Across the energy sector, a new kind of race is underway: Organizations are rushing to prove that they are the most customer friendly of all. Shell CEO Wael Sawan, for example, says the company wants to become “the most customer-focused energy marketer and trader.” And bp Senior Vice President Greg Franks has been talking up his company’s “customer-obsessed mission.”
It’s a seismic shift for one of the biggest industries on Earth. As people become savvier about how energy supplies work and more aware of energy brands, providers find themselves having to appeal directly to consumers in new ways.
The same goes for another key constituency that energy companies have long focused on: policymakers. Many of today’s political figures at the federal and state levels have different values and a different focus from previous generations. And, the most powerful, senior decision makers can be especially difficult and expensive to reach. Energy companies need insights into where they stand.
The industry needs to know the concerns, hopes, demands, and needs of these and other stakeholders. That requires deep, trustworthy data. On this, there’s good news and bad news.
The bad news is that, for far too long, many businesses have failed to get a rich, detailed picture. The good news is that new technologies offer a revolutionary way to fix this problem.
Shining Light on “Dark Data”
Over the years, many companies have spent huge sums of money on surveys aimed at getting to know their stakeholders. But the lessons they drew from these surveys have focused largely on quantitative findings, such as numbers and yes or no questions. That’s how they came up with results like, “80% of customers believe” or “30% of policymakers want” something. These findings are helpful in general terms, but provide little in the way of understanding the person’s psyche – and even less at providing true decision making direction.
Many of those same surveys did include open-ended questions for people to answer. But businesses haven’t known what to do with all that text. Across a range of industries, organizations have largely ignored all that qualitative research. These companies also have heaps of information that’s too often underutilized, including energy consumption figures; heating types; whether someone is enrolled in a green energy program; and more.
It’s among what’s called “dark” or “grey” data. As a study of multiple industries, including energy, explains, “Dark data refers to information that is collected but remains unused for decision-making or analysis. This data is often stored in archives or databases without being actively analyzed, which means its potential value is untapped… Grey data, on the other hand, represents information that is incomplete, imprecise or uncertain, necessitating further investigation or refinement. This type of data is partially analyzed but lacks the clarity or accuracy needed to be fully reliable on its own.”
Now, thanks to AI, new tools can mine all that information. Using large language models (LLMs), these technologies can piece through huge sums of unstructured data. These tools can discover common themes, insights, and perspectives. The same new technologies can also suggest new markets to survey, what to ask, and much more.
The New Digital Twins
With that kind of technology in place, businesses can achieve a panoramic view of stakeholders, which automatically updates in real time as new information comes in. And, to make all that information accessible, these platforms can create a new version of something the industry is already familiar with: digital twins.
Currently, the energy sector uses digital twins that are simulated copies of physical equipment. Now, they can also use digital twins of customers, policymakers, energy influencers, and others. Executives can interact with these personas and ask them about all kinds of things, such as energy needs, customer experiences, marketing, proposed new legislation, or just about anything else.
These digital twins respond in realistic language, synthesizing the kinds of responses real people give. Crucially, these twins are not simply making responses up. Their responses are based on an extensive foundation of real world data, finally combining qualitative and quantitative information to offer a multidimensional, nuanced view.
We’ve found that digital twins are useful for crisis communications as well. During a natural disaster or blackout, some big energy companies like utilities have trouble reaching customers. Virtual panels of digital twins can stand at the ready, providing truly representative real-time feedback to steer crisis communications.
A deep data set will be extensive both “horizontally” and “vertically,” offering more types of personas from different demographics and more information within each persona, providing a more well-rounded, three-dimensional understanding.
For all this to work, the programming driving it must be well designed. It must vet, curate, and contextualize data. It must include not only millions of data points, but also the language, expressions of emotion and sentiment, and other information people provide in qualitative research.
These kinds of technologies are available not only to corporate giants, but also to small businesses. And they work for all forms of energy. A solar energy startup needs to recognize who its most likely buyers are. A midsize hydropower company needs to determine its growth targets. An incumbent power utility needs to diagnose where its churn is worst, and what to do about it.
All of this is possible when companies open up their treasure troves of black and grey data to AI. Those findings will fuel the efforts to appeal to customers like never before – and the organizations that do this the best will speed ahead to the front of the pack.
