Don’t leave money in the couch
How to get more value from data investments you’ve already made.
It’s frustrating to really need something badly but not have the money to pay for it. Sometimes you can find some change in the couch or try to shift funds by “robbing Peter to pay Paul,” but that’s not a sustainable strategy. Wouldn’t we all just love to have a windfall, win the lottery or have a solution dropped in our lap?
Check that couch again, as you may be sitting on a gold mine.
As utilities grapple with a seemingly unending litany of regulatory and market uncertainty, the pressure is on to take bold steps to secure your future relationship with your customers. But how can you figure out what your strategy should be, let alone how to pay for it, when predictability and the old ways of funding such endeavors is changing? Given shifting revenue projections from the core business and the likelihood of mixed signals from regulators, you can’t afford to sit still. That’s particularly true when we all know that customer expectations, wants, and needs are continually expanding – in lock step with technology innovations that are outside of your control.
The good news is you are already sitting on a solution: DATA. When extracted, cleaned up. and analyzed, there’s “found money” in data.
Think about it: Utilities have invested years and years into selling products, engaging with customers through energy efficiency and customer service programs, and providing related services such as training to trade allies. Reams of data have been generated through all those touch points, data that represents more than just loose change, but rather is a virtual gold mine of raw material from which to develop your strategy and as a source of new product lines for your customers. Even better, data isn’t static, but is continually being updated, refreshed, and expanded. The trick is to understand how to extract insight quickly and efficiently.
Have you ever heard the phrase “Make your money work for you?” That’s the alchemy of data analytics. Here are some examples of how utilities are extracting value from using the information they already have in order to find new sources of revenue – no couch diving required.
Shared Solution: Helping Small Local Power Companies Connect with Customers
The Challenge: A common problem for smaller distribution utilities is a simple lack of manpower. They may have AMI data, but they don’t have the staff or the systems to take advantage of it. And individually, they don’t have the time or money to invest in the design of a system to turn the data into insights.
The Solution: Grouping a few local power companies together, DNV GL is serving as a “virtual load research department” to develop a shared platform for analyzing their data. Each participating utility benefits from shared development costs while protecting the confidentiality of their own customer data by limiting who has access.
For a selection of power companies participating in the test case, DNV GL ingested their demand data, using data transfer protocols designed to protect customer confidentiality, cleaned it up, and turned it into useful market characterizations and segment profiles for use by the individual local power companies in customer engagement. In the next phase of work, each participating utility will be able to access their own data using dashboards that display customer characteristics in various graphics and maps. The data are continually refreshed so dashboards will always have the most current information.
Finding Needles in a Haystack: Engaging Underserved Markets
The Challenge: At the other side of the spectrum, larger utilities are also experiencing challenges getting full value from the data they already have. Those with longstanding energy efficiency programs are particularly interested in how to use that data to identify where remaining savings can be found.
The Solution: Analytics can find pockets of remaining opportunity in mature markets. By combining usage data, historical program data from tracking systems, and purchased datasets, data analytics keeps these programs growing.
For most utility clients, analytics insights will show that over 90% of all consumption is represented by 10% of customers. Identifying customers that fell into the top 10% and had not participated in an incentive program in the last three years is a great place to begin an analysis. In addition, utilities want to know which of their participating customers may be leaving opportunities untapped, so they can go back and encourage reengagement. Addressing these issues can be a very complex task. To solve the problem, DNV GL developed a benchmarking and technology recommendation scorecard using machine learning and clustering that helps outreach professionals and account managers work with customers to identify energy savings opportunities. Machine learning regression is also used to determine when projects will meet key milestones, which improves forecasting and helps identify when to check-in on project progress. Such information tightens the band between projections and outcomes and can help program managers better predict how and when they are likely to reach their goals.
In the scorecard and regression solution, operational efficiencies are achieved through data analytics and machine learning and provide an historical, current, and future view of program activity. The dynamic nature of the system also means it provides insights on a near real-time basis.
It’s all about Jobs: Keeping Trade Allies Engaged
The Challenge: All utilities know that their customer engagement efforts would go nowhere without the support and active engagement of the market players and trade allies (TAs) that serve those same households, businesses, and municipal entities in our communities. Decades of effort have been put to engaging TAs in energy efficiency programs, primarily through incentives paid either directly to them or to their customers. These partnerships are a major asset that may be threatened as programs mature, or as some utilities cut back on rebates or shift gears toward training and education. The challenge here is to find ways to keep these market actors engaged and maintain TA relationships that are so critical to the success of their businesses as well as yours.
The Solution: Data analytics, again, can be a critical part of the solution to your trade ally challenges, using data you already have. As noted above, some of DNV GL’s clients are using utility-augmented data to identify underserved markets by business type, geographic location, or measure. This helps utilities better target marketing and outreach resources to where they can get the most payoff. This information can be shared with TAs as leads for them to pursue, following tiered utility TA certification programs. For operational efficiency, distributors and trade allies can use the insights gleaned from data analytics to track progress of their projects and/or applications or find out if customers meet eligibility requirements. Since the data a regularly refreshed, the data analytics platform can serve as a continuing pool of new leads that your key partners can tap into.
The costs of NOT mining all that data are too high to contemplate. It costs too much to collect, protect, and store data, so you might as well get off the couch and make good use of it through data analytics. There’s more than loose change at stake.
Learn more about DNV GL’s data analytics approach and how you can make your data work for you by emailing us.