In the face of deregulation, green consciousness, and smart metering, UK energy suppliers are increasingly turning to a holistic approach to analytics. Some major approaches include distributed; offshore, on-site; front-end, back-end; and Centre of Exellence models.

1. • Cognizant 20-20 Insights
Why UK Utility Suppliers Can Get ‘Smarter’
with Advanced Analytics
Executive Summary per payment method. It has also proposed to
standardize the format of these tariffs, with
Utility suppliers in the UK operate in an increas-
suppliers allowed to compete on a single “per
ingly complex environment driven by ever-
unit” price. Consumers would then be able to
escalating demands on capital, continually
tell at a glance whether they can save money
evolving technology and continuously changing
either by switching suppliers or by moving to a
regulations.
new deal. This is expected to impact over 75%
The UK electricity and gas sector, in particular, of customers who are on standard products.
is amongst Europe’s most competitive markets, • Growth and heritage systems challenges.
addressing the energy needs of approximately 29 Given the competitive nature of the market,
million customers. The industry is deregulated and controlling operational costs and improving
consists of numerous big-to-medium suppliers. By efficiency have emerged as top priorities for
nature, deregulation brings fierce competition, suppliers. By and large, operational inefficiency
and the supply side of this market is no different. is caused by legacy IT systems that have not
Since May 1999, all customers, whether they are kept pace with suppliers’ torrid growth, which
domestic, commercial, or industrial, are eligible has created unintended waste and redundancy.
to change their gas or electricity supplier. In fact For example, many suppliers struggle to obtain
during 2010, 17% of electricity consumers and a single view of the customer, which leads to
15% of the gas consumers switched suppliers.1 numerous operational shortcomings in some
basic functions (i.e., debt collections, customer
Three major issues have emerged:
service, etc.). This directly impacts the top and
bottom line of suppliers.
• Deregulation is fuelling increased com-
petition. Ofgem, the government body that • The advent and pressures of smart meters.
regulates the electricity and gas markets in Among the key benefits of proliferating smart
Great Britain, is pushing for even more compe- meters placed across the UK’s power generation
tition to bring down any barrier to switching. grid is the access suppliers will have to a large
On the back of its recent Retail Market Review, amount of accurate billing data (50 million new
Ofgem has recommended that to make it meters will be added over a seven-year period 2).
simpler for domestic consumers to compare This data will enable suppliers to increase
prices and choose a better deal the number billing accuracy, customize their offerings (e.g.,
of tariffs for standard evergreen products time of use (ToU) tariffs) and reduce opera-
from each supplier be restricted to only one tional costs. Suppliers could optimally use
cognizant 20-20 insights | february 2012

2. From Data to Insights
Sales Channel Campaigns Revenue Cross-sell Up-sell
Lifetime Cost to Customer
Customers Value
Churn
Serve
Segment Experience Loyalty
Products Pricing Margin Competition Portfolio
Capacity
Operations Planning Forecasting Leakage Effectiveness Performance
Vendor Efficiency Optimisation
Figure 1
this data to deliver more customer value (i.e., Creating Competitive Advantage by
more relevant and “greener” services), thereby Applying Analytics Holistically
increasing customer loyalty.
Analytics is one tool suppliers can leverage to
Given these challenges, suppliers will need to address market-driven challenges. Traditionally,
differentiate and take necessary steps to breed suppliers’ business processes generate a stream
customer loyalty and increase efficiency. Inaction of useful data collected during the entire meter-
means that the gap between proactive and to-cash operating cycle.
reactive suppliers in this market will only widen
at a faster rate. This white paper discusses the As a result, a variety of analyses can be conducted
role analytics can play in making UK utilities which can individually and collectively deliver
suppliers smarter about how they move forward extremely useful business insights (see Figure 1).
to seize market opportunities. It also covers These insights can inform a series of actions and
various models that can be deployed to leverage drive the overall strategy of any given supplier
analytics, depending on supplier maturity and (Figure 2).
risk profile.
Holistic Approach to Analytics
Strategy
Action Business Initiatives,
Tracking Enterprise
Metrics, Balanced Scorecard,
Strategy Maps
Advanced Analytics
Insight Predictive & Optimisation Modeling, Business
Processes Analysis, Functional Analysis
BI/Reporting
Information Data Mining, OLAP Modeling, Performance Reporting,
Dashboards, Scorecard
Data Integration & Management
Data Data Warehousing, Data Quality, Master Data Management,
Metadata Management
Figure 2
cognizant 20-20 insights 2

3. Historically, supplier organisations have used Holistic Approach to Analytics
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 Demographics
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 Risk Value
an individual agent’s ability to cross- or up-sell Customer
customers (these customers would have to Lifetime
have been identified through a different set of Index
analyses). Hence, the need for a more holistic
approach to analytics (see Figure 3).
Cost to
Loyalty Serve
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 Figure 3
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.),
Challenges to Implementation
loyalty (renewals, stickiness, net promoter score) The previous example showcases the efficacy
and risk (churn and payments). of a holistic approach to analytics. In the UK’s
competitive energy markets, suppliers are con-
In our opinion, this represents an optimisation tinuously seeking more innovative and effective
problem that can be resolved progressively. ways of operating to gain market share. They
To start with, we can optimise the individual work hard to understand market dynamics,
parameters in each silo and then integrate the customer behaviour and their impact on internal
processes over the medium to long term (see activities, but their inability to identify and
Figure 4).
Progressive Optimisation Approach
Tackle more holistic Optimise at the
parameters in medium term organisation level in
Customer
Lifetime Index
the long term
Long
Term
Demographics Value Cost to Serve Loyalty Risk
Time scale
Customer Consumption Contact Cost Debt Call Centre Churn Account
Medium Segmentation Analysis Servicing Modelling Receivables
Term
Market/ Tariff Plan Online/ Agent
Offline Early/ Late NPS
Product Analytics Collections Efficiency Theft
Segmentation Efficiency
Short Cross Sell/ Contact Debt Agent
Term Up-sell Reduction Servicing Handling Time
Analytics
Contact
Efficiency
In short term, optimise
the individual point-based
parameters
Figure 4
cognizant 20-20 insights 3

4. Analytics’ Challenges
Organisation Process People Technology
Analytics is not seen as Structuring of analytics Lack of proficiency in Unavailability of data at
a lever for supporting function to optimise only quantitative methods granular levels.
corporate innovation. a single business area. applicable for utilities.
Analytics is not classified Focus on current and Unclear career pro- High cost of technology
as a distinct capability. future goals rather than gression and lack of for enterprise-wide
historical trends across mentorship. solution.
enterprise.
Unclear roles and respon- Insights from analytics More confidence on Over-reliance on
sibilities for modelling are tested only for experience and intuition technology as an
between IT and analytics. limited business areas. rather than facts. analytical solution.
Deployment of multiple Inability to select right Focus on meeting Inability to validate data
point solutions in data and in right format individual or business integrity and quality at an
isolation rather than for analysis. unit’s objectives rather enterprise level.
looking at the big picture. than working towards
a balanced scorecard
model.
Lack of single view of Focus on incorrect or Time to design an
customers and relating unnecessary metrics. enterprise-wide analytics
them to customer solution.
segments.
Not involved in planning Relating analytics to Complexity involved in
process of strategising KPIs of a business area integrating data from
for business units/propo- and not on multiple multiple sources.
sitions. aggregated levels.
Figure 5
correct inaccurate/inconsistent data typically Various Operating Models
creates misalignment between expectation and for the Analytics Function
results. Multiple data sources and disparate silos
As analytics emerges as a key ingredient for
of data often mean individuals or business units
organizational success, different variations of
are using different information than their coun-
operating models have emerged that can be
terparts, which generally results in misleading or
deployed depending on the supplier’s maturity
complicated messages for stakeholders. There
and business goals. The effectiveness of
is also an opportunity cost due to their inability
any of these models also depends on senior
to identify potential or existing customers who
management buy-in and application for tactical/
can be acquired or retained to maximise value,
strategic decision-making.
rather than targeting each and every one with
generic offers and gaining minimal conversion.
Key analytics challenges faced by suppliers are
• Distributed model: Different functional or
business units have separate groups that collect
summarised in Figure 5. and analyse data. This is the easiest model to
implement but it brings with it a very immature
As utilities move towards providing products
approach to analytics, especially where various
and services for smarter homes and businesses,
business units within the supplier’s organisa-
they are also making significant investments in
tion intersect with one another. For example,
new technologies that will streamline data and
a customer can be considered an existing or
processes. IDC’s “2011 Vertical IT & Communi-
potential residential, business and services
cations Survey” found that 86.7% of utilities
account, all at the same time.
worldwide had invested in analytics and over
one-third have been able to demonstrate positive • Offshore/On-site model: An on-site or cus-
business benefits.3 However, most organisations tomer facing team is used for data gathering,
are a long way away from achieving “analytical scoping, model creation and liaising with func-
maturity.” tional or business areas while offshore teams
cognizant 20-20 insights 4

5. generate reports based on these models and “social media analytics,” “predictive analytics,”
interpret outcomes for decision-making. “Web analytics,” “customer value analytics” and
“real-time decisioning,” which take the analytics
• Front-end/Back-end model: Responsibility for discipline to another level. With these techniques,
analytics and providing meaningful insights is
utilities can obtain more real-time, accurate
split between external facing and operational
and effective ways of delivering meaningful and
teams. Data related to customers, competi-
relevant insights and foresights that have the
tors, suppliers and industry are analysed by
potential to project/predict customer behaviour.
a front-end team for decision-making related
to sales, marketing, campaign management Due to the growing importance of collecting and
and customer experience. At the same time, analysing vast amount of data there is a logical
a back-end team works on data related to call shift from the distributed or individual functional
volumes, agent performance, cost and opera- area level analytics to a more enterprise-wide,
tional activities. corporate-level model. Suppliers can adopt a
• Centre of Excellence model: A corporate progressive approach to building analytics with
centre of excellence (CoE) supervises the enter- a view toward getting to a level where analytics
prise-wide collection of data and analysis. The can be provided as a service to various stakehold-
CoE helps individual business units with their ers in the organisation. From ad hoc analytics,
specific analytics requirements and provides suppliers can move into complete processes and
the latest and most relevant insights. Individual then to platform-based enterprise-wide function-
business units/functional areas are assigned ality (see Figure 6).
members from a central pool of resources for
providing analytics and business intelligence. Conclusion
These members can work on a project or Given shifting regulatory sands, the proliferation
business as usual (BAU) mode, depending on of smart metering and a greater green conscious-
the requirement. All resources report to the ness that is sweeping the business and consumer
central pool and can be redeployed in other worlds, UK utilities have reached a major shift
areas of business when necessary. Knowledge point.
management and communication between BUs
As such, holistically harnessing the power of
and the CoE is the key to success in this model.
enterprise analytics, across various silos and
Effective implementation and management of data functional areas, can enable them to reduce oper-
or information depends on the ability to collect, ational costs and achieve greater levels of opera-
analyse, interpret and act quickly and effectively. tional agility, while more effectively meeting new
Most organisations are not only working on data regulatory and market mandates, with minimal
from traditional sources, but embracing emerging operation disruption.
Taking Analytics to a Higher Plane
Analytical
Outsourcing
& Analytics-
Analytical Outsourcing &
as-a-Service Analytics-as-a-Service
Analytical Analytical Applications & Platforms
Applications Increasing
& Platforms Analytical
Maturity Joining, Leaving and Movement,
Analytics Maturity
In-process Meter, Billing & Consumption,
Business Payment & Collections
Analytics
Commercial, Risk & Fraud Management
Basic Customer Service
Ad Hoc Analytics
Analytics Services
Ad Hoc Analytics
Energy
Analytics
Time
Figure 6
cognizant 20-20 insights 5

6. Energy suppliers that attempt to leverage establish analytics practices will determine which
analytics without consistent and accurate companies achieve fact-based advantage in a
information will struggle to compete and miss fast-changing and ultra-competitive environ-
emerging business opportunities. The speed ment. Inaction will only widen the gap between
with which supplier organisations adopt and 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.
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