What Utilities Can Gain From Customer Profitability Analysis by Chris Trayhorn, Publisher of mThink Blue Book, May 14, 2007 Introduction Recent moves toward a more competitive retail environment, as well as increased performance expectations from shareholders, have focused utilities on improving the customer experience and, to some extent, on improving customer profitability. However, many utilities continue to have only a very limited understanding of the unique financial costs and benefits associated with each customer. While regulatory requirements and the societal obligation to provide basic utility services will certainly continue, even for unprofitable customers, we believe utilities need to better understand profitability at the customer level in order to improve the customer experience and contribute to the bottom line. This article begins with a discussion of the benefits of utility customer profitability analysis, then outlines a unique approach to modeling profitability for individual customers and customer segments and provides an example of some successful profitability analysis results. All utilities should consider performing this type of analysis to support their efforts to improve the customer experience, increase revenues, reduce costs and achieve profitable growth. Value of Customer Profitability Analysis Customer profitability analysis is most effective when combined with both demographic and customer satisfaction data. The result is a powerful tool that utilities can use to create new value in four primary ways. Know What Is Driving Profit and Loss This seems very basic, but utilities often dont understand what drives their profitability. As a result, they have difficulty answering some important questions, such as: Are sales to residential customers during weekday peak demand periods resulting in significant losses? How do repeat calls from certain customers to address basic billing questions affect the profitability of those customers? Should programs to drive customers toward online billing programs be expanded, or do those programs result in a financial loss? Customer profitability analysis can help answer these and other important questions. Earn the Allowed Rate of Return A surprising number of utilities earn less than their allowed rate of return for at least a portion of their customer base. The reason is that most utilities in this situation look at their customers as one uniform group, even though the failure to achieve the allowed return may, in fact, be due to low returns for certain customer groups, geographic areas and/or products and services. Customer profitability analysis can reveal those segments with the lowest returns so that the utility can take a focused approach to addressing the root causes and increase the likelihood of earning the allowed rate of return. Target the Marketing of New Products and Services Most businesses selling goods and services to the mass market perform extensive market analysis to identify the targets with the most potential. In contrast, utilities whose offerings go beyond the traditional electric and gas service often possess limited information on which they must base critical decisions. By combining customer profitability analysis with demographic data, utilities can identify the most promising prospective customers and more efficiently market new products and services, resulting in profitable revenue growth. Focus Process Improvement and Cost Reduction Efforts Utilities often use benchmarking to identify functions with inefficient processes and excessive costs. Unfortunately many utilities have discovered an inherent weakness in benchmark data: It can mask financial losses driven by the unique characteristics of each utilitys rate structures, cost profiles and customer behaviors. Customer profitability analysis accounts for these profit drivers to better pinpoint areas of inefficiency and opportunity. Traditional benchmarking can also miss some of the most costly inefficiencies those resulting in dissatisfaction among the utilitys most profitable customers. When combined with customer satisfaction surveys, customer profitability analysis can show which processes are most troublesome to customers both driving profits today and most likely to buy new products in the future. Taken together, these applications of customer profitability analysis can provide substantial value through reductions in costs, increases in revenue and improvements in customer satisfaction. Profitability Analysis Approach While there are several ways of talking about customer profitability, we define it in terms of Gross Margin, as follows: Customer Gross Margin = Net Revenue – Cost of Goods Sold – Transaction Costs In this formula, Customer Gross Margin is the estimated gross profit for each customer. Note that this approach does not take operating costs into account. This is because a fundamental goal of the analysis is to understand how customer behaviors drive profitability and how the utility can change the way it does business to enhance customer profitability. These customer behaviors and utility actions will not typically drive operating costs, but operating costs could certainly be factored in to the calculation to measure net profit. Net Revenue represents the amount paid monthly by the customer for utility services, as derived from actual billing data, less any refunds or other items that should be netted, and excluding taxes and fees that are passed directly through to the customer. Cost of Goods Sold measures the cost of the electricity and/or gas provided to the customer and can be estimated at the individual customer level from a combination of customer usage data and electric and gas market prices. Transaction Costs include meter-reading costs, billing costs, demand-management program payments and related costs, and call center costs, all of which can be allocated to each customer based on his individual activities and attributes. One key challenge in calculating a customers gross profit is matching energy usage with market prices to estimate the cost of goods sold. Ideally, hourly meter data for each customers usage would be available, but this information actually remains quite limited. Instead, a reasonable estimate of energy usage can be developed by analyzing representative load curves by day of week and time of day for various customer types and then allocating total monthly usage data from customer billing records into more discrete time periods. If the representative load curves are available on an hourly basis, they can be used to estimate hourly usage and then hourly prices for energy can be applied to estimate cost of goods sold. Alternatively the day can be divided into periods of, for example, six hours, and then the average energy price for each period can be applied to estimate the cost of goods sold. The diagram in Figure 1 shows a highlevel view of the typical data flow for a customer profitability model. Customer data is combined with unit cost data in the analytics engine to calculate gross margin, and then data on customer demographics and satisfaction are merged to populate the database storing the analysis results and key supporting data. The results and supporting data can then be extracted for reporting and further analysis outside of the model. Typical Profitability Analysis Results Once the customer profitability model is developed and the data is merged and analyzed, the results can be put into reports to support further analysis. The most basic report provides information on the distribution of profitability for all customers, as shown in Figure 2. In Figure 2, derived from the actual results of one electric and gas utility, roughly 15 percent of customers prove unprofitable, with the loss reaching up to about $100 per month for some. This is not too surprising, given that cross-customer subsidization is well-recognized. The more interesting and actionable results are revealed when the unprofitable and the most profitable customers are analyzed in detail. Consider the diagram in Figure 3, which compares the relationship between the cost of goods sold and net revenue for customers with the highest gross margin versus those generating a loss. The result, again based on a real electric and gas utilitys data, demonstrates that transaction costs are not a substantial factor in driving profitability for these particular customers. Instead, for the loss customers, the high cost of goods sold is the primary driver of negative gross margin. This suggests that these customers may be particularly heavy users of high-cost energy during peak demand periods. Based on this analysis, the utility should consider targeting these customers for some kind of demand-management program. In fact, the utility can use the profitability model to establish the level of demand-management payments to these customers that would result in an increase in profits. In contrast, the high-margin customers use a greater portion of their energy during non-peak periods. This result implies that demand-management payments to these customers may actually reduce profits. More detailed analysis of the usage patterns of these customers is required, but the customer profitability model has already uncovered potentially valuable insights into profit and loss drivers as well as pointing to opportunities for improvement. Conclusion Customer profitability modeling can provide valuable insights into the factors that impact profits and losses within a utilitys customer base. These insights can, in turn, drive specific actions to increase profits by: Changing customer behaviors to enhance profits through program and policy changes; Focusing process improvements and technology investments where they will have the most bottom-line benefits; and Targeting new products and services to those customers most likely to buy them under profitable terms. Because most utilities already have much of the required data, the costs of profitability model development and analysis are moderate. Therefore, we believe this analysis easily pays for itself, and should be considered a fundamental capability for utility management and planning. Filed under: White Papers Tagged under: Utilities About the Author Chris Trayhorn, Publisher of mThink Blue Book Chris Trayhorn is the Chairman of the Performance Marketing Industry Blue Ribbon Panel and the CEO of mThink.com, a leading online and content marketing agency. He has founded four successful marketing companies in London and San Francisco in the last 15 years, and is currently the founder and publisher of Revenue+Performance magazine, the magazine of the performance marketing industry since 2002.