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EDF is the United Kingdom’s largest producer of low-carbon electricity, with a vision of “Helping Britain achieve net zero" carbon emissions. It was recently rated number one for customer service out of 40 energy suppliers in the country. That’s no small feat when you have 3 million customers and your market’s awash in energy startups intent on disrupting the industry.

In a landscape so competitive, putting customers first is key. So, when EDF faced challenges recognising return customers to thank them for their loyalty, it embarked on a quest to better identify them when they came back.

EDF creates new accounts whenever customers sign up, which makes identifying return customers a challenge. It was more than a missed opportunity for EDF to welcome customers back. It was also a lost opportunity to assign any energy bills that either were left behind or arrived after customers moved – bills that would go into debt collection if left unpaid. With a second chance to settle their debts, customers could avoid the stress of the collections process and protect their credit scores from damage due to late or missing payments.

The dual prospect of optimising customer experience and company revenue made customer account matching part of a larger data initiative across EDF. “We were looking for all of the good stuff that enterprises do with their data programs,” says John Hutchins, Head of Smarter Living at EDF’s Blue Lab, an innovation hub working to accelerate the transition to a sustainable, low-carbon society. “ Master data management. Better quality. Access. Standardisation.” The data scientists at Blue Lab are just one team of many that will be able to leverage data across the company using a customer analytics platform that EDF and Slalom created together.

For years, EDF had been working to build a robust data strategy to improve internal reporting and get more value from its data. It had moved much of that data to the cloud but wanted to do more with it: prepare it, engineer it, and open it up for different teams to use.

At a meeting with Slalom and leaders from EDF, a truth surfaced. In the effort to unite data across multiple platforms and vendors, the technology had stolen the spotlight from the business need—the “what” had eclipsed the “why.” This discovery sparked a shift. “We knew where we wanted to get to,” says Slalom’s UK Energy Lead, Davi Quintiere. “The key question was how to take those first steps up the ladder in the time we had. So rather than, ‘Let’s fix all data problems,’ we asked, ‘What are the use cases we should tackle first that would most benefit EDF and its customers?’”

The first true use case for the platform became debt recovery, specifically by matching lapsed customer accounts to active ones for the same customers. This called for data processing on a massive scale—especially if EDF ever wanted to integrate other use cases in the future—so Slalom helped EDF move from data warehouses to a cloud-based data lake architecture on AWS with three analytics zones: a data landing zone, a technical zone, and a democratisation zone. The result is a secure, scalable analytics platform that separates storage from compute and is designed to be used by data engineers, data scientists, and product teams alike.

To build the platform, Slalom leveraged Amazon EMR, the big data processing service of AWS. In the technical zone, EMR helps ingest raw data from the fully protected data landing zone, process it, and then release it in structured format. Data can then be prepared for different use cases in the democratisation zone, which displays only what users need to see based on their credentials. To accommodate EDF’s security requirements, Slalom also collaborated with Amazon to enhance EMR’s encryption key provisioning.

In its first quarter of use, the platform helped EDF match hundreds of thousands of pounds in outstanding balances to the right customers. On an annual basis, it’s projected to match multiple millions of pounds. But the financial benefits of the platform go beyond debt recovery. Hutchins considers the decoupling of storage and compute to be an essential feature of the platform for its ability to optimise costs. The platform’s modular architecture and EMR enable EDF to quickly isolate what it needs from billions of rows of data. EDF only pays for what it uses, and the number of compute instances can be increased or decreased automatically. “We minimise the amount of compute that is running all the time,” says Hutchins. “There are companies that don’t separate [storage and compute] with services like EMR and if we were one of them, we would be paying a lot of money to host those tables.”

EDF is already thinking of new uses for the platform. One involves machine learning to help service agents proactively offer payment plans or bundled tariffs to customers who might want them. Agents could make offers in near-real time as customers’ usage and payment history ran through the model. Another involves data mapping and visualisation to illustrate to customers how their energy usage compares to that of their neighbors. If usage is comparatively high, EDF could then provide recommendations for reducing it, such as adding insulation – all in service of EDF’s vision to help Britain achieve net-zero emissions.

It’s no coincidence that both use cases play a role in improving customer experience. They put more power in the hands of customers to manage their accounts – just like the original use case did. It marks the beginning of what’s possible when you unite customer data and unlock its potential. Hutchins hopes that the platform will lead to “a better, more seamless customer experience and relationship.”

As he says, “It’s already helped us improve our offboarding and onboarding process so that it has some great impact. Now we can say, ‘Hey, Mrs. Smith. Thanks for coming back to EDF!’”