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• Cognizant 20-20 Insights
Why UK Utility Suppliers Can Get ‘Smarter’
with Advanced Analytics
Executive Summary
Utility suppliers in the UK operate in an increasingly complex environment driven by everescalating demands on capital, continually
evolving technology and continuously changing
regulations.
The UK electricity and gas sector, in particular,
is amongst Europe’s most competitive markets,
addressing the energy needs of approximately 29
million customers. The industry is deregulated and
consists of numerous big-to-medium suppliers. By
nature, deregulation brings fierce competition,
and the supply side of this market is no different.
Since May 1999, all customers, whether they are
domestic, commercial, or industrial, are eligible
to change their gas or electricity supplier. In fact
during 2010, 17% of electricity consumers and
15% of the gas consumers switched suppliers.1
Three major issues have emerged:
• Deregulation
is fuelling increased competition. Ofgem, the government body that
regulates the electricity and gas markets in
Great Britain, is pushing for even more competition to bring down any barrier to switching.
On the back of its recent Retail Market Review,
Ofgem has recommended that to make it
simpler for domestic consumers to compare
prices and choose a better deal the number
of tariffs for standard evergreen products
from each supplier be restricted to only one
cognizant 20-20 insights | february 2012
per payment method. It has also proposed to
standardize the format of these tariffs, with
suppliers allowed to compete on a single “per
unit” price. Consumers would then be able to
tell at a glance whether they can save money
either by switching suppliers or by moving to a
new deal. This is expected to impact over 75%
of customers who are on standard products.
• Growth
and heritage systems challenges.
Given the competitive nature of the market,
controlling operational costs and improving
efficiency have emerged as top priorities for
suppliers. By and large, operational inefficiency
is caused by legacy IT systems that have not
kept pace with suppliers’ torrid growth, which
has created unintended waste and redundancy.
For example, many suppliers struggle to obtain
a single view of the customer, which leads to
numerous operational shortcomings in some
basic functions (i.e., debt collections, customer
service, etc.). This directly impacts the top and
bottom line of suppliers.
• The advent and pressures of smart meters.
Among the key benefits of proliferating smart
meters placed across the UK’s power generation
grid is the access suppliers will have to a large
amount of accurate billing data (50 million new
meters will be added over a seven-year period 2).
This data will enable suppliers to increase
billing accuracy, customize their offerings (e.g.,
time of use (ToU) tariffs) and reduce operational costs. Suppliers could optimally use
From Data to Insights
Sales
Channel
Campaigns
Revenue
Cross-sell
Up-sell
Customers
Lifetime
Value
Churn
Cost to
Serve
Segment
Customer
Experience
Products
Pricing
Margin
Operations
Capacity
Planning
Forecasting
Vendor
Efficiency
Optimisation
Competition
Portfolio
Leakage
Effectiveness
Loyalty
Performance
Figure 1
this data to deliver more customer value (i.e.,
more relevant and “greener” services), thereby
increasing customer loyalty.
Given these challenges, suppliers will need to
differentiate and take necessary steps to breed
customer loyalty and increase efficiency. Inaction
means that the gap between proactive and
reactive suppliers in this market will only widen
at a faster rate. This white paper discusses the
role analytics can play in making UK utilities
suppliers smarter about how they move forward
to seize market opportunities. It also covers
various models that can be deployed to leverage
analytics, depending on supplier maturity and
risk profile.
Creating Competitive Advantage by
Applying Analytics Holistically
Analytics is one tool suppliers can leverage to
address market-driven challenges. Traditionally,
suppliers’ business processes generate a stream
of useful data collected during the entire meterto-cash operating cycle.
As a result, a variety of analyses can be conducted
which can individually and collectively deliver
extremely useful business insights (see Figure 1).
These insights can inform a series of actions and
drive the overall strategy of any given supplier
(Figure 2).
Holistic Approach to Analytics
Action
Insight
Information
Data
Strategy
Business Initiatives,
Tracking Enterprise
Metrics, Balanced Scorecard,
Strategy Maps
Advanced Analytics
Predictive & Optimisation Modeling, Business
Processes Analysis, Functional Analysis
BI/Reporting
Data Mining, OLAP Modeling, Performance Reporting,
Dashboards, Scorecard
Data Integration & Management
Data Warehousing, Data Quality, Master Data Management,
Metadata Management
Figure 2
cognizant 20-20 insights
2
Historically, supplier organisations have used
analytics on an ad hoc basis. This “ad hoc-ism”
originated from the fact that analytics were
triggered by discrete events. For example, the
customer service team might want an analysis
of agent handling time (AHT) in order to reduce
operational costs. Although this analysis might
lead to certain actions which reduce AHT,
enacting these measures may directly impact
an individual agent’s ability to cross- or up-sell
customers (these customers would have to
have been identified through a different set of
analyses). Hence, the need for a more holistic
approach to analytics (see Figure 3).
Holistic Approach to Analytics
Demographics
Value
Risk
Customer
Lifetime
Index
Cost to
Serve
Loyalty
But with fierce competition, coupled with the
deluge of data, utilities are beginning to realize
the benefits of a holistic approach. We illustrate
this through an example. A customer’s lifetime
value can potentially combine a variety of factors
such as demographics (age, location, segment,
etc.), value (consumption, tariff plan, range of
products purchased, etc.), cost to serve (debt,
customer contact, call center operations, etc.),
loyalty (renewals, stickiness, net promoter score)
and risk (churn and payments).
Figure 3
Challenges to Implementation
The previous example showcases the efficacy
of a holistic approach to analytics. In the UK’s
competitive energy markets, suppliers are continuously seeking more innovative and effective
ways of operating to gain market share. They
work hard to understand market dynamics,
customer behaviour and their impact on internal
activities, but their inability to identify and
In our opinion, this represents an optimisation
problem that can be resolved progressively.
To start with, we can optimise the individual
parameters in each silo and then integrate the
processes over the medium to long term (see
Figure 4).
Progressive Optimisation Approach
Tackle more holistic
parameters in medium term
Customer
Lifetime Index
Long
Term
Medium
Term
Short
Term
Time scale
Demographics
Value
Optimise at the
organisation level in
the long term
Cost to Serve
Loyalty
Customer
Segmentation
Consumption
Analysis
Market/
Product
Segmentation
Tariff Plan
Analytics
Online/
Offline
Efficiency
Early/ Late
Collections
Agent
Efficiency
Cross Sell/
Up-sell
Analytics
Contact
Reduction
Debt
Servicing
Agent
Handling Time
Contact Cost
Debt
Call Centre
Servicing
Churn
Modelling
Account
Receivables
NPS
Theft
Contact
Efficiency
In short term, optimise
the individual point-based
parameters
Figure 4
cognizant 20-20 insights
3
Risk
Analytics’ Challenges
Organisation
Process
People
Technology
Analytics is not seen as
a lever for supporting
corporate innovation.
Structuring of analytics
function to optimise only
a single business area.
Lack of proficiency in
quantitative methods
applicable for utilities.
Unavailability of data at
granular levels.
Analytics is not classified
as a distinct capability.
Focus on current and
future goals rather than
historical trends across
enterprise.
Unclear career progression and lack of
mentorship.
High cost of technology
for enterprise-wide
solution.
Unclear roles and responsibilities for modelling
between IT and analytics.
Insights from analytics
are tested only for
limited business areas.
More confidence on
experience and intuition
rather than facts.
Over-reliance on
technology as an
analytical solution.
Deployment of multiple
point solutions in
isolation rather than
looking at the big picture.
Inability to select right
data and in right format
for analysis.
Focus on meeting
individual or business
unit’s objectives rather
than working towards
a balanced scorecard
model.
Inability to validate data
integrity and quality at an
enterprise level.
Lack of single view of
customers and relating
them to customer
segments.
Focus on incorrect or
unnecessary metrics.
Time to design an
enterprise-wide analytics
solution.
Not involved in planning
process of strategising
for business units/propositions.
Relating analytics to
KPIs of a business area
and not on multiple
aggregated levels.
Complexity involved in
integrating data from
multiple sources.
Figure 5
correct inaccurate/inconsistent data typically
creates misalignment between expectation and
results. Multiple data sources and disparate silos
of data often mean individuals or business units
are using different information than their counterparts, which generally results in misleading or
complicated messages for stakeholders. There
is also an opportunity cost due to their inability
to identify potential or existing customers who
can be acquired or retained to maximise value,
rather than targeting each and every one with
generic offers and gaining minimal conversion.
Key analytics challenges faced by suppliers are
summarised in Figure 5.
As utilities move towards providing products
and services for smarter homes and businesses,
they are also making significant investments in
new technologies that will streamline data and
processes. IDC’s “2011 Vertical IT & Communications Survey” found that 86.7% of utilities
worldwide had invested in analytics and over
one-third have been able to demonstrate positive
business benefits.3 However, most organisations
are a long way away from achieving “analytical
maturity.”
cognizant 20-20 insights
Various Operating Models
for the Analytics Function
As analytics emerges as a key ingredient for
organizational success, different variations of
operating models have emerged that can be
deployed depending on the supplier’s maturity
and business goals. The effectiveness of
any of these models also depends on senior
management buy-in and application for tactical/
strategic decision-making.
• Distributed
model: Different functional or
business units have separate groups that collect
and analyse data. This is the easiest model to
implement but it brings with it a very immature
approach to analytics, especially where various
business units within the supplier’s organisation intersect with one another. For example,
a customer can be considered an existing or
potential residential, business and services
account, all at the same time.
• Offshore/On-site
model: An on-site or customer facing team is used for data gathering,
scoping, model creation and liaising with functional or business areas while offshore teams
4
generate reports based on these models and
interpret outcomes for decision-making.
• Front-end/Back-end model: Responsibility for
analytics and providing meaningful insights is
split between external facing and operational
teams. Data related to customers, competitors, suppliers and industry are analysed by
a front-end team for decision-making related
to sales, marketing, campaign management
and customer experience. At the same time,
a back-end team works on data related to call
volumes, agent performance, cost and operational activities.
• Centre
of Excellence model: A corporate
centre of excellence (CoE) supervises the enterprise-wide collection of data and analysis. The
CoE helps individual business units with their
specific analytics requirements and provides
the latest and most relevant insights. Individual
business units/functional areas are assigned
members from a central pool of resources for
providing analytics and business intelligence.
These members can work on a project or
business as usual (BAU) mode, depending on
the requirement. All resources report to the
central pool and can be redeployed in other
areas of business when necessary. Knowledge
management and communication between BUs
and the CoE is the key to success in this model.
Effective implementation and management of data
or information depends on the ability to collect,
analyse, interpret and act quickly and effectively.
Most organisations are not only working on data
from traditional sources, but embracing emerging
“social media analytics,” “predictive analytics,”
“Web analytics,” “customer value analytics” and
“real-time decisioning,” which take the analytics
discipline to another level. With these techniques,
utilities can obtain more real-time, accurate
and effective ways of delivering meaningful and
relevant insights and foresights that have the
potential to project/predict customer behaviour.
Due to the growing importance of collecting and
analysing vast amount of data there is a logical
shift from the distributed or individual functional
area level analytics to a more enterprise-wide,
corporate-level model. Suppliers can adopt a
progressive approach to building analytics with
a view toward getting to a level where analytics
can be provided as a service to various stakeholders in the organisation. From ad hoc analytics,
suppliers can move into complete processes and
then to platform-based enterprise-wide functionality (see Figure 6).
Conclusion
Given shifting regulatory sands, the proliferation
of smart metering and a greater green consciousness that is sweeping the business and consumer
worlds, UK utilities have reached a major shift
point.
As such, holistically harnessing the power of
enterprise analytics, across various silos and
functional areas, can enable them to reduce operational costs and achieve greater levels of operational agility, while more effectively meeting new
regulatory and market mandates, with minimal
operation disruption.
Taking Analytics to a Higher Plane
Analytical
Outsourcing
& Analyticsas-a-Service
Analytics Maturity
Analytical
Applications
& Platforms
Analytical Outsourcing &
Analytics-as-a-Service
Increasing
Analytical
Maturity
In-process
Business
Analytics
Ad Hoc
Analytics
Basic
Analytics
Services
Analytical Applications & Platforms
Joining, Leaving and Movement,
Meter, Billing & Consumption,
Payment & Collections
Commercial, Risk & Fraud Management
Customer Service
Ad Hoc Analytics
Energy
Analytics
Time
Figure 6
cognizant 20-20 insights
5
Energy suppliers that attempt to leverage
analytics without consistent and accurate
information will struggle to compete and miss
emerging business opportunities. The speed
with which supplier organisations adopt and
establish analytics practices will determine which
companies achieve fact-based advantage in a
fast-changing and ultra-competitive environment. Inaction will only widen the gap between
proactive and reactive suppliers.
Footnotes
1
http://www.ofgem.gov.uk/Markets/RetMkts/rmr/Documents1/IpsosMori_switching_omnibus_2011.pdf
2
http://www.ofgem.gov.uk/Media/FactSheets/Documents1/consumersmartmeteringfs.pdf
3
http://www.teradata.com/WorkArea/DownloadAsset.aspx?id=17013
4
http://www.gartner.com/it/content/1322300/1322319/april_7_top_5_technology_trends_to_disrupt_crm_
ethompson.pdf
About the Authors
Arvind Pal Singh is a Senior Manager within the Energy and Utilities Practice of Cognizant Business
Consulting. He has more than 13 years of energy industry and consulting experience and has led and
executed multiple consulting engagements. At present, Arvind leads Cognizant’s UK E&U Consulting
Practice. He holds a master’s degree in international business and an engineering degree. He is also a
TOGAF certified Enterprise Architect. Arvind can be reached at Arvindpal.Singh@cognizant.com.
Vinitesh Gaurav is a Senior Consultant within the Energy and Utilities Practice of Cognizant Business
Consulting. He has more than five years of consulting and business analysis experience, working with
UK and European customers in the energy and utilities, insurance and reinsurance industries. His
areas of expertise include customer acquisition and retention, customer self-service, smart metering,
business energy management, billing, energy services, service-oriented architecture, e-commerce and
Web technologies. He has an MBA in systems and marketing and an engineering degree in computer
science. He is also a certified Prince 2 practitioner and Agile Scrum Master. Vinitesh can be reached at
Vinitesh.Gaurav@cognizant.com.
About Cognizant’s Energy & Utility Practice
Cognizant’s Energy & Utilities (E&U) Practice is among the company’s fastest growing business units. Backed by strong
focus and commitment to service delivery excellence, our E&U practice has established a unique position for itself by
delivering strategic blueprints, technology frameworks and innovative consulting solutions to various players across
the global energy and utilities industry. In addition, we provide vital business transformation, process optimization and
information management solutions across the industry value chain.
About Cognizant
Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process outsourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered in
Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industry
and business process expertise, and a global, collaborative workforce that embodies the future of work. With over 50
delivery centers worldwide and approximately 137,700 employees as of December 31, 2011, Cognizant is a member of
the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing
and fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant.
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