by the computer equipment industry of particular interest to IOUs in the state (and neighboring states) as well as to nonutility generators and independent power producers.
Table 1 shows only those counties where the industry is present. In 1994, the computer equipment industry spent $85 million on electricity. As expected, Santa Clara County ("Silicon Valley") accounted for over one-half of the total. The industry's expenditure on electricity is expected to grow at an annual rate of 5.27 percent over the next five years, reaching $116 million by 2000.
Table 2 shows electricity sales computed from rates obtained for each IOU and municipal utility from EEI, FERC Form 1, the California Public Utilities Commission, and several other sources. Though sales to the computer equipment industry declined from 1990 through 1993, they picked up thereafter and are expected to grow at an annual rate of 2.77 percent over the next five years.
Figure 1 (see next page) highlights some of the information from Table 2. Such maps are used by marketing and sales departments in other industries as an effective visual tool to target areas for their goods and services. The maps can identify geographic areas of high growth by customer segment, helping strategic planners focus their resources to realize the maximum gain for their effort.
The information compiled above contains the information a utility needs to devise an offensive or defensive strategy for market share in a competitive environment. Using the empirical framework this intelligence provides, utilities can identify and evaluate customers who might move to another electricity provider, as well as industries with high margins and geographic concentration of load.
Geographic load concentration indicates the possibility of regional and/or industry buyers' coalitions. Regional and industry coalitions are common in the deregulated gas market. We can expect similar coalitions to emerge in the electricity market as well, especially among commercial customers. For example, a coalition of businesses in a large shopping mall or an office building could aggregate enough load to qualify for retail wheeling. Such concentrations, therefore, constitute an important piece of information for strategic planners and market researchers.
Given estimates on electricity sales and a utility's and rival company's rates, it is a relatively straightforward exercise to compute the marginal cost or benefit a customer may derive by switching its electricity supplier. The input-output table enables exact dollar estimates of the savings an individual company or a coalition of customers can expect. Table 3 presents a partial analysis of the competitive situation of Utility 1 vis- -vis four other utilities.2
The first column in Table 3 is the estimated purchase of electricity by the corresponding row industry in 1993. Based on 1993 rate information, stratified by customer class and size for Utility 1 and Utility 2, the second column computes the electricity cost savings (-) or increase (+) an industry would experience by choosing Utility 1 over Utility 2. The third column is the percent reduction (-) or increase (+) in cost of goods sold for the industry. The remaining columns compare Utility 1's competitive position to that of three other utilities
As a latecomer to a competitive environment, the electricity industry has access to a wealth of market research tools developed for other industries. In addition, the industry can learn from the experience of other recently deregulated industries. Aside from minimizing costs, successfully competitive utilities must expend the necessary effort to learn about their customers. And not merely the current needs of their customer, but their future needs as well. t
Virendra Singh is vice president of utility services at The WEFA Group, an international economic forecasting and consulting company. Dr. Singh has a doctorate in economics from Temple University.
.Bio1 See Input-Output Analysis: Foundations and Extensions by Ronald E. Miller and Peter D. Blair: Prentice-Hall, Inc., 1985, for a description of the RAS technique.
2 Rate data was supplied by five utilities in the west, all of which are in a position to supply customers in Los Angeles County.1 See Input-Output Analysis: Foundations and Extensions by Ronald E. Miller and Peter D. Blair. Prentice-Hall, Inc., 1985, for a description of the RAS technique.
2 Rate data was supplied by five utilities in the west, all of which are in a position to supply customers in Los Angeles County.
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