The Utility Analytics Summit (UAS) has quickly grown in popularity among utilities and vendors, with the number of attendees almost doubling since its inception in 2012. Data analytics used to typically be the focus of just one track at a utilities conference, but interest in the subject has increased exponentially as utilities have rapidly uncovered patterns in their data that have enabled tremendous improvements in operational efficiency, customer service and decision making. The successes have spurred most utilities to catapult data analytics to the top of their priority lists.
Glen Mannering, a utility analytics expert at PA Consulting, agrees and added that “the key to this increased popularity and attendance is twofold—the benefits that utilities are now realizing from deploying use case capability within solid business driven and led analytics programs, as well as the increased maturity of the enabling analytics technology (including AI), where vendors have stood up and can now demonstrate deployed sophisticated use case algorithms and data.”
Yifan Lu, Business Intelligence Strategist at SMUD, shared her company’s Digital First Strategy, which uses Google Analytics and Tableau to uncover insights and create personalized customer websites.
“If you use Amazon, then based on your shopping history, you receive personalized recommendations,” Lu said. “We want to do the same for our customers—a personalized website can improve customer engagement.”
Mannering also noted, “While the Grid domain is still prominent in utility programs, the Customer domain is now emerging from pilot initiatives into enterprise wide programs. The programs highlighted at UAS are typical of this transformation and parallel customer driven programs at other utilities across the industry.”
Lu also discussed a recent success story of SMUD’s that offered customers a small rebate program (up to $1,000) to encourage them to replace their heat pumps. The company segmented its customer base and instead of targeting all 44,000 customers, it ended up reaching out to only about 9,000 customers. As a result, SMUD saved on costs by targeting a smaller group of customers and saw a 50% uptick in customer acceptance of the program.
Additionally, SMUD used machine learning to understand customer energy usage patterns along with other data to enable customer engagement. In the end, the company saw an improvement in operational efficiency, as SMUD spent less money and reached the right customers, as well as an enhancement of the customer experience to become a true energy advisor.
The company also used machine learning to find out customer energy usage patterns and match energy efficiency programs for low-income customers. SMUD was able to determine the reason for high usage and then identify the program that would help solve the problem. For example, if the problem was inefficient HVAC, replacing or repairing could be helpful as well as offering an HVAC rebate program. For poor insulation, weatherization programs were an option, or if the reason for high usage was simply out of habit, energy efficiency education was offered.
Kristy Lovett, Senior Manager Customer Satisfaction, Customer Experience at Ameren Missouri, talked about the benefits of using customer specific data to create personalized messaging and providing delivery through the customer’s preferred channel.
For example, when customers call the utility, most representatives handling the call will not have selling experience, but rather, experience with traditional services such as helping a customer with the move-in and move-out process. By leveraging customer data, a personalized script pops up for the representative when the customer calls in, containing specific information on the products that would be of interest.
Austin Kesar, a utility analytics expert at PA Consulting, said the two prominent areas representing a platform for utilities to explore analytic programs are Customer Engagement and Asset Management/Maintenance. In speaking with utility attendees, he said these two topics were referenced routinely as areas of interest and participants were eager to examine how to design strategies that effectively leveraged investments in analytic programs in this space, which could create tangible business value and improve key metric performance (KPIs).
Ioannis Katsanos, Lead Data Scientist, Customer Business Analytics at ComEd, discussed the company’s strategy of fully realizing the value associated with its BIDA (Business Intelligence and Data Analytics) journey and becoming a more mature analytics organization.
He went through the set of seven use cases that have been identified for the first wave of delivery 2017-2018: inbound call reduction, cross-channel effectiveness, advanced speech analytics, eBilling propensity models, augmented risk scoring, 360 degree view of the customer and gas conversion optimization.
Katsanos explained that the use cases identified are only the starting point of the program, with the expectation being that as solutions are developed, new questions and opportunities will arise.
“The ultimate goal is to help solve real business problems, empower the decision-making process, and gain a better understanding of the customer to make sure the work being done has a positive impact on their lives,” Katsanos said.
Mannering weighed in, “As Katsanos states ‘the ultimate goal is to help solve business problems’ and in all of the cases highlighted above, this is the key factor. Business led programs that use advanced analytic and data capability to solve real business problems and significantly improve customer engagement and business operations, are the ones that succeed and realize true benefits for both customers and utilities.”
Kesar concludes that ultimately, “Ankush Agarwal, Senior Manager, BIDA Grid Lead at Exelon Utilities, presents the most important considerations that utilities should evaluate prior to investing in an analytics program, which is how to ‘justify the investment in an analytics program’. An organization needs to define expectations and understand the value that each analytics use case presents. The investment in these programs can be significant and it is important to analyze the return before making them.”
