Published on November 27th, 2014 | by Guest Contributor1
Data Analytics Reducing Solar Customer Acquisition Costs
November 27th, 2014 by Guest Contributor
By Jake Rozmaryn
As the US solar industry continues to see year-over-year growth, solar companies are met with a unique set of challenges to take advantage of this growth and market opportunities. Whether focused on generating sales via door-to-door efforts or traditional marketing channels, all solar companies stand to benefit from looking at the data behind their leads and existing customers, to help streamline future sales. The need for analytics in solar sales is of particular importance as solar systems have a “unique selling proposition and typically represent a one-time sale,” according to Marc Guy, CEO of Faze 1. Robbie Adler of Faraday saw the need for their analytics platform as the solar industry continued to see a “dramatic decrease in hardware costs over the past decade while soft costs remained flat.”
Marketing to clean energy customers is fraught with inefficiencies, according to experts in data analytics for clean energy and, according to Guy,“80-90% of homes” currently contacted by solar companies are “immediately unqualified.” Pasi Miettinen, CEO of Sagewell, Inc. calls attention to the “wastefulness” in spending on unproductive sales leads due to a lack of sufficient differentiation and filtering. Miettinen highlights the need to “define the best customer,” and reach “high value and satisfied” customers.
Because of its greater variability and complexity, the use of analytic tools and techniques has been most useful in the residential solar market, says Dr. Beau Peelle, President of Clean Energy Experts*. Peelle also calls attention to the importance of big data in defining and targeting qualified customers. As he notes, the combination of user profiles and activity data with geographic information is particularly effective in “producing higher quality leads” in clean energy.
Peelle claims that Clean Energy Experts has “tripled the production of qualified customer prospects in solar,” and the cost per customer acquisition for solar companies using their analytics platform “has dropped by 30-60%” due to resulting efficiencies in their sales process. Peelle added that when overhead costs are factored in, “fully loaded customer acquisition costs” of their clients have declined even further.
In an effort to better target higher value solar customers, Guy notes that Faze 1 focuses on those customers that have already purchased solar and on homeowners with high credit scores, high interest in solar, and other key attributes. The use of such data can help clean energy businesses generate leads that are “85-90% more qualified, which is two to three times higher” than the traditional qualification rate. The company “aggregates solar installation information, demographics and sensing information, and analyzes the likelihood someone will purchase solar.”
By applying analytics to customer acquisition Adler shares that the “Faraday data management platform uncovers superior customer acquisition strategies—using a map-driven platform—improves lead conversion rates for companies selling considered purchases, such as solar, into the home. The Faraday platform has helped leading organizations in the energy industry win new business more effectively, producing customer conversion rate lift of over 30%.”
In addition to analyzing data to attract future customers, it is imperative to know who your current customers are and what drove their buying decision. Miettinen underlines the value of examining the “entire lifecycle of customers” using analytics tools. By doing so, clean energy companies are able to identify customers that are best suited to their sales organizations in order to “produce the best result.”
Sagewell “uses big data and predictive analysis extensively, in order to estimate the value of a customer and determine key attributes of customers that work for some time,” says Miettinen. He notes that these attributes “are continually evolving,” and that “adjustable models need to be created that track sales outcomes over time and examine geographic, seasonal, and other factors that account for varying sales results.”
No matter what resources solar companies utilize, big data and intelligent analytics are helping to lead clean energy companies to better customer prospects. This renders the customer acquisition process far more cost-effective. Miettinen notes that this process creates a “virtual referral circle,” where your one-time customers now become advocates for your product, spread the word and drive more qualified leads to your lead pipeline—traditionally at a greatly reduced acquisition cost. While better data on qualified customers alone won’t necessarily lead to significant sales boosts, it’s a starting point where all clean energy companies stand to gain.
Amanda Brodbaek and Michael Mascioni contributed to this article.
Further reading: SEIA Solar Industry Data.