Big data and analytics are becoming mainstream and vital to business operations and customer relations. Here’s a look at how modern tech fits into the customer service puzzle.
Last year, Scott Feldman, a health-company financial director, received an electric bill for $2,253.73 for his Seattle area home. The home didn’t use electric heat or air conditioning, and Feldman’s past usage pattern resulted in $50 bills every two months, so something was wrong. Feldman called the utility’s customer service line and was informed at one point that it would take nine to 10 months to get a refund. In the course of resolving the billing discrepancy, Feldman said he experienced telephone hold times of as long as one hour, five minutes, and 53 seconds.
Feldman’s experience is all too common, in part because public utilities have functioned as near monopolies that historically haven’t felt a strong need to compete. For many, customer service has not been a priority, and agents can help customers only by using outdated customer information systems (CISes) and order systems.
Unfortunately, these transactional systems can’t tell the full story of a customer, their preferences, or their pain points. However, by combining the information from transactional systems with information from analytics, big data, and IoT, service agents—and everyone who is customer-facing in a utility—can get a more complete picture of their customers.
“Utilities understand this, but for many, using big data and analytics is a major challenge,” said Michael Rigney, SVP of sales, marketing, and regulatory affairs for EnergySavvy, which specializes in customer engagement services for utilities. “This is because many utilities run legacy systems that don’t readily integrate with newer analytics or big data technologies. Meanwhile, customers are getting tired of receiving offers and communications from utilities that don’t address their needs. The goal for public utilities now is to be able to better understand their customers with the help of analytics and big data. For this to occur, a utility must understand each customer’s energy usage patterns, demographics, whether they use solar energy, and their ability to pay.”
I witnessed this firsthand when I was consulting to a large west coast utility company. The utility was starting a smart meter pilot project it wanted to promote and expand. It began by enrolling interested customers in a smart meter trial. In the pilot, the utility monitored smart meters in real time, with IoT-generated big data from the meters being collected and then aggregated with standard customer data derived from customer information systems (CISes) and order systems. The end products were an analytics report for the end customer that showed their household energy consumption and made recommendations for energy use improvements, and input into the company’s internal operations systems.
“Better energy management resonated with our customers,” the utility’s operations manager said. “It enabled us to rapidly expand our IoT-based smart meter program and our analytics, and it also contributed in a positive way to our community reputation.”
What did it take to get there?
1: Customer awareness
The company spent two years talking with customers, giving out surveys, and running analytics from its marketing system to determine which customers were most likely to embrace smart meters. The analytics delivered value because the technology could aggregate many types of dissimilar information about each customer and then assemble and evaluate this aggregated information in ways that delivered customer insights not readily discoverable with other methods.
2. System integration
A faction in the company wanted to do away with legacy systems altogether, but over time, the project team realized the importance of the information, the business processes, and the user familiarity with these systems. The choice then became one of integrating the legacy system base with newer IoT, big data, and analytics systems. The result was a blended data repository derived from both legacy and big data that could be queried with analytics.
3. Channel optimization
The company had never spent much time looking at how it communicated with its customers. This communication could be by phone, through email or regular mail, or through social media. The analytics it developed in marketing had combined data from both structured and unstructured data sources. The company was also able to see the communication channel preferences by customer.
4. Cultural change
The IT department began to coalesce around the chief data architect (now known as a CDO, or chief data officer). There was a realization that data was morphing into new, unstructured forms like social media and IoT that somehow had to be blended successfully with standard, transactional data—and that a data architect was needed to do this. On the customer-facing side of the equation, the organization began to seek new talent with skill and commitment to customer relations.
The role of big data and analytics
The bottom line for utilities is that big data and analytics are becoming as mainstream and vital to business operations and customer relations success as the standard systems that have always supported these functions.
Two years, ago, Maureen Quaid of the Public Service Company of New Mexico summarized both the change and the challenge:
“Our current knowledge about customers is sufficient to create program processes, establish incentive levels, develop marketing campaigns, and report on achievements,” she said. “What we cannot yet do is 1) predict which customers will be most interested in participating in the future, 2) market directly to customers on the basis of our understanding of their needs and preferences, 3) track the success of direct marketing campaigns in encouraging customer participation, or 4) measure changes in customer satisfaction as a result of our targeted communications and their subsequent program participation. The customer analytics initiative will increase our ability to know our customers, communicate effectively with them, and measure the outcomes from these changes in approach.”