As many utilities will share at the Utility Analytics Week conference and exhibition, there is a strong relationship between the use of analytics and business performance -- whether you use analytics to streamline business operations, improve service delivery or enhance customer engagement. I believe the odds of winning with analytics investments are far better than your odds of winning in Las Vegas, and utilities that measure ROI will be positioned to make the sustainable and ongoing investment in analytics necessary to transform business operations.
Authors Thomas H. Davenport, professor of information technology and management at Babson College and Jeanne G. Harris, executive research fellow and a senior executive at the Accenture Institute for High Performance, cited the following research findings in their highly regarded book, Competing on Analytics: The New Science of Winning, "When we compared the responses of high performers (those who outperformed their industry in terms of profit, shareholder return and revenue growth -- about 13 percent of the sample) with those of low performers (16 percent of the sample), we found that the majority of high-performing businesses strategically apply analytics in their daily operations...High performers were 50 percent more likely to use analytics strategically compared with the overall sample and five times as likely as low performers."
In talking with analytics solutions providers for the Utility Analytics Institute's Customer Analytics Report and Grid Analytics Report, I found common themes when I asked what advice they would offer to utilities pursuing analytics initiatives:
- Start now
- Think big, develop an enterprise-wide strategy upfront
- Identify and prioritize business challenges to be addressed
- Define desired outcomes
- Start small, achieve quick wins, and build on early success
- Measure the value
The path to becoming a data-driven utility does not end with the implementation of a single analytics project. It is a never-ending journey consisting of a series of discrete analytics initiatives. A well-articulated strategy that considers people, process and technology implications will act as an umbrella under which utilities can pursue and measure the success of discrete initiatives.
Even as utilities forge ahead with analytics, I have observed that few measure their ROI for analytics projects. Possible reasons include:
- Lack of a baseline: Insufficient data and/or the lack of reliable data prevent utilities from measuring incremental benefits delivered by analytics initiatives.
- Cost of measurement: Business cases may fail to anticipate the funds needed to measure analytics benefits.
- Measurement challenges: Given the many variables involved in these projects, utilities may have difficulty correlating business results (e.g., customer satisfaction) with a particular initiative.
- Business case content and validation: Business cases may forecast investment needs, operational expenses, cost savings, revenue impacts and qualitative benefits, but stop short of estimating ROI. Even when estimated, utilities may not have an ongoing effort to compare actual results to business case projections.
Utilities can effectively address all of the above challenges, though it is essential to do so upfront. ROI calculations are essential to support continuing investment in data and data analytics.
An idea that has intrigued me is a self-funding business model for analytics investments. In concept, utilities would reinvest all or a portion of the returns achieved from "quick wins" back into future analytics initiatives. To be sure, initial investment is required to achieve that first win and start the process. This is akin to establishing a gambling budget for a Las Vegas trip and using winnings, or "house money," to stake future bets and generate big returns -- assuming all goes well!
The good news is that analytics investments are not nearly as big of a gamble. Even if a self-funding approach is not feasible, measuring ROI can provide convincing evidence of the value generation possible. It is much easier to place that next bet on analytics, if the utility can substantiate the return contributed by earlier investments in data and data analytics.
Kim Gaddy is a consultant and a Utility Analytics Institute analyst with extensive experience in customer-facing leadership roles in the utility and telecommunications industries.
