“You can’t solve climate change only with nudges, but you can’t solve climate change without them.” – Richard Thaler, author of Nudge
This post is about how I used Loss Aversion as a primary behavioral lever in launching a new energy efficiency product at Opower. The product is called Behavioral Load Shaping and currently serves about 500,000 homeowners across 5-7 major utilities in the US. Our team built and launched a product from 0 to 1 using behavioral design, made it easy for homeowners to save money and for utilities to manage a cleaner electric grid.
Quick primer on Opower: it’s a SaaS customer engagement platform for utilities, leveraging behavioral science to motivate homeowners to reduce their electricity and gas consumption. It’s one of the few success stories of the Cleantech 1.0 boom and was acquired by Oracle Utilities. Opower takes homeowners’ electricity and gas usage data, analyzes it and sends homeowners data driven reports with behavioral nudges on how to save energy. It is a highly effective and low cost solution for utilities and the company is a leader in the industry.
Here’s the TL;DR of this article:
The Problem
Electric utilities are rolling out Time of Use electricity rates to encourage homeowners to use less electricity during peak hours. Their goal is to reduce peak demand but customers don’t understand the rate and feel ripped off by the utility.
Behavioral Levers used to help customers and the utility
- Loss Aversion – Instead of emphasizing the potential savings by reducing electricity usage, we focused on the penalty associated with high consumption during peak hours.
- Self Comparison – We showed customers a week over week comparison of their peak electricity use. If they used More the loss aversion was amplified (ie more painful), if they used Same or Less, they were proud of themselves and would continue their behavior.
The Results
The product was designed to move two metrics: Reduce Peak Electricity Use and Increase Customer Satisfaction. Randomized Control Trials showed that customers who received Opower’s data driven and behavioral emails used about less electricity during peak hours versus customers who didn’t receive our communications. That kind of reliable reduction in peak demand is meaningful to utilities.
For example, homeowners at Arizona Public Service who received behavioral emails shifted over 250 MWh to off-peak periods between July and September 2021 and about 1 MW of peak demand reduction between 3-5 p.m., which is the hottest time of the day. Furthermore, satisfaction with among participants grew 13% while dissatisfaction decreased by 33%. And that was for only 40,000 customers, they are now expanding the program. The product hit both metrics, just by nudging.
“When you look at the bigger picture, this program was part of a 150-megawatt DR portfolio – that is a power plant. A power plant that didn’t have to run. That’s meaningful, that’s value.” Kerri Carnes, Director of Customer to Grid Solutions, APS
The Problem
When I arrived at Opower/Oracle, I was confronted with a very juicy problem. Our electric utility clients were increasingly concerned with how to manage Residential Peak Demand – the surge of electricity consumption that occurs in the late afternoon to evening hours of the day.
Think what happens in every home across America between 3pm – 8pm on the weekdays: people come home and rapidly start using their appliances. Utilities have to purchase enough electricity and maintain their transmission/distribution system to deliver all this electricity. Rocky Mountain Institute estimates that utilities will spend $1 trillion over the next 15 years to manage the peaks.
What’s worse is that electricity that is delivered during peak hours is the worst kind – it’s high cost and high emission electricity. This is because short duration “peaker plants”, which are only activated during peak hours, are inefficient and expensive to run.
So, utilities across the country are rolling out Time of Use electricity plans to homeowners. This means that the price of electricity costs more during peak hours and less during off peak hours. The utilities assumed that their customers would behave rationally and use less during peak hours but at Opower we knew that wouldn’t happen!
How Loss Aversion and Self Comparison helps reduce Peak Demand
In order to build the right solution for this, we ran a Design Sprint upfront to empathize with Homeowners who were on a Time of Use Rate plan. Through our customer interviews we found that most customers:
- Don’t even understand how a Time of Use Rate plan works,
- Whether they are on it
- Feel the utility is just ripping them off!
That laid the foundation for our prototyping with two principle requirements: educate customers how a Time of Use Rate plan works and motivate them to use less during peak hours. I won’t go into detail on the prototyping in this post but what we found that resonated most with customers was using Loss Aversion and Self Comparison.
Loss Aversion
A quick primer on Loss Aversion. Loss Aversion is a behavioral concept that refers to the tendency of individuals to feel the pain of losses more intensely than the pleasure of a gain. Humans are generally wired to avoid losses rather than to acquire equivalent gains.
We decided to use “Loss Framing” terminology when educating homeowners how their TOU plans work. That means we presented energy consumption in terms of potential losses rather than gains. Instead of emphasizing the potential savings by reducing electricity usage, we focused on the penalty associated with high consumption during peak hours. We tested many phrases and ultimately landed on “Electricity costs XX more during peak hours”.
We wanted to make it seem painful for someone to use electricity during peak hours. This is the same messaging that Uber and Lyft used to use during peak hours of traffic (hey, great artists steal right?). And our prototyping showed us that this message resonated the most and got customers to say “Oh ok, I’m losing out on cheap electricity during off peak hours, so that’s when I should run my appliances. That makes sense.”
Here’s the catch – for most homeowners whether they use electricity during peak hours wasn’t going to break the bank. In fact, we found that most homeowners don’t care if their utility bill fluctuates by $15 – $25.
So, even though Loss Framing was resonating during prototyping, we felt we needed to do something more.
Self Comparison
The actions that we want customers to take are very specific – use major appliances such as HVAC, washing machine, dryer, dishwasher, EV, pool pumps, etc – during off peak hours. Most homeowners don’t remember when they used their appliances and they definitely don’t remember the time of day. Furthermore, we know that our email communications are strongest (i.e. more open rates, click rates, energy savings) when it starts with a data-driven insight at the very top.
That’s why we decided to show them a weekly self comparison of how much they are using each week. If they used more during peak hours this week, the impact of Loss Aversion is amplified. If they used less during peak hours this week, then they are proud to see that message and continue the behavior.
Self Comparison as a behavioral lever, for the win.
The Results
We designed and built the product to meet two primary metrics for our utility clients: reduce electricity use during peak hours and increase customer satisfaction.
Reduce Peak Electricity Use
The randomized control trial is the gold standard to test whether a behavioral product is working. By comparing hourly electricity usage data between homeowners with our product versus those without, we could analyze at a granular level what was happening.
A completely passive and behavioral tool to reduce peak load is a tremendous help to utilities. While this isn’t a silver bullet to reducing peak demand (there isn’t a silver bullet), a customer engagement product is absolutely part of the solution.
Increase Customer Satisfaction
It’s not often that homeowners have something pleasant to say about their electric utility. But after receiving Behavioral Load Shaping communications, customers had very positive things to say about their ol’ electric company. Overarchingly, customers appreciated that the utility was educating them and trying to help them save money. We also heard people joking about how they were now nagging their spouses to be better stewards of their electricity consumption.
When utilities get positive feedback from their customers it makes them look good in front of their shareholders and their regulators. It means that they have reduced call center volumes and can save on operating costs. And all of this is good for their business.
Conclusion
I’m thrilled that Opower’s Behavioral Load Shaping is firmly part of the company’s product line and doing well in the market. It’s such an interesting problem and there are many things that we did not do. For example, we never had to rely on social comparison or gamification tactics but they would be very interesting to test.
The product will have to continue to evolve as the grid evolves, especially with the rise of electric vehicles. By continuously interviewing customers, testing multiple behavioral strategies and keeping an elegant design, the product can continue delivering for homeowners, utilities and the climate.