The three steps of a successful smart transition

Smart technologies are revolutionizing utilities' customer relationships, bringing vital lessons to other industries for the age of the internet of things.

woman using smart thermostat

“Ask yourself this,” says Richard Postance of EY’s Power & Utilities business. “Even if you had the best and most innovative products and services on the market, would your customers buy them from you?”

His answer is simple: “Without a trusted relationship, they won’t.”

To gain a trusted relationship with your customers, first you need to understand what they want. “Raising your expectations to the level of your customers’ and putting in place service excellence offers a more solid return on investment than anything that will come from attempted cross-selling to dissatisfied customers,” Postance says.


The smart way to build customer trust

This is where smart technology, a subset of the internet of things, can come in: using the power of automated data analytics to deliver improved, more personalized customer service, and to secure more detailed understanding of your customers’ habits and needs than ever before.

Smart technology is being positioned by some as a golden opportunity to enter the digital age and cross-sell other products and services. But the most significant opportunity, says Postance, is to reduce your cost of operations by having better-quality data on what you already do. It is only by being brilliant at your core operations that you will win genuine customer loyalty.

The rise of smart metering – as well as devices such as smart thermostats, which can allow users to control the temperature of their homes and offices remotely – has put the utilities sector at the forefront of the move to smart.


What can other industries learn from utilities’ early experiences with smart devices?

As utilities have begun to transition to smart technologies, Postance has identified three vital key steps to take, which businesses in other industries should also bear in mind as they transition to a more interactive, data-driven, customer-focused model.


1) Shut down old accounts cleanly

“Many of these accounts have multiple data issues,” warns Postance. “It could be the meter reading has always been a little bit out of sync, or bad debt is associated with the account. It may not be clear who the bill payer is. The point is, if you don’t close down that account cleanly, you will incur both significant costs later on and leak customer satisfaction.”


2) Start afresh with good data

“This is an opportunity to get the data right and start the relationship on the right foot,” says Postance. “Seize that opportunity and avoid eroding the value of your smart investment by carrying over bad data.”


3) Keep it clean

“Data cleansing is often one of the first things to go when utilities are trying to cut costs,” Postance says. “It doesn’t have a direct line of sight, and the benefits can seem theoretical. But, in fact, clean data is essential for good customer processes because data pollution is insidious. All you need is one mistake, and it taints trust in the rest of the data.”

Digital thermostat

The cost of poor data

The smallest data error can have significant implications, not just for your direct customer relationship, but also for your longer-term product development, by feeding you with false information, leading you to make the wrong choices.

This is where smart technologies can drive true efficiencies. In the utilities sector, it is not just about cost savings from avoiding the need for physical meter readings, it is about using smart technologies to increase bill accuracy and improve communication with customers. Furthermore, these technologies can better understand how customers are using energy to develop more efficient and effective services.

Ultimately, Postance says, “Investing in getting the data right reduces operational costs and improves customer satisfaction.” This is not just true for the utilities sector – it is a truth that lies at the heart of success in this new data-driven age.


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