The Power of Customer Analytics to Transform the Utilities Industry
Disruptive technologies are transforming a wide range of industries and the utility industry is among them. Technological developments in solar and storage are seen to have the largest impact, but few realize that the changing preferences of the customer will also have a major impact on operations. Understanding those changes will be critical to the industry’s ability to adapt.
How will utilities companies understand which customers are likely to adopt solar, join micro grids, demand pay-as-you-go plans, or expect their utility to provide in-home energy management? Luckily, technology and analytics have become so advanced and affordable that gaining a full understanding of all customers’ interactions is no longer out of reach.
For a utility to better understand its customers and then predict customer behavior, it first needs to gain a 360-degree understanding of its customers. At most utilities, customer transactional data is isolated in multiple different systems. IVR data, is separated from web and mobile transactions and most utilities have little understanding of why their customers call into their call centers.
Using advanced data and analytics gives utilities insights into its customers, enabling them to improve customer service, better plan for future customer needs and streamline operations.
For a start, natural language processing and machine learning can help to analyze call center conversations and interpret notes and comments, while big data platforms have the ability to combine customer
transactions from multiple channels.
From there, advanced statistical models can generate insights into unique customer segments and their differentiated behaviors. Meanwhile, predictive analytics can help anticipate changing customer behavior. Statistical tests can then predict the success of utility initiatives in impacting customer behavior, such as the adoption of a new self-service functionality on the web or mobile app.
Recently a utility leveraging these kinds of customer analytics was able to identify customer transactions at new service set-up that could be combined and thus eliminate repeat calls and customer frustration. By better educating the customer during the first contact about available services, account options and assistance programs, customers were able to make better informed decisions and select the options that were best for their circumstances. The utility in return eliminated calls to the call center by implementing these improvements.
Meanwhile, customer analytics can have an immediate effect on customer satisfaction and help reduce call center volumes. But the benefits do not stop there. Utilities that have harnessed customer analytics have also been able to improve other processes, such as credit and collections.
Specifically, understanding individual customer segments and their payment behavior can help identify customers at risk for late or no payment, develop counter initiatives, and thus reduce the number of customers slipping into the dunning process. This in return can help reduce bad debt write-offs and better predict balances at risk once a program is put in place to assist customers with payment difficulties. Lessons learned from these programs’ successes can then be applied to the overall credit collection process. Customer analytics enables this process by quantifying results and predicting success.