What is it like to be the next Amazon of energy efficiency? Soon we might have to ask Amazon.
In 2009 I co-developed a business model that uses behavioral techniques to promote energy efficiency investments. We published the business model in hopes that smart entrepreneurs would implement it.
Indeed ideas in the model are now appearing in businesses like Building Energy, Populus (now CLEAResult), Fiveworx and others I’m not at liberty to name.
As the authors of the business model anticipated, there have been obstacles. However the technology pieces are now coming together to implement the whole concept. Two of the important pieces are recent introductions from Amazon: Machine learning, and home services. Here’s how they fit.
THE BUSINESS model we proposed is called Efficiency Tomorrow. It is based on using choice architecture and a mentor model to guide homeowners and small businesses to make energy efficiency investments. You may recognize the choice architecture concept from Nudge by Richard Thaler and Cass Sunstein. The mentor model is what we have come to know as the trusted advisor.
The effectiveness of each of these concepts has been proven in the real world. We proposed putting them together in one technology platform.
Efficiency Tomorrow is primarily a software and marketing company. Its choice architecture directs customers to measures they can almost certainly accomplish based on their track record. When the customer falters, the mentor model provides support at the right moments from a pool of trusted advisors. Each time the customer tries a measure, the platform learns from the experience. Suggest, help, learn, next.
A barrier we identified was the ability to scale up as the platform serves large numbers of customers, vendors, nonprofits and utilities.
Efficiency Tomorrow could start as a concierge service within the territories of a few utilities, offering a dozen possible energy efficiency measures to targeted segments of interested customers. With some automation and less human intervention, the platform could serve several utilities offering dozens of measures to thousands of customers.
When hundreds of measures, thousands of utilities and millions of customers are active on the platform, it can’t rely on people and expect to keep up. The solution is to automate.
In its early days, Populus was sending energy advisors into homes to propose energy-efficiency upgrades. Almost all homeowners accepted the upgrades, and the advisors had a 70 percent success rate at persuading those homeowners to invest in more energy upgrades. But one-to-one, in-person interactions are slow and expensive. After its recent acquisition by CLEAResult, the company is trying to scale the same success rates without relying entirely on house calls. Company strategist Laura Hutchings says their approach is centered on adopting the most promising technologies without compromising service.
“As new technologies emerge or become more accessible, we are always looking for opportunities for those technologies to improve our current program delivery and further enable our scaling as a company,” Ms. Hutchings says.
SOFTWARE IS costly to build, and building an algorithm-intensive transaction platform is doubly so. But an interesting development will change the ROI of automation for complex projects like Efficiency Tomorrow. Amazon has released to developers its ADAPA Decision Engine, the big data mining, predictive analytics engine behind its very successful shopper recommendations.
When on Amazon.com you see, “Recommendations for you in Electronics,” you’re seeing the Decision Engine in action, the result of machine learning. Of Amazon’s millions of products, the decision engine picks the ones you’re most likely to buy. That capability is no longer proprietary – any developer can tap into it. It could very well be the answer to the scalability problem.
Another early-stage company, Fiveworx, is working on automating the choice architecture and trusted advisor functions. The company provides cross-promotional services to utilities, making suggestions to encourage consumers to make not one but five energy efficiency improvements to their homes.
“There is definitely a parallel between Amazon’s Machine Learning engine and our adaptive learning engine,” says company co-founder Patrick Hunt.
“Our trusted advisor model is actually not hard to scale,” Mr. Hunt says. “We are finding that meaningful, timely, and relevant communications help empower customers to take the initiative to engage with energy efficiency on their own. And the model assists us in identifying customers who need more help from an actual human being, enabling us and our utility clients to allocate resources according to where they’ll have the most impact.”
A well-known player in the automation of energy efficiency programs is Opower. The company’s latest software platform can target customers at life’s turning points with relevant offers described according to each customer’s attitudes toward saving energy.
“Amazon’s recommendation engine is something we aspire to and that we hear utilities talk about,” said Emily Bailard, said, when she was Opower’s director of energy efficiency solutions marketing. “But it’s not just about delivering the right offer. It’s critical to make sure the recommendations come at the right time for that person.”
TIMING IS everything, like providing help when needed. Another barrier we anticipated is the need for registered contractors in every region. After all, what good is it to recommend just the right measure only to have it fall flat when the homeowner can’t find a contractor.We can automate the rest, but this is where we need people to be involved.
Service businesses tend to be local, and recruiting them nationwide would be a monumental task.
Today the focus of Amazon Home Services is on things like installing a TV you buy from Amazon, but other services are available. AHS options are available to shoppers in selected cities (including Amazon’s home and mine, Seattle).
Search AHS for “energy efficiency” today and you won’t find much. Just wait. It will soon catch up to Angie’s List, which has plenty of efficiency contractors listed, including those who participate in utility rebate programs.
Rebates are a critical component of many measures. Without financial incentives, homeowners must bear more cost up front for a delayed reward in the years to follow – what behavioral economists would call a bad idea. So whether the contractor search engine of choice is Amazon or Google or Angie’s List, it will need to filter for rebates. It could even find them for customers.
“I think customers might use Amazon’s ‘Angie’s List’-like service for energy efficiency improvements,” says Mr. Hunt. “But until Amazon and its stable of contractors are certified by utilities, the customer will lose out on their rebate and the utility on their savings.”
EFFICIENCY TOMORROW was conceived as a residential efficiency platform. Its choice architecture and trusted advisor/mentor model could be applied to small business, commercial, and even industrial customers. More barriers await the bold entrepreneur who ventures there.
Building Energy operates a platform for commercial customers in cities where building owners are required to disclose the energy use index of their facilities. The Building Energy platform includes suggestions of measures for owners to undertake. But the path to implementing those ideas is not as straight as it is with homeowners.
“Commercial building sometimes have preferred contractors,” says Erik Larson, co-founder of Building Energy. “Sometimes there are unions to contend with.”
Nonetheless Building Energy has stayed focused on the commercial market and is expanding as rapidly as do EUI disclosure mandates.
WHETHER HUMANS will be obsoleted by technology in the sales process will depend on the company deploying the technology. Low-priced simple measures, like LEDs or air filters, can be ordered online. High-value goods and services sell best with a high-touch, high-trust approach.
“While technology is constantly opening new opportunities,” says Laura Hutchings, “our approach is to understand not only the impacts that the new technology has on energy saving, but also the impacts that it has on building relationships of trust and driving customer experience and customer satisfaction for our utility clients.”
Amazon’s ADAPA Decision Engine and Home Services are not directly linked, and there’s no promise that AHS will expose hooks for developers to use it. Amazon is dedicated to creating everything as a web service, so the capability is not far fetched.
Together these new services make Efficiency Tomorrow a concept that energy entrepreneurs can implement starting, literally, tomorrow.