At a recent Utility Week webinar designed to shed light on the realities of data-driven customer engagement in the energy retail sector, Rahul Arora, vice president and head of utilities at global technology and professional services company EXL, explained that efforts to create a more customer-centric approach to data were playing out against a backdrop of “intense disruptive pressure”. This spans customers becoming increasingly connected and empowered, new technology and disruptors, and fluctuating market conditions.
“Customers are demanding a lot of newer services which include connected homes and EVs,” he explained. “The demand is really going up from a customer expectation standpoint. They are motivated by price, there are no two ways about it, but at the same time the customers are conscious of the environment.”
This consciousnesses however comes with widely varying abilities among customers to embrace new technologies or ways of managing energy consumption. So knowing what to offer to which customers, and when is essential and will rely on confident use of data about a growing array of unique customer preferences and circumstances.
One of the things we’ve been doing is deciding how much data is enough data to start, test, run and refine.
The event – hosted in association with EXL and available to listen back to on demand here – centred on case studies from British Gas, presented by Jill Dougan, director for customer engagement, and US energy giant NRG, presented by Dak Liyanearchchi, head of enterprise data and analytics.
Following these case study insight, EXL’s Arora joined Dougan and Liyanearchi for a peer to peer discussion of the key pillars which support successful execution of customer-centric data strategy. Here are a few of the standout points they highlighted:
The need for agility and ‘speed to market’
According to Arora, “speed to market” and company-wide agility are key to executing a successful data strategy – a stance shared by British Gas’ Dougan.
“I think in the past we’ve been guilty of not doing anything until we’ve done a year’s worth of analysis, collecting every single source of data known to man and then we might actually make a decision,” she told Utility Week. “One of the things we’ve been doing is deciding how much data is enough data to start, test, run and refine.”
She explained that a laser-like focus on the needs of the customer, an in-depth understanding of KPIs before using data to “populate that picture” and setting a clear vision are key to quick and effective decision making.
“What is important is to lay data at the core and make data accessible and available to the entire enterprise – which makes it easier for businesses to make intelligent decisions,” Arora added.
Getting data to enable empathy – the key to long term customer value
NRG’s Liyanearchchi explained that in getting closer to its customers against the backdrop of a global pandemic, recent price increases and severe weather events, the firm found that trust and empathy were key to driving long term customer value and understanding consumer priorities.
Ultimately, painting a picture of needs and wants through data gathering was key, and provided the platform for NRG to refocus its efforts on a number of tailored solutions.
As an example, Liyanearchchi explained that, as the pandemic progressed, data showed that the interactions on its website shifted from being cost related to focusing on reliability. As such, the firm changed its online messaging accordingly to remain “relevant and empathetic to our customers”.
Data governance – same view, same terminology
Liyanearchchi also emphasised the importance of establishing a common, company-wide “vernacular” when discussing data in order to establish a focused philosophy and standardised data approach.
He explained that given the number of recent acquisitions and newly integrated teams at NRG, he’d found there were several different ways of talking about the same issues when it came to company data.
“We have so much data and we have so many different teams looking at that data. But how do we get a common view of the customer and work in the same way?” he said.
Identify the right KPIs
Another aspect of successful data strategy revolves around selecting the most relevant metrics and performance indicators for each business’ objectives.
“It’s not just about self-service and analytics, there’s also a lot of work around what are we trying to measure and what are we trying to improve as we become more and more consumer centric,” Liyanearchchi said.
Dougan seconded this point, saying British Gas was on a journey to define new KPIs to for successful data leverage which varies from a historic approach which was much more “performance focussed” in its thinking about how to use data.
Harnessing external data
Incorporating third party data into an overarching strategy is an increasingly important pillar according Dougan, who cited British Gas’ creation of ‘The Grid’ – a new tool, supplemented by third party data, to profile and understand audience members.
She explained that having the means of utilising third party data is particularly important in the current climate with suppliers going under and new customers being taken on from failed firms, for example.
“Data is ever more important as we bring customers on board into our energy companies, but also in terms of retention,” she said. “Most of those customers have not come to us through choice, they’ve come to us because their supplier has collapsed.”
Identify and act on customer trends
According to all three contributors, using data to identify trends within a customer base provides a solid platform for a proactive approach to their wants and needs – and therefore creates a far more engaged life-cycle.
As an example, Dougan explained that British Gas used ‘The Grid’ to conduct social listening exercises and to understand the most important priorities for customers around homes – “what broke down, what were the things they really needed help to fix,” she said.
Having identified the most common household problems, British Gas recruited engineers to help solve them, organised live events on YouTube, held Q&A sessions with customers. This approach delivered more than 4m video completions and reached 4.3m unique users.
Be transparent about data use
All three panelists stressed the importance of transparency in relation to the intended use of different types of customer data in order to promote customer buy in to data sharing and build trust. Dougan stated that a successful data strategy must “absolutely” include an explanation of how data is protected, why it’s being collected and what ultimately the customer benefit is.
Arora concurred: “Customers are anxious about sharing their data, but as long as we are transparent about the information we are gathering, give them control over their personal data, and offer fair value in return – I think they are ok with it.”
Back up your business case for investment in data intelligence tools
Arora was also keen to stress, that transitioning to a customer-centric data strategy and becoming more proactive in the way customer data is sued, doesn’t have to be a cost-intensive exercise in the round.
While investment in new tools such as AI and machine learning are necessary to unify and extract intelligence from data, the benefits this will bring in reduced “failure demand” – demand fuelled by customers not receiving service or communication that they were supposed to – can be substantial, more than paying for the technology outlay.
To listen back to the event in full, click here.
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