Utilities and Big Data: Using Analytics for Increased Customer
Satisfaction | Whitepaper | OracleUtilities and Big Data: Using
Analytics for Increased Customer Satisfaction
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 money—in customer relationships.”2
The historic utility-customer relationship is rapidly changing, and
customer satisfaction has become an increasingly important key
performance metric for today’s 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
utility’s 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 service—in 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.
1
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Utilities and Big Data: Using Analytics for Increased Customer
Satisfaction
While drivers will change based on each utility’s needs, each
provides a compelling argument for using analytics. From customer
satisfaction drivers—such as insights into customer usage
(individual or aggregated) and the ability to target specific
programs to specific customer groupings—to 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:
4
26%
34%
40%
42%
47%
47%
51%
57%
Establish new pricing programs
Using predictive analytics to minimize outages/improve
reliability
Executing demand response programs
Implementing/improving energy efficiency programs
Providing usage patterns to customers
Clearly, there is plenty of room in which utilities—whether
electricity, gas or water and wastewater, each with their different
data challenges and customer needs—can 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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Utilities and Big Data: Using Analytics for Increased Customer
Satisfaction
segmentation basically limited to three different types of
customers—residential, 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, today’s customers
are benchmarking their utility’s 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
Settlements reporting
to support customer inquiries
New meter health
AMI contract validation
Meter inventory tracking
customers
information and load profiles
Electrical arc flashes
Utilities and Big Data: Using Analytics for Increased Customer
Satisfaction
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 doesn’t 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 customers—including usage data
and billing/payment information, how the customer prefers to
communicate with the utility, information from third-party data,
and more—the 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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Utilities and Big Data: Using Analytics for Increased Customer
Satisfaction
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
Today’s 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 utility’s 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 and Big Data: Using Analytics for Increased Customer
Satisfaction
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 meters—slowly dying
meters—at 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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Utilities and Big Data: Using Analytics for Increased Customer
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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.
Let’s 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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Utilities and Big Data: Using Analytics for Increased Customer
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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 today’s 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.
Proper Input Practical Outputs Real Results
Premise Data
Weather Data
Ass et Data
AMI Meter Data
Reports
Actionable work into Operational System s
Sam ple Outputs
• Custo mer Tamp ering
• Maintenance Prio rities
REDUCED SAIDI/CAIDI
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Utilities and Big Data: Using Analytics for Increased Customer
Satisfaction
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
utility’s 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 utility’s unique and
changing needs
Proven Results Utility Focused Comprehensive Utility Operations for
Utilities
2,600+ Utility cu stomers
Forecasting
To us, analytics is not just a theoretical exercise; it is a
pragmatic approach to getting the most out of the enterprise’s
operations, in order to provide top-quality, best-of-class,
personalized solutions for its customers, no matter what their
needs.
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Increased Customer Satisfaction
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