Why UK Utility Suppliers Can Get Smarter with Advanced Analytics

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1 Cognizant Insights Why UK Utility Suppliers Can Get Smarter with Advanced 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 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 cognizant insights february 2012

2 From Data to Insights Sales Channel Campaigns Revenue Cross-sell Up-sell s Lifetime Value Cost to Churn Segment Loyalty Serve Experience Products Pricing Margin Competition Portfolio Operations Capacity Planning Forecasting Leakage Effectiveness Performance Vendor Optimisation 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 Holistically 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 Action Strategy Business Initiatives, Tracking Enterprise Metrics, Balanced Scorecard, Strategy Maps Insight Information Data Advanced 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 insights 2

3 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). 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). 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). Holistic Approach to Figure 3 Risk Loyalty Demographics Lifetime Index Cost to Serve Challenges to Implementation Value 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 Progressive Optimisation Approach Long Term Tackle more holistic parameters in medium term Lifetime Index Demographics Value Cost to Serve Optimise at the organisation level in the long term Loyalty Risk Medium Term Time scale Segmentation Market/ Product Segmentation Consumption Analysis Tariff Plan Contact Cost Online/ Offline Debt Early/ Late Collections Call Centre Servicing Agent Churn Modelling NPS Account Receivables Theft Short Term Cross Sell/ Up-sell Contact Reduction Debt Servicing Agent Handling Time Contact In short term, optimise the individual point-based parameters Figure 4 cognizant insights 3

4 Challenges Organisation Process People Technology 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. 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. Various Operating Models for the 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 cognizant insights 4

5 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 to a Higher Plane Analytical Outsourcing & as-a-service Analytical Outsourcing & -as-a-service Maturity Analytical Applications & Platforms In-process Business Ad Hoc Basic Services Increasing Analytical Maturity Joining, Leaving and Movement, Meter, Billing & Consumption, Payment & Collections Commercial, Risk & Fraud Management Service Ad Hoc Analytical Applications & Platforms Figure 6 Energy Time cognizant insights 5

6 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 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 or follow us on Twitter: Cognizant. World Headquarters 500 Frank W. Burr Blvd. Teaneck, NJ USA Phone: Fax: Toll Free: inquiry@cognizant.com European Headquarters 1 Kingdom Street Paddington Central London W2 6BD Phone: +44 (0) Fax: +44 (0) infouk@cognizant.com India Operations Headquarters #5/535, Old Mahabalipuram Road Okkiyam Pettai, Thoraipakkam Chennai, India Phone: +91 (0) Fax: +91 (0) inquiryindia@cognizant.com Copyright 2012, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein is subject to change without notice. All other trademarks mentioned herein are the property of their respective owners.