Until (relatively) recently, electric utilities pretty much had a single mission: the generation, transmission, and monetization of electrons. It was a simple business model. The customer flips a switch, or claps their hands, or yells across the room to Alexa to turn on the lights, and the fulfillment process happens, literally at the speed of light. For many years, that was enough. But what happened before is not what happens today. Instead, we live in an economic marketplace where cable service providers provide home security, phone companies provide video entertainment options, drivers with some spare time provide rides from the airport, and Amazon provides, well, everything. Utility companies learned that within a fairly narrow space, they too can compete for share of consumers’ share of wallets for products that most consumers would have traditionally sought elsewhere. The “narrow space,” at least today, tends to center on either 1) products and services directly related to the home, such as things like home warranties, appliance repair and maintenance, home management and efficiency systems, and yes, home security; or 2) optional, voluntary programs like community solar. Configuring and ultimately pricing these types of products is a well-established, tried and true exercise in product development research. Got ballpark pricing questions? Use Van Westendorp Price Elasticity research. Want to see what consumers will trade off for lower prices? Use conjoint analysis. Want to offer complex pricing models based on tons of optional add-ons and choices? Use adaptive conjoint. Want to pick a winner among some product finalists? Use a monadic experimental design. The limitation with all these methods is that they generally yield relative consumer adoption estimates, not absolute ones. Relative metrics reliably show which product will outperform the others but doesn’t help much in populating that top line in the product’s pro forma. And obviously, it helps to know how many people will sign up for a community solar initiative—and at what price—before a utility can reasonably estimate their investments in hardware, marketing, insurance, rolling trucks, regulatory compliance, and, importantly, impact on their brand. In research for consumer package goods, the transformation from relative adoption estimates to hard forecasts of adoption and dollars has relied on mountains of historical data that model specific products in specific categories (e.g. a new flavor of potato chips in the salty snacks category). For new concepts like community solar initiatives, however, this history doesn’t broadly exist. Fortunately, many utilities do have historical adoption rates for many analogous, if antiquated, products. In the final, monadic stage of product development, a key question is about purchase intent. Generally speaking, the concept with the highest PI wins. Producing a forecast from these outputs, while far from an elementary exercise, is founded on best-practice modeling and marketing science. Adoption of some original products can be treated as dependent variables is a logistic model, with things like household demographics, marketing outreach, seasonal factors, and other characteristics as predictors to that outcome. A similar process can then be applied to the “new product,” using an outcome of “definitely would purchase” as the binomial target result and the current values (or, in the case of marketing effort, estimated values) along with their previous coefficients as the predictors. The final data science application involves scaling the model’s results to the adoption levels of previous products. In a nutshell, the modeling application for these utility applications is similar to the large-scale efforts applied in CPG and other sectors, except that the process tends to be unique to each utility, based on their history, customers, and many other factors. This fact doesn’t allow for an easy, out-of-the-box solution, but it does allow for a scientific, empirical, and defensible approach to forecasting new product sales.
Surprisingly, 2018 marks the 40th year of selling and marketing in my career. My original on the job training focused on sales methods and techniques. Naturally, I was introduced to the concept of center of influence for life insurance production. So, at the risk of boring you already here is a quick list of those original audiences recommended to me: Family, friends CPA/Lawyer Doctor/Dentist Business owners or leaders Government, school, church officials I observed many of my enthusiastic fellow newbies start building their networks of contacts. I also monitored the level of failure to reach the minimum goals. Most of the new recruits did not succeed. Luckily, I was tasked with a national direct mail effort by the CEO within 30 days of my recruitment. And it opened my eyes to the ability to reach out to highly qualified contacts who I did not know. In my small world, I was always the number 1 salesperson due to this knowledge. I normally state that if I had to depend on my center of influence that I would have failed miserably. I grew up in a rural area with lower socioeconomic conditions and very small population. There were 8 students in my grade throughout my K-12 education. In a job of numbers, that was very "slim pickins” Utilization of data, technology and analytics afforded me an infinite universe of influence by comparison. After careful consideration of my firm’s product, my product knowledge and the economic concerns at that moment in time, I could select the audience most likely to: a. need my services b. to listen to my presentation c. buy the policy and d. refer other buyers. Let me pause for a couple of different examples Challenge Agent, 22 years old. New BBA in Finance. No family/friend network. Life Insurance firm—Good Brand recognition, excellent disability policy and cash value life products. Method Selected the Surgeons in the radius area by the year graduated from Medical School. Used direct mail to present a major business issue for them—disability would cause a disruption in their cash flow which would possibly lead to loan defaults. Outcome The mailer produced a 1 percent response, 10 doctors inquired. All 10 set up appointments and purchased disability policy. Then each doctor offered a referral to their fellow doctors in their practice. In this case that was an average of 5. The agent had an ever- expanding circle of strong income professionals who would need his cash value solutions after the original loans for their medical school and practice were paid off. Challenge Agent, 30 years old. BBA in Marketing, making a career change. Senior market insurance services—Medicare supplement and retirement products Method Targeted the turning 62,63, 64, 65,66, years old audience in the radius area. Used direct mail to invite them to a seminar on Medicare. Medicare choices are extremely confusing to most consumers compared to their use of group health policies for most of their lives. Outcome 20 to 40 attendees per session. 50 percent would request a follow up meeting by submitting a complete financial profile stating their primary concerns. The close rate for Medicare Supplement policies is very high. And the same consumer also seeks the alternatives for their retirement funds—money management, cash value policy or annuity. The agent has an ever- expanding circle of satisfied seniors with retirement funds who eagerly recommend his services. Today, I have many more resources at my disposal to create a. custom audience b. write a personalized message c. conduct an omnichannel campaign d. predict the premium production I recommend that you build your center of influence by using data driven marketing. Direct Mail is the foundation of that effort as legislation has restricted initial contacts by phone or email. In the current digital age some young marketers dismiss the use of direct mail. I smile as I know that effective direct mail drives my digital flow of inquiries and initiates my center of influence. Happy Hunting, as my original sale manager used to say!