Connecting to the Customer: DM's role in the Deregulated Energy Marketplace
But when it comes to the energy industry, who comes to mind? Naturally, Homer Simpson, Mr. Burns and the rest of the radioactive crew from the Springfield Nuclear Power Plant. Not exactly the image energy marketers want to project when trying to convince current customers to maintain loyalty to their current supplier or to persuade new customers to switch suppliers.
Projecting a decent public image is but one of many challenges energy companies face in this newly deregulated environment. For an industry that is now open to competition, there is still a surprising amount of government regulation to deal with. Aside from that massive headache, there remains the issue of distinguishing one's product offering from the competition. Decisions need to be made whether to concentrate marketing efforts toward new customer acquisition, current customer retention, new product offerings, business vs. consumer marketing or a combination of these and other strategic directives. Whatever direction an energy company decides to take, intelligent direct and database marketing methods can play a major role in the eventual success of its efforts.
In their pre-deregulated monopolistic incarnations, most energy companies have not had a pressing need to concern themselves with what we direct marketers call the total customer relationship. For the most part, customers needing the product being supplied by the utility company have had no choice in utility suppliers, and customers were essentially forced to use the local utility's services. As such, many regulated industries concentrated their efforts on internal transactional and operational processes. An accurate meter reading was more important to the local electric company than gathering useful marketing information on the folks who happened to live in the structure being metered.
When delving into the brave new world of the deregulated marketplace, a good place for energy companies to start their marketing efforts is to perform some intensive analysis on their primary resource: the current customer base. As mentioned above, most, if not all, energy companies have fairly comprehensive history on their current customers' behavior as it relates to energy use and bill payment. Whether deciding which customers to retain or which new customers to pursue, it would be of significant benefit to initially divide the customer base into distinct behavioral segments. Just by using criteria such as high/medium/low energy use and good/bad payment history could yield six different behavioral segments. After further dividing the current customer base into consumer and business categories, we now have 12 behavioral segments.
Unfortunately, one piece of past behavior that can't be measured as accurately as energy usage or payment history is retention. Since the customer base has been, for lack of a better description, "captive," the companies all have a perfect retention record. However, there are some methods in which to project a customer's propensity to stay with his current energy supplier.
With a segmented view of the current customer base, more educated decisions can be made regarding new customer acquisition and/or retention strategies. The first question that an energy company may want to ask itself is "What type of customers do we want to keep, and what would it take to keep them?" The next question they may want to ask is "What type of customers do we want to replicate in a new market?" The answers to these questions will help drive strategic marketing direction.
For instance, a company attempting to retain the top energy-consuming industrial businesses in the current territory will employ a very different marketing plan than the company that is trying to acquire new, mid-level energy consumers with good payment history in the residential market. In the latter instance, the next step is to identify the demographic characteristics of the current customer base that match the target market. This information should be the basis of a predictive model. However, there are more questions that need to be answered.
Energy marketers need to decide if they want to target only those prospects who have indicated they want to receive information on energy companies. However, in many cases a customer will be allowed to switch energy suppliers even if they were not among the initial group of hand raisers. There are generally government-dictated restrictions on the percentage of customers who will be allowed to switch companies in a designated period. This leaves the energy marketer with a hard choice: Should we market only to the hand-raising customers who we know have indicated interest and will likely be allowed to switch suppliers? Or should we target those prospects that match our targeting criteria and hope that the switching limit is not reached?
While most energy companies have good behavioral information on their current customer bases, they generally don't have comprehensive demographic information on their clientele. Also, when marketing to a new territory, there is no prior response history to use as the dependent variable in a predictive statistical model. But there is an alternative to flying blind in a new territory. First, there are many companies (many that advertise in this periodical) that specialize in overlaying vast quantities of demographic information on a customer file. Once this is completed, a predictive model can be built using past energy use and credit history as the dependent variable. For example, an energy company can identify current customers who have used an average of between 750 and 1250 kilowatt hours per month over the last year. They can then append syndicated demographic information to build a model that will help distinguish these customers from the rest. Once a target territory is identified, many of the companies that sold you the demographic information on the current customer base will be happy to sell you both names and numbers on the new market.
Beyond standard demographic information, some companies specialize in selling syndicated attitudinal data. An example of this type of information would be propensity to switch utility companies. While this certainly looks extremely valuable at first glance, there are a couple of things to keep in mind. First, this attitudinal data is generally much more expensive than standard demographic overlay data. Also, this information gets generated via a sample of telephone and personal interviews, as well as credit data. These results are subsequently used as input to an attitudinal model. So, you would be buying modeled attitudinal data, which is not nearly as hard and fast as syndicated demographic information. Also, the attitudinal information being sold may not provide the knowledge that an energy marketer really needs. Are you trying to target customers who are price sensitive? Environmentally aware? Angry at the current energy provider?
While it may be more difficult to get the answers to these questions, it may also prove worthwhile to develop and implement more "personalized" attitudinal segmentation. Again, while not quite as precise as a demographic segmentation, this approach could very well give an energy company the edge they need to succeed in a highly competitive marketplace.
Roger Marcus is a senior consultant with Nykamp Consulting Group, Chicago. His e-mail address is firstname.lastname@example.org.