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Meet the participants

In this section, we present an overview of the people who participated in the study. We aimed to select participants with a range of levels of engagement in Power Payback, from those who opted in and responded to many events, to others who chose not to participate at all. The final sample included more highly-engaged participants, despite substantial efforts to recruit a more even spread. We should perhaps not be surprised by this outcome, and it may be informative in itself of a level of commitment to the service.

To learn more about the participants, we first asked introductory questions about their household setup, what their typical days and weeks looked like, their use of their smart meter and any low-carbon technologies (like solar power, heat pumps, and electric vehicles) if they had them.

Turning to flexibility, we were interested in their experiences of participation in Power Payback:

  • What factors affected their decision to (a) opt in to the service, (b) opt in to events, and (c) participate in events? How did this change over time?
  • What factors affected their perceived ability to benefit?
  • What is their overall attitude towards the offering – what did they like and dislike?
  • What features of future offerings would make it more likely that they would participate, respond, and have the best experience?
  • How do experiences/perceptions differ with household characteristics?

We then asked about their perceptions of their potential ability to participate in, and benefit from, other flexibility offerings – what might affect this, and how would they evaluate possible trade-offs between financial savings and service quality? What is their attitude towards, and awareness of, the need for flexibility in general – and who might and might not benefit?

We also conducted an analysis of MSE forum discussions about the DFS to provide additional insight on these questions. Full details of the method, including details of recruitment, interview conduct and analysis, and forum analysis are available later in this report.

Table 1 provides an initial overview of the participants, all of whom were resident in England and Wales.

The customers we spoke to generally held positive perceptions of Utilita, and most had explicitly chosen to switch to them. There was also generally high satisfaction with their smart prepayment meters, especially compared to previous non-smart versions. Perceived benefits included the ability to top up easily via the app, schedule regular top ups, and monitor electricity and credit use.

Turning to headline participation in Power Payback, across the group, savings per event ranged from £0.19 to £0.78, while total rewards ranged from £0.47 to £8.59. Maximum participation was 11 events, with a mean opt-in rate of 84%, although some people only came onto the service later, meaning that while they responded to all calls, they participated in fewer events.

Those are the overall numbers. Click the dropdowns to see short descriptions of a few of our participants and how Power Payback worked for them.

Jane (pseudonym 5) is a 45-year-old resident of a wheelchair-adapted bungalow where she has lived for six years. She lives alone with her two cats and frequently attends hospital appointments due to health issues. Her daily routine is quite irregular, with most of her activities, such as laundry and TV watching, happening at night. Her keenness to take opportunities to save money, along with a commitment to environmental sustainability, drives her participation in Power Payback, and her flexible routine makes it quite easy to respond. However, her low electricity use means her savings are generally small.

Chris (pseudonym 6) is a 35-year-old father of four, including three children with special needs. He lives in a housing association property and manages a busy household with high energy consumption due to the needs of his children and various smart devices. Chris is tech-savvy, environmentally friendly and tries to save money, but sometimes has to deal with high and inflexible demand for energy at peak hours due to the children's schedules.

Lisa (pseudonym 8) is a single mother in her early thirties living with four children in a semi-detached house, and did not opt to participate in Power Payback. Her weekdays are packed with school runs and managing the children’s needs when they get home. She didn’t have the chance to think about joining Power Payback due to her busy lifestyle having to take care of the children.

Christine (pseudonym 9), a single woman in her fifties, who lives in a house bought with family inheritance. She has been in her home for 13 years and manages her household independently despite health issues that prevent her from working. Christine spends most of her time at home, using electricity primarily for her television, lights and kitchen appliances. She has a stair lift and an outdoor platform lift, both essential for her mobility. Engaged with Power Payback, Christine takes pride in doing her bit for the planet by reducing energy consumption.

The following section provides a synthesis of experiences of Power Payback across the participants.

Authors

Michael Fell

Michael Fell

Michael Fell

Senior Researcher, sustainable future mission

Mike is a senior researcher in the sustainable future mission at Nesta, on secondment from his role as a senior research fellow at University College London (UCL).

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Yini Zheng

Yini Zheng

Yini Zheng

Designer, Design & Technology

Yini is a designer for the Design & Technology practice and will be working on various projects combining behavioural insights with her service design expertise.

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Andy Regan

Andy Regan

Andy Regan

Senior Mission Manager, sustainable future mission

Andy works within the Nesta Cymru team as mission manager for a sustainable future.

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Max Woollard

Max Woollard

Max Woollard

Analyst, sustainable future mission

Max joins Nesta as an analyst in the sustainable future mission.

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Sofia Pinto

Sofia Pinto

Sofia Pinto

Data Scientist, Data Analytics Practice

Sofia is a data scientist working in the Data Analytics practice.

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