Utilities and Big Data: Using Analytics for Increased Customer

An Oracle White Paper December 2013

Utilities and Big Data: Using Analytics for Increased Customer Satisfaction


According to Accenture research, companies across many different industries are getting much better at understanding

customers by using analytics and, more important, by using data-driven insights to design and improve the customer

experience. 1

Introduction

Analytics can help to transform just about any part of your business or organization. Many organizations start where they make their moneyin customer relationships.2

The historic utility-customer relationship is rapidly changing, and customer satisfaction has become an increasingly important key performance metric for todays utilities. As a result, the new, enlightened utility is one that has turned to data-driven, information-enabled decision-making to better serve its customers.

The use of analytics is fundamental to improving and sustaining a utilitys all-important customer connection, as well as its business performance. There are numerous customer-focused drivers at play, one of the biggest being the ability to provide more customized, individual servicein effect, a more personal and effective relationship with each one of its customers.

Analytics is Fundamental to Improving and Sustaining Utility Business Performance:

1 A new path to growth: How to stay a step ahead of changing consumer behavior, Paul F. Nunes, Samuel Yardley and Mark Spelman, Accenture, June 2013.

Analytics at Work: Smarter Decisions, Better Results, Thomas H. Davenport, Jeanne G. Harris, Robert Morison, Harvard Business School Publishing Corporation, 2010.

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While drivers will change based on each utilitys needs, each provides a compelling argument for using analytics. From customer satisfaction driverssuch as insights into customer usage (individual or aggregated) and the ability to target specific programs to specific customer groupingsto increased reliability, operational efficiency and safety drivers, there are numerous reasons to implement analytics processes across the utility enterprise.

Yet, while there are many ways in which new data can be more effectively used to better serve customers, a recent Oracle survey of more than 150 North American senior-level electrical utility executives found that just half of utilities today are fully leveraging smart grid data to improve customer service (through forecasting, demand management and improved reliability.)3

The data usage areas in which the most activity was reported were:

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26%

34%

40%

42%

47%

47%

51%

57%

Alert customers with usage spikes

Establish new pricing programs

Target customers for new programs

Using predictive analytics to minimize outages/improve reliability

Executing demand response programs

Identifying trends and forecasting demand

Implementing/improving energy efficiency programs

Providing usage patterns to customers

Clearly, there is plenty of room in which utilitieswhether electricity, gas or water and wastewater, each with their different data challenges and customer needscan grow their use of data analytics.

In fact, across the board utilities are grappling with each step of the data review and reporting cycle, particularly when it comes to extracting value, or actionable intelligence, from the data. In our survey, for example, only 32 percent of the electric utilities we talked to gave themselves an A grade in delivering useful information to customers.5

The challenges are many. A big challenge is cultural: While utilities have traditionally espoused customer-centric goals, historically it has been a straightforward transactional relationship, with

3 Utilities and Big Data: Accelerating the Drive to Value, Oracle Utilities, July 23, 2013. 4 Utilities and Big Data: Accelerating the Drive to Value, Oracle Utilities, July 23, 2013. 5 Utilities and Big Data: Accelerating the Drive to Value, Oracle Utilities, July 23, 2013.

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segmentation basically limited to three different types of customersresidential, commercial and industrial, and other. Additionally, customer need has traditionally been identified very simply as a basic desire for reliable electricity, gas or water at affordable prices. But the reality is, todays customers are benchmarking their utilitys service against that provided by other day-to-day service providers, and utilities are scrambling to catch up. But utilities are working to evolve their relationship with customers not only to increase satisfaction scores. There are real business benefits to doing so, as well, including reduced operating costs, reduced customer churn in competitive markets, and more detailed information for rate cases and other regulatory requirements.

The data is now available with which to do all of this. But turning raw data into actionable intelligence requires new tools, new processes, and a step-change shift in utilities traditional approach to their customers.

There are many areas in which utilities can focus their analytic efforts in order to develop more proactive, rather than reactive, responses to customer needs. In the following pages we have detailed a number of specific use cases for customer-focused analytics that more fully describe the art of the possible.

FUNCTIONAL AREA BUSINESS CHALLENGE OPPORTUNITY

Billing Exceptions Better prioritize and manage

exceptions

Billing exceptions

High bill prioritization

Low bill prioritization

Bill cycle monitoring

Tracking usage information Consumption reporting

Settlements reporting

Call Center Support Providing relevant usage information

to support customer inquiries

Consumption graphs

Usage comparison graphs

Meter Operations Managing problematic meters Defective meter ID

New meter health

AMI contract validation

Tracking metering issues Geospatial data quality/reporting

Meter inventory tracking

Energy Efficiency/Demand

Response

Targeting programs to the appropriate

customers

Targeted marketing based on customer

information and load profiles

Safety Theft resulting in unsafe situations Gas leaks

Electrical arc flashes

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From delivering more tailored customer service and improving response time to customer issues to targeting customers for specific energy efficiency programs and avoiding costly capital expenditures, analytics are in play in utilities around the globe as they begin to explore new avenues for using the data now available to them.

Delivering Personalized Service

Personalized service can begin with meter data, but it doesnt end there. As the Utility Analytics Institute noted in a recent report, meter data can provide insights into customer behaviors and preferences by helping utilities understand a variety of factors about the customers, from the types of appliances that they use, to whether they have an electric vehicle, to their highest-use times throughout the day.6

The more a utility knows about its customersincluding usage data and billing/payment information, how the customer prefers to communicate with the utility, information from third-party data, and morethe better able a utility is to be able to manage each customer relationship individually.

The issue of personalized service reaches into almost every customer analytics use case identified here. From improving billing accuracy and alerting customers to unusual usage spikes to providing call center personnel with a 360-degree view of each customer and faster and more complete resolution of high bill calls, analytics plays a role in each, all benefitting in increased customer satisfaction.

And, just as customer service must be personalized, so too must the analytic tools and solutions a utility chooses be specific to the problem or problems it is trying to solve.

Proactively Addressing Potential Safety Risks

Using meter consumption data, customer account information and third-party data (such as weather), utilities are able to reduce potential safety risks. A utility can quickly identify cases of usage spikes and send a field crew to investigate, repair and report back (as field crew feedback closes the loop and enhances the effectiveness of the algorithm).

Here are some real-life examples of how utilities are using analytics to prevent customer safety hazards.

Prevent Gas Leaks by Identifying Usage Spikes: Using analytics, one utility discovered that thieves were entering vacant premises and stealing copper pipes or appliances. In some cases, a pipe would break or the gas would be left on, either of which could have led to massive fires if not addressed quickly. Leveraging very sophisticated algorithms, this utility was able to detect these gas

6 Meter Data Analytics: Analytics in the post-smart meter world, H. Christine Richards, Utility Analytics Institute, 2013.

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leaks while eliminating false positives caused by other causes (such as pool heaters), thereby reducing unnecessary truck rolls.

Daily monitored tests by this utility have detected, on average, 10 cases per year of usage spikes that the utility believes would have led to potential public safety hazards within a short time had they not been so quickly detected.

Proactive Bill Adjustments after a Wildfire: In 2012, an electricity, gas and water/wastewater utility had a major wildfire in its service area that necessitated the evacuation of a significant number of its residential customers from their homes. As a fire mitigation tactic, many of these evacuated residents turned on their water hoses and sprinkler systems before they left.

Analytics enabled the utility to proactively identify each of these customers and reduce the excess, or fire preventative, water usage from their bills. Customers were not faced with a high bill or the need to dispute the charges.

Improving Response Time to Customer Issues

Todays consumer has come to expect immediate, 24/7 response to any issue, from an outage to a billing query or complaint. They receive it from other service industries, and feel their gas, electric, or water utility should be no different. With every issue and every query, customers want a timely, personalized and proactive experience.

However, from a utilitys perspective, the coordination and analysis of numerous sources of customer data (both structured and unstructured) can be overwhelming without the proper tools to reveal important trends. In this case, integrated and embedded business intelligence tools can provide immediate answers to questions such as:

Are we accurately detecting and identifying defective meters or meter theft?

Are we providing our customer contact representatives with the specific information necessary for them to expedite and resolve high-bill inquiries and other customer questions?

Are we properly identifying usage spikes (i.e., increased usage due to weather conditions versus potential gas or water leaks)?

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Utilities are using analytics to fully operationalize their meter-to-bill process, redesigning their billing exception queues to reduce false positive exceptions, identify new anomalies previously missed, reduce break-to-fix time, and automate many exceptions to bypass manual processes and go directly to work orders.

This type of use of analytics is an immediate win/win for the utility and for its customers. One example comes from the water industry. Historically, most water customers usage was not metered, but billed monthly on a flat rate. In several areas, thanks to water scarcity and increasing costs to the utility due to aging infrastructure, water usage is now metered. But metered usage is proving to be a boon to customers, as well, as the resultant data is being used to identify leaks and to provide other beneficial customer services.

However, identifying water leaks (as opposed to gas leaks) is complicated by seasonal issues such as the use of sprinklers, making the use of analytics and the ability to pull in third-party data (weather, etc.) a real necessity. By being able to use daily temperature to filter out false spike positives, field crews can then be dispatched to real leak issues more quickly, resulting in near-immediate cost savings for the customer.

Customer Issues Use Cases

Here are some examples of how utilities are using analytics to respond more quickly to customer issues and in some cases address them before the customer is even aware there is an issue (e.g., they receive a delayed or adjusted bill).

Meter-to-Bill operations: Quickly and more accurately identify defective meters or metering conditions.

By eliminating false positives, a U.S. Midwest utility was able to reduce one of its manual review work queues by 80 percent and its largest back office billing exception work queue by 38 percent.

Another utility in the southeast United States leveraged analytics to create an automated process to prioritize billing exception queues from its legacy Customer Information System (CIS). This increased the hit rate effectiveness of its High/Low billing process from 2 percent to over 90 percent, allowing for a time reduction equivalent to 2.2 full time employee hours per year.

Meter accuracy: Calculate performance metrics to validate performance of the advanced metering network provider to avoid billing and lost revenue implications.

Using analytics, a U.S. Midwest utility was able to uncover an unknown defect in some solid state electric metersslowly dying metersat a near 100 percent hit rate in detecting and replacing these meters each month. The estimated savings is over $750k in annual revenue recovery.

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Communicating Restoration Progress

When customers are without electricity, gas or water, they want their utility to be on the ball with a speedy assessment of the problem, the ability to widely communicate an accurate estimated time of restoration (ETR), and a clear explanation, after the fact, of why the service went out in the first place.

Lets use as an example a major electrical outage due to a heavy storm. In an ideal situation, your network management system would have the ability to integrate and analyze data from areas across the entire utility enterprise, as well as third-party data appearing on social media channels, to provide detailed information and insights into what is necessary to restore power as well as how long it will take.

Here is how it could work for a water utility: A customer notices a leaking water pipe in the neighborhood. The social-savvy customer can post a photo of the leak to quickly alert the utility. The water utility actively monitors its social media sites (Twitter, Facebook, etc.) and sends this information to its outage management system. The utility can determine the location of the leak with GPS coordinates from the photo, as well as the right crew and equipment to make the repair, and send the information to its outage team. This type of information and analysis enables the utility to more accurately provide an estimated time of restoration to all the customers impacted, often before they even realize there is a problem.

More importantly, this type of information is frequently the most accurate source of information the utility can use to respond to customer calls as well as provide proactive updates for social media, press and other stakeholders regarding storm restoration progress and estimated repair times.

Offering the Right Program to the Right Customer

Being able to target the right demand response or efficiency program to the right customer dramatically increases both the expected uptake on each program and the savings results enabled (both for the customer and for the utility).

There are many reasons for implementing demand response programs, from the need to delay or avoid constructing new electric or gas generation to the need to avoid new substation construction. Efficiency programs can be implemented to conserve water during hot or dry months, or to reshape the electricity or gas demand during peak periods.

Being able to accurately target high potential customers is the key to a successful demand response or efficiency program. Analytics provides that key not only by increasing the accuracy of the targeting, but also by reducing the outreach costs to potential customers.

Customer Program Targeting Use Cases

Here are some examples of how utilities are using analytics to identify and properly market demand response and energy efficiency solutions to either avoid construction or assist customers in lowering their bills.

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Avoid costly capital expenditure: An electric utility was faced with a costly substation upgrade due to an overloaded transformer location on a remote island, requiring invasive construction in a residential area. By analyzing data from all of its feeders, the utility was able to quickly identify customers on feeders contributing to poor load factors with electric heat.

With this information in hand, the utility was able to successfully target a demand response solution to the problem, thereby avoiding costly construction.

Target customers for energy efficiency programs: Using analytics, utilities are able to develop lists of target customers for marketing and quantify program impacts to determine incentives and support program evaluation.

A Midwest utility was able to identify its highest residential users of gas (top 10 percent) across more than 150 different segments, based on the vintage and square footage of their homes, in order to offer them energy efficiency program options. This resulted in higher program participation rates and lower marketing costs.

How to Get Started

Is your utility using analytics to drive similar business value? What areas should you focus on? Where are the biggest opportunities?

Oracle understands how important the utility-customer relationship is in todays customer-centric world. We work with utilities around the world to drive business benefits utilizing the data they already own to improve business processes. We understand how important it is for utilities to be able to deliver quick results.

Practical Approach. Real Results.

Discover insight not visible through traditional BI approaches

Proper Input Practical Outputs Real Results

Premise Data

Cus to mer Attrib utes

Weather Data

Netwo rk Co nnectiv ity

Ass et Data

AMI Meter Data

KPI Dashboards with Drill Down Capability

Reports

Calculations/Algorithms for Ad Hoc Anal ysi s

Actionable work into Operational System s

Sam ple Outputs Meter Malf unc tio ns Custo mer Tamp ering Overloaded

Trans f o rmers Billing Co rrec tio ns Maintenance Prio rities EE / D R Targ ets

REDUCED Back-of f ice Processing

REDUCED SAIDI/CAIDI

REDUCED Public Saf ety Hazards

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With Oracle, there is no one size fits all approach to analytics solutions. We offer far more: a wide choice of applications and technologies to fit the precise requirements of the problems you are trying to solve. When you choose Oracle, you work with our industry experts to define your immediate analytics goals and your longer-range directions. You move forward at the speed your staff and your budget can accommodate. You choose the right applications and technologies from the most comprehensive utilities analytics solutions available.

Oracle offers more than just tools. We provide out-of-the-box analytics solutions that are focused on the fundamentals that drive utilities today. From credit and collections, revenue and customer to device, grid, and meter data; from work and asset management to mobile workforce, Oracle offers end-to-end analytics for the utilitys myriad systems and processes. We have a library of pre-built analytics with proven results that customers can leverage from Day One to drive operational benefits.

End-to-End Analytics for Utilities

Oracle offers a complete set of data handling, organizational and analytic tools that let you select the approach that works best for your utilitys unique and changing needs

Proven Results Utility Focused Comprehensive Utility Operations for Utilities

2,600+ Utility cu stomers

Customer Service

Grid Human Resource

worldwide

500M Billing, Payment, Credit

Work

Asset Supply Chain

En d-cu stomers worldwide Metering Financial Planning & Forecasting

To us, analytics is not just a theoretical exercise; it is a pragmatic approach to getting the most out of the enterprises operations, in order to provide top-quality, best-of-class, personalized solutions for its customers, no matter what their needs.

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Utilities and Big Data: Using Analytics for Increased Customer Satisfaction December 2013

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Utilities and Big Data: Using Analytics for Increased Customer