Lucy Yu, CEO, Centre for Net Zero Retail excellence, Comment, Octopus Energy, Climate Change, Energy Efficiency, Internet of Things, Energy, Customers

Centre for Net Zero CEO Lucy Yu discusses what lessons utility firms can heed from consumer-facing sectors such as finance, transport, retail, and innovations such as Spotify Wrapped, in creating a richer energy data landscape.

One of the most striking things about joining the energy industry as a relative outsider is the comparatively low use of data and digital technologies to drive innovation, transform business models, and make life easier, cheaper and more convenient for customers.

While there are a handful of trailblazers bucking the trend, compared to other industries such as finance, transport, retail and food, energy is perhaps a decade or more behind.

In other areas of our lives we’ve become used to new products and services enabled by internet technologies, and in many cases we’ve transformed our own habits – managing our pensions automatically; using fleets of shared vehicles instead of owning our own; and assembling meals from some of the world’s top restaurants in the comfort of our own kitchens.

It would be foolhardy, however, to think that energy won’t only catch up but perhaps even surpass some of these first mover industries.

Efforts to collect and open up more data to support the transition to green energy are growing at pace and the list of potential customers and use cases for that data grows by the day, as more entrepreneurs and governments focus their attention on the climate crisis and ways to address it head on.

Here are just a few ways that using data could help to advance a faster, fairer and more affordable energy transition.

Finding interesting ways to engage customers

How much energy did you use this month? Which days and times was your use highest? How did that compare to last month, and to pre-pandemic times? How much did that all cost you, and what were the greenhouse gas emissions created from its generation? Many people are taking a keener interest in their energy use as wholesale prices reach unprecedented levels.

And there’s even some evidence that some customers will engage with ‘gamification’ if it could save them money or give them a personal challenge to meet – Octopus Energy’s own ‘Winter Workout’ challenged customers to take small actions designed to cut emissions in return for a share of a large cash incentive. Thousands of customers took part, with many sharing their progress with friends and family.

Showing households data about their energy use and habits isn’t rocket science, but could have some useful outcomes, helping households to understand the general trends in their use and what that means in terms of cost and carbon.

Here, the industry could learn from some of the most engaging examples from elsewhere.

Driven by data and highly personalised, Spotify Wrapped is a popular year-in-review recap of a customer’s listening habits, with some additional games and quizzes built in – listeners can compare their music tastes with friends, and share their ‘Wrapped Cards’ through social media channels.

Google Maps also offers a similar ‘timeline’ summary, sending those who opt in a monthly or yearly summary of the places they’ve been and the distances travelled on different modes of transport – and a recent update shows users how much of their travel was made using low emission modes.

Identifying and supporting vulnerable customers

With richer data and analysis techniques come stronger possibilities to identify the most vulnerable customers and to provide help and support at the earliest opportunity.

Spotting deviations from historical patterns of behaviour could be a strong indicator of a customer whose circumstances have changed, perhaps for the worse.

For instance, customers on pre-pay meters topping up less frequently or with lower amounts has been used by some utilities to spot those who might need extra help. As adoption of smart meters grows, more granular behavioural data becomes available, for a broader set of customers.

Of course, using data in this way presents risks as well as opportunities. Near to real time data about how much energy is being used and when could also indicate when a property is occupied or empty, presenting potential security concerns.

While existing regulations may provide some protection against misuse, energy is likely to be the latest in a long line of industries where debates about ethics have become louder and more frequent.

Understanding people’s lifestyles and promoting greener lifestyle change

It goes without saying that data can tell us a lot about people’s lifestyles – how they live now, and what changes might be on the horizon.

If I buy cat food and nappies at Tesco, you may infer that I own a cat and have a baby. But some slightly less obvious examples exist too.

Public transport authorities that operate account-based smart ticketing can predict with good accuracy which travellers have children – those whose regular journeys change or disappear during school holidays – and some credit card companies even claim they can spot the first signs that a relationship is in trouble.

Understanding people’s lifestyles better means we can understand when key decision points are likely to arise. For example, we can assume that most people applying to the DVLA to upgrade their provisional driving licence for a full one have recently passed their driving test, making them more likely to consider buying a car than at other times in their life. Sending those people information about local car and bike sharing schemes could help them understand the alternative choices available to them.

Similarly, could we use energy related data to pinpoint major changes in people’s lifestyles, or other opportunities to promote greener lifestyle change?

This has been a whistlestop tour of just a few of the myriad innovations that a richer energy data landscape could offer. And it’s the reason we have a lighthouse data project of our own at Centre for Net Zero. Our Project Faraday aims to develop an API to generate electricity load profiles, based on input parameters such as building type, low carbon technology ownership, and weather and tariff information.

While there are obvious use cases for a tool such as this (for example, to help distribution network operators forecast future load on the network), experience tells me that it’s the use cases that haven’t even been thought of yet that will have some of the greatest impact on customer experience, household bills, and carbon reduction – and that’s why the energy industry is such an exciting place to be right now.

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