Once upon a time, and honestly for far too many years after that, energy retail was built around a useful little piece of fiction: the average customer.
Most households consumed electricity in fairly predictable ways. None of us are that special, surely? The relationship with the energy supplier was purely transactional. The bill arrived, payment was made, and unless something went very wrong, that’s all she wrote.
The fairytale has ended.
Energy retailers today are sitting on more data than ever before. Smart meters, digital channels, tariff data, weather data, market prices and customer interactions all create a much richer picture of energy behaviour than what we’ve seen before. In many European markets, the industry is also moving towards much finer time granularity, with 15-minute and 30-minute data increasingly becoming part of how energy is traded, settled and understood.
So much data. So many dashboards. So many strategy decks about “unlocking customer value”. And yet many retailers still operate like it’s 2009 with slightly better graphs.
Many retail processes still operate as if customers can be managed through averages. Tariff design, forecasting, marketing, flexibility programmes and customer support are often still built around broad profiles or simple archetypes: the high user, the low user, the family home, the apartment, the electric heating customer. Average Joe and his little gang of statistically convenient friends living in roughly the same house with roughly the same heating patterns.
But a customer with an electric vehicle, solar panels, a heat pump and a dynamic tariff is not the same as their neighbour who cycles to work, doesn’t believe in PV and has an old classic tariff. Sure, they might consume roughly the same number of kilowatt-hours over a year. Apart from that, they have about as much in common as a Tesla owner and a man still proudly driving a diesel Passat from 2004. Treating those customers as the same is just a bad decision.
I see this in my own home. I have a dynamic electricity tariff. My heat pump is connected to price signals. I have solar PV, an EV and a wallbox. None of these things were offered to me or installed by my utility. They came from different providers, different apps and different decisions made at different points in time. And I’m not even an extreme case, I’m just your average electrified home.
For an energy retailer, this creates both a challenge and an opportunity. The challenge is that the customer is becoming harder to understand. The opportunity is that the retailer is still one of the few players with a continuous view of actual energy behaviour. But having data is not the same thing as understanding customers. Right now, a lot of the industry is basically sitting on a goldmine using it as a paperweight.
So what data to start with then? Energy data on its own is not very helpful. A meter reading every 15 or 30 minutes may be technically rich, but it does not automatically tell you what is happening behind the meter. It does not explain that an EV has appeared in the household, that a heat pump is driving winter peaks, that a customer would be materially better off on another tariff, or that a sudden consumption change is likely to trigger a support call next month.
The business model of energy retail needs to evolve. Yesterday.
The future retailer will not only bill customers for energy. It will need to understand each customer well enough to serve them at the right moment, with the right proposition, in the right channel. That could mean recommending a dynamic tariff to a household that has enough flexibility to benefit from it. It could mean identifying a customer who has started charging an EV at home and offering a better charging proposition within days, not months. It could mean spotting a solar household with low self-consumption and making a battery recommendation that is actually relevant. It could mean helping the support team explain a bill before the customer loses trust.
Honestly? Give up on traditional campaign segmentation already.
Historically, a utility might design a product and then find a group of customers who roughly fit. In the next model, the retailer starts with actual behaviour and builds from there. Which customers have flexible load? Which customers are exposed to peak prices? Which customers are likely to benefit from automation? Which homes appear to have assets the retailer does not yet know about? Which tariff creates value for both the customer and the supplier?
They require a more individual understanding of energy behaviour, and that understanding needs to become operational. It cannot sit in a data science team as yet another fascinating dashboard that nobody outside the analytics department ever logs into voluntarily.It needs to be available to product teams, marketing teams, support agents, forecasting teams and flexibility teams. The whole organisation needs to work from a better view of the customer.
Customer experience and operational performance have spent years sitting on opposite sides of the office pretending not to know each other.
When a retailer understands what is happening in the home, customer support becomes more proactive. Campaigns become more relevant. Forecasting becomes more accurate. Tariff recommendations become more trusted. Flexibility programmes become easier to target. The customer feels less like they are being sold to and more like they are being helped.
People do not want more energy complexity.
Nobody wakes up at 06:30 thinking “today I’d really love to optimise my household load curve”. They want comfort, control and confidence that they are not wasting money. If a retailer can use data to simplify that experience, it earns a different role in the customer’s life.
At Eliq, our work has always started from a simple idea: energy data needs to make sense to people. For years, that has meant helping customers understand what is happening inside their property, across electricity and gas consumption, bills, forecasts and personal insights. The next step is just as important: helping utilities operationalise that same understanding so they can serve customers better at scale.
This needs to be done carefully. Customer intelligence should not mean intrusive profiling or complicated black-box decisions. It should mean using data transparently and responsibly to create better outcomes. A better tariff recommendation. A clearer bill explanation. A more relevant flexibility invitation. A timely warning before a bill shock. A service interaction where the agent can see the context and actually help.
The energy transition will not be delivered by infrastructure alone. It will also depend on whether millions of customers can be guided through more complex choices without feeling overwhelmed.
That is why the “average customer” is quietly becoming one of the most dangerous ideas in energy retail. Because averages hide the exact differences that now matter commercially.
The winners in energy retail will not simply be the companies with the most data. Almost everyone will have more data. The winners will be the companies that can interpret it, act on it and turn it into better customer relationships.
Energy retail has outgrown the average customer. The next business model will be built around understanding the real one.
