As businesses, electric utilities enjoy what other businesses can only dream of -- full market acceptance. Almost everyone is buying what they are selling, as electricity is not only a necessity, but also an expectation. In an interesting turn of events, utilities must now convince customers to not buy so much of what they are offering. During the past decade, there has been much ado about conservation and for good reason: we are taxing the grid and we are hurting the environment. There is reluctance to build more power plants due to permit nightmares, prevailing 'not in my backyard' attitudes and exorbitant costs. According to Michael Dworkin, director of University of Vermont Law School's Institute for Energy and the Environment, "coal-fired power plants were expected to cost approximately $1 billion apiece to build, but the big ones are costing closer to $3 billion apiece." Thus, there has been more pressure on customers to curb consumption, which theoretically, sounds like a good idea, but many haven't changed their routines. With so many alternatives on the market, from heat exchanges on drain pipes to new and better lighting and photovoltaic systems, maybe the challenge is that the commercial customer does not know where to start and needs direction that fits with their business objectives: What will make the most impact? Will it affect my customer's experience?
Switching to LED light bulbs, turning off the printers during non-work hours, and installing energy-savor appliances are easy ways to decrease energy bills. Replacing equipment or performing a comprehensive facility upgrade varies depending on each individual business' situation, but are owners missing an obvious benefit? How does one business' energy efficiency compare to the business next door? Or the franchise across town? Certainly, it would be nice to have a guide on this journey. Fortunately, some utilities have already started to employ advanced analytics, which aggregate this type of information and tailor a plan for each business to reach its efficiency potential.
Understanding the characteristics of a business provides valuable insight into the account's use of energy. With this insight, a utility account's energy usage can be compared to a group of peers with similar characteristics, and outliers can be flagged for further analysis and investigation. "The need to identify customers for energy efficiency programs will continue to grow in importance as efficiency targets increase for utilities," said Vincent Graziano, president of RISE Engineering, one of the oldest and most established providers of energy efficiency services in North America. "In a very short time, an enhanced analytics program proved capable of making a dramatic increase in the effectiveness of our marketing efforts and customer acceptance of the energy efficiency programs," said Graziano.
Typically, utility data consist of the business' name, primary contact, phone number, address and type of business. The utility may know a client account is a restaurant, but probably doesn't know how large the premise is or whether it's a sit-down or take-out restaurant. How about if it's a franchise? The number of employees, its chain affiliation, other metered service for the account and whether or not it's a seasonal operation need to be part of any intelligent usage analysis.
This lack of information leads to another roadblock to outreach: the current benchmarking process. Utilities review year-over-year data on a business. Did a specific business use more or less energy last year? Have there been unexplained spikes or troughs in consumption? If there were more data points to consider and analyze, the utility would be in a better position to offer customized information about energy usage and recommend energy-efficiency programs; thus, truly offering something useful and economically sound to the business owner.
The utility needs its customers just as the customer needs the utility and in the current economic climate, every penny saved helps the utility's customer and therefore helps the utility. Consistent and substantive energy savings contributes to lower retail prices, which may lead to more shoppers, which may lead to more jobs and therefore better economic conditions for all including the utility. Simple steps can lead to greater benefits for communities and utilities alike. Touting conservation and supplying customers with the tools to make informed decisions about their energy usage, is a huge win-win for the utility and small businesses.
The rollout of Automatic Meter Reading (AMR) and Advanced Metering Infrastructure (AMI) provides utility-service companies the opportunity to improve customer service and increase operational efficiency. However, these programs are just pieces of a larger puzzle. Collecting such an unprecedented amount of data about energy use will be useful only when the data compiled is analyzed and turned into actionable intelligent information. Fortunately, there are new and improved ways of achieving the goal -- starting with enhanced analytics.
Ratepayers don't have to wait for the arrival of AMR/AMI. Right now utilities have the ability to aggregate information from a myriad of accessible sources, including public records, business yellow-page listings, among others. Utilities can also, make energy-usage benchmark comparisons of businesses against other businesses of that genre, geographical area, and other franchise operations in that chain. Very sophisticated algorithms are already available to link utility customer records, with very limited data, to information-rich databases acquired from third parties and compiled from Internet information. These new tools and processes can and will significantly change the way that small commercial energy customers are contacted and educated about energy-saving opportunities. The new and innovative approach to reaching out to small commercial customers with intelligent messages about energy conservation for their business is made up of the following steps:
Data Enhancement -- Utility customer data are enhanced and improved by acquiring third-party data and integrating it using sophisticated, pattern-matching algorithms. Business codes and contact information are validated or corrected, while a myriad of additional operational information is added. This enhanced data set on its own improves outreach efforts, and also becomes the basis for more advanced customer modeling steps.
For example, if a utility wants to reach out to a restaurant to offer a custom energy-efficiency program, analytics are used to compile public information on that location and compare it against other restaurants in the area and other franchise locations. This analysis provides a more complete picture of where that small business stacks up, thus leading to customized energy-efficiency outreach, possibly lowering the bill for the owner.
Account Ranking -- A key step in delivering any energy-efficiency program is aligning the correct accounts with the correct program and then ranking them based on the objectives of the program. Program alignment can be as simple as segmenting based on the corrected business codes or as complicated as mining for specific energy-use patterns such as for data centers or refrigeration. Once aligned, the proper ranking can be established based on parameters added during data enhancement. Ranking options include energy use per square foot of number of employees, amount of seasonal variation, etc.
Participation Likelihood Modeling -- The final step in account prioritization is predicting which customers will participate in a program. Patented, customer-behavior models are used to determine the program-participation likelihood of each customer. These models learn from the outcomes and attributes of previously-delivered programs to determine which accounts in the current program group have similar characteristics. These predictions can be made using a utility's own program data or outcome from a national customer behavior characteristics database.
Many U.S. states are in the process of defining energy-efficiency deployment strategies to meet ever-increasing goals by state governments requiring them to include this large, energy-consuming group in their plans. The national average retail price of electricity has increased 16.5 percent from 7.6 cents per kWh in 2004 to 9.1 per kWh in 2007. These types of increases are difficult to pass on to the consumer, especially during a down economy.
Today, thanks to sophisticated data analytics, utilities are now able to successfully target and educate customers with the greatest needs in a customized manner. Consumption models, segmentation, behavior modeling and participation-likelihood metrics can work together to give the utility a much better approach and success in implementing vital cost-saving energy programs for the diverse and growing small business sector. Make sure your utility is working for you and with you.