The Solution

We designed a series of emails—called the Rate Coach—to augment the effect of the utility company’s peak pricing program by sending personalized insights and analysis to people’s homes. The rate coach emails:

• Educate people about their rate plan details

• Motivate them to shift energy use

• Compare personal data to hourly prices

• Track people’s progress over time

• Savings motivation. People were willing to shift their usage if they could save $5-10 a month

• Habit-forming. “Lifestyle” changes are too extreme—bite-sized actions will more likely to lead to new habits

• Challenges. Renters, medical equipment users, and larger families with kids will find it harder to change

"If I switched I'd want it to be effortless. I won’t change my lifestyle.” - Mary

Design iterations

Using our research insights and customer journey mapping, we sketched and developed wireframe flows.

We considered both repeating and rotating information. Based on engineering feasibility, we balanced what could be launched now versus what should end up on the backlog as our teams build out new algorithms.

Usability testing

We started with a primary graph that maps your electricity use against the hourly prices for electricity to show how much more you pay during peak hours.

We loved how our line graph packed in so much information. But, when we tested it with users, we learned this graph was too complicated for the average residential customer. The double axis was confusing, and we needed to further highlight the most expensive hours. After testing we iterated on the design, separating the rate plan info from the usage info, and switching to a bar graph with only one axis.

Diary study

With our new graph and additional insights from testing, we designed a series of 4 emails. We ran a diary study to replicate the experience of receiving these emails for a month. The same 11 participants were shown each week’s progressive email, first seeing an email inbox and subject line. We learned:

• The new pricing timeline was clearly understood and participants retained the info through the study

• The bar graph focused people’s thinking about the activities they did during each hour of the day

• Behavior changed. Participants reported running their dishwasher or doing laundry outside of peak hours.

“Each week I’ve been impressed, and think that each week builds on the last one. It feels more and more personalized and more valuable.” - Allison

Using Behavioral Science

The exploratory research, usability test, diary study, and additional quantitative testing informed each design decision, coupled with behavioral science principles as shown here in the first email in the series.

Intro area

1. Simplification. The intro area highlights the benefits to the customer and sets expectations for the frequency and content of communications.

2. Anchoring and social norms. People rely on comparisons to make decisions. Initial research showed that customers asked, “Would I save 5 cents or 20 dollars?” Providing a range of real savings helped frame the possibilities and context. This light framing of what “other people” have done emotionally triggers competitive instincts.

Data section

3. Learning. The details of the pricing plan must be clear and easy to understand. The dollar signs remind people of Yelp and give a quick view of the differences in time periods.

4. Personalization. People want to see their own data—it makes them care. The graph subhead suggests thinking about electricity as part of a personal routine. Color coding draws connection between the customer’s use and the details of their rate plan.

TIP AREA

5. Salience. Vividness and specificity can make a message more memorable. By including specific examples of which appliances to use during off-peak hours, the message becomes actionable.

6. Trust. Including appliances that don’t use a lot of energy builds trust that the power company is looking out for the customer.

Next Steps

The product launched with one of our most strategic clients in April 2019, reaching over 50k residents, with regulatory approval from the Maryland Public Utilities Commission. We’ll measure the success of the program using a randomized control trial and A/B testing to determine:

• Energy savings during peak hours

• Reduced call volume

• Customer satisfaction

This test will inform future iterations of the product. Additionally, as energy regulatory markets continue to change, the product can be adapted to meet the needs of other use cases to move toward a cleaner energy economy.