Big Data Strategies Creating Customer Value In Utilities

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1 Big Data Strategies Creating Customer Value In Utilities National Conference ICT For Energy And Utilities Sofia, October 2013 Valery Peykov Country CIO Bulgaria Veolia Environnement г.

2 One Core Business: Environmental Solutions 4 Divisions No. 1 worldwide No. 1 in Europe No. 2 worldwide No. 1 in Europe N 1 provider of water utility services to municipalities in the world 158 years experience (activities started in 1853) 67 countries; employees worldwide 2

3 Summary Shaping-up utility services of tomorrow from current challenges to future opportunities Big data: the next frontier for innovation, productivity and competition Applying Big Data strategies to improve customer management and reduce operational costs Where science meets business 3

4 1 Shaping-up Utility Services of Tomorrow From Current Challenges To Future Opportunities

5 Environmental Efficiency vs. Financial Performance Utility companies deal with natural resources threatened by scarcity and long term environmental strategies aim to rationalize the use of these resources (water, gas and energy sources). Decreasing trend of end-users consumption, due to: the wish to reduce expenses related to utilities; more efficient management of internal networks; use of devices with reduced consumption (cost reduction and environmental awareness). Utility companies need to manage more efficiently the raw sources they use, before delivering the service to the final customer and reduce the losses - network management performance. 5

6 From Captive Customers to Increased Awareness and Power of Choice Customers of utility companies have become more aware of their rights and their level of expectations is constantly increasing. The monopolies are no longer an indestructible myth in utilities EU legislation on deregulation of the market increasing competition and allowing customers to choose the supplier Alternative supply solutions between utilities can make companies lose their customers: e.g. electrical or gas heating instead of centralized heating Performance of customer management is a differentiation factor between utility companies Utility companies must therefore discover who the customers are, what their specific needs are and how to address them. 6

7 Customer Trust in Utility Companies Frequently, when customers hear from a utility company is when is sending a bill, a disconnection warning, or notice of a rate increase. It is no surprise then that in an age of increasing customer importance, trust in utility companies is the lowest level it has been in years. Source: Google trends 7

8 In Bulgaria Source: Google trends 8

9 2 2 Big Data: The Next Frontier For Innovation, Productivity And Competition

10 Big Data The amount of data in our world has been exploding. Utility companies already capture trillions of bytes of information about their customers and operations, and millions of networked sensors and devices are being embedded in the physical world in devices sensing, creating, and communicating data. Big data is often described as data sets so large and complex that it becomes difficult to manage and analyze them with the traditional data processing tools. The problem how this tidal wave of information can be captured, communicated, aggregated, stored, and finally analyzed to create value is now part of every sector and function of the global economy. We create more data in a day then we did from the dawn of man through 2003 and approximately 90% of all the world's data has been created in the past 2 years. 10

11 Google search results for the term Big Data Data from Google Trends 11

12 Becoming a Data-Driven Organization To cope with the changing nature of information, organizations must transition from an application-driven focus to a data-driven approach. Innovative, data-centric companies view information as an asset as valuable as buildings, employees, production equipment and intellectual capital. It should be considered the value of the information as a corporate asset. Evolving technologies in the utilities industry, including smart meters, smart grids, loggers can provide companies with unprecedented data volume, speed and complexity. To manage and use this information to gain insight, utility companies should turn to Big data strategies involving technologies capable of high-volume data management, advanced analytics and scientific methods applied to the business. One of the components of the Big Data movement is the emerging role of the data intelligence and since. 12

13 3 2 Applying Big Data Strategies To Improve Customer Management And Reduce Operational Costs Processes, Technologies and Science Techniques to Manage Customer Performance

14 Technology Architecture Turing Data Into Information - In order to support Big Data initiatives hardware and software solutions are needed to capture, store, process, analyze and report on large volume of business data to support the decision making at operational, tactical and strategic level. The so called Decision Support Systems - Business Intelligence and Data Warehouse Systems. Business Intelligence according to Wikipedia is the ability for an organization to take all its capabilities and convert them into knowledge, ultimately getting the right information to the right people, at the right time, via the right channel. 14

15 Technology Architecture Data Warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process. According to: Ralph Kimball: Data Warehouse is a copy of transaction data specifically structured for query and analysis. Bill Inmon: Data Warehouse is a subject-oriented, integrated, timevariant and non-volatile collection of data in support of management's decision making process. 15

16 Information Needs at Operational, Tactical and Strategic Level 16

17 4 3 Customer Performance Management Action Insight 360 Customer View

18 Customer Debt Management Strategy - part of the Customer Performance Management Cycle Customer Performance is a measurable monetary or non-monetary result of all customer relationships in a defined period. Customer Performance Management: is a CRM function comprising all customer performance indicators, instruments, processes, software tools and systems to analyze and control Customer Performance. To optimize customer performance we need deep customer insight based on technologies capable of high-volume data management, advanced customer analytics and applied scientific methods. The developed by Veolia Bulgaria team Customer Performance Management Cycle includes also Customers Behavior Analysis, Profiling, Scoring & Segmentation, Automated Risk Management, Customer Debt Management, Consumption Predictive Models etc. Customer Strategies and Campaigns implemented by in house developed information systems. 18

19 360 Customer View To optimize the customer performance first we need 360 View on our customers. Customer Data Warehouse has been build in Sofia Water Company which daily process hundreds of millions of customer records. Business Intelligence solution supports decision makers to get customer and enterprise performance insight at tactical and strategic level with a single version of the truth. A project to integrate all corporate customer, financial and operational data is ongoing. The project is supported entirely by internal IT resources and centers of competence. 19

20 5 2 Sofia Water Business Intelligence - Data Warehouse Project Turning Data Into Information

21 Sofia Water Business Intelligence / DWH Initiative OPERATIONAL & MANAGEMENT REPORTS DYNAMIC DASHBOARDS EXECUTIVE MANAGEMENT & REGULATOR REPORTS BUSINESS ANALYSES CUSTOMER ANALYTICS STATISTICS & DATA MINING ANALYSIS STRUCTURED DATA STRUCTURED DATA Finance data STRUCTURED DATA Business Intelligence & Sofia Water DWH Customer Interactions STRUCTURED DATA Call Center Interactions Web Site Statistics Customer Service Centers Payment Chanels Transactions STRUCTURED DATA Billing data Water& Sewage connections GIS Devices Objects SCADA SYSTEMS Assets& Operational management Clients Addresses Devices Objects DATA LOGERS ERP SYSTEM CC&Billing Budgeting, Financials, Management accounting, Procurement, Supply Chain, Warehouse Management, Projects, Operation Management, Assets Management REVENUE VAT 21

22 Network Management Analysis in Business Intelligence Solution 22

23 Payment Channels Analysis Customers Debt Aging & Pareto Analysis 23

24 Customer Debt Management Strategy - current issues and proposed approach Usually companies react to decreased collection rates and search post factum improvement measures Prevent accumulation of the debt, by taking actions before loyal customers turn into debtors refine customer segmentation, through big data analysis Collection methods are limited: phone calls, letters, site visits, court actions, disconnection using the right measure to the right type of situation obtain maximum results from limited methods Companies usually deal with the effect of increased debt, without considering the real causes, at the very individual level (e.g.: discontent about the service) identifying causes which make customers stop paying the bills 24

25 Customers Risk Analysis & Segmentation Implementing Real Time Customer Behavior & Performance Analytics. Customer Risk Management; Customer Debt Internal Controls Monitoring 25

26 Debt Collection Campaigns Initialization based on customer behavior analytics Customer Reactions Sensitive Analysis based on Customer Analytics 26

27 Campaign Management System - In House Solution Solution Built around best of the Veolia Bulgaria team experience, the in house Campaign Management System provides Customer Service teams with the tools to turn customer insight into customer strategies, actionable plans and performance controls. Integration The solution integrates with the Business Intelligence/ Customer Data Warehouse and based on the customer behavior and performance analytics, fine tuned with Data Mining techniques allows the executions of a number of customer campaigns through different channels, touch points, and interactions. 27

28 Optimization Customer INTERACTIONS Business Intelligence/ DWH Artificial Intellect Machine Learning Statistics OPERATIONS FINANCIAL STATUS Data Mining Campaign Management System ECONOMICAL & ENVIRONMENTAL FACTORS CHARACTERISTICS TRANSACTIONS Analysis

29 6 4 When Science Meets Business Turning Since Into Customer Value

30 Turning Information Into Knowledge a step beyond.. Adopting Data Mining, Statistics analytic techniques and Mathematical Models designed to explore large amounts of business data known as Big Data. Data Mining (also called knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Given today s explosion of Big Data, companies need more advanced methods for leveraging their data methods that don t rely solely on tribal knowledge, personal experience or best guesses. What s needed are new technologies and purpose-built solutions that reveal questions to answers no one even knew to ask. 30

31 What is Data Mining Data Mining is a multidisciplinary field Data Mining involves the use of sophisticated techniques for analyzing data to discover previously unknown, existing patterns and relationships in large databases. Data Mining techniques include statistical models, mathematical algorithms, methods for machine learning (algorithms that improve their performance automatically based on experience) and artificial intellect data analysis. Data Mining includes also prediction models. 31

32 Addressing Data Mining to Customer s Debt Being part of the Customer Performance Management, customers debt is analyzed in Sofia Water Company with data mining techniques. Correlations have been analyzed between customer management data, operational data, assets, internal teams and subcontractors' meter readings performance. Have been also analyzed the correlations, variables weight and influence of customer behavior and performance KPIs and interactions on the customer debt. Case Study: Sofia Water Company In the process of Data Mining research projects sometimes are done unexpected foundlings. By analyzing data across SW company It has been found strong correlation between a Revenue Meter Diameter and Customer Debt. Further investigation and internal interviews explored years ago not well finished by a meter reading subcontractor task, multiplied during many years and not noticed later, passed through the business processes and information systems and finally allowed a group of customers to generate a huge volume of customers debt. 32

33 Primary Data Analysis (descriptive statistics) on Debtors Data Frequency table: Average Billed Consumption (m3) (15 000_Random_Domestic-Sof_Debtors_Red) K-S d=,46345, p<,01 Count Cumulative - Count Percent - of Valid Cumul % - of Valid % of all - Cases Cumulative % - of All -5000,00<x<=0, ,000000<x<=5000, ,000<x<=10000, ,00<x<=15000, ,00<x<=20000, ,00<x<=25000, Missing

34 Statistical Correlation Analysis on Customer, Operational and Performance Data 34

35 Who Our Real Customers Are? Most companies segment customers based on their performance - financial value, debt, geographic, products and services classification. For the needs of the internal reporting, it is OK. But do the customers really behave and group themselves in alignment with our internal reporting? If we want to change the numbers, we need the answer.. To influence on customers and generate successful customer strategies, campaigns, actions and loyalty programs, we must understand and anticipate customer behavior in relationship with internal and external factors and get deep customer insight. We need to identify the factors influencing on customers behavior and performance - Consumption, Satisfaction and Customer Debt. 35

36 Hidden clusters of customers Appling advanced statistical analysis revealed hidden clusters of debtors based on unknown behavioral models. 36

37 Hidden Clusters of Debtors - follow up During the scientific research, have been identified the parameters describing every cluster. Their values will be programed into the BI in order to uncover who those customers are. What are their similarities and the factors influencing on their behavior in order to generate successful and customized campaigns to them. 37

38 Customer Risk Management Based on scientific researches and tests on real customer data have been identified the most proper advanced Data Mining techniques to automate the Customer Risk Management. The developed Data Mining risk classification models, optimize the percentage of correct classified risk customers with more than 60% compared with the conventional methods based only on human experience and logic.(based on the our tests and observations). The analysis applied during the customers risk management allows to be identified per customer the critical threshold after which the same turns into a permanent debtor. Those thresholds could be implemented into the BI solution as internal controls alerting Customer Service departments proactively to act and prevent the accumulation of new debt. 38

39 Analyzing Water Consumption Study of the correlation of the consumption data with the factors "average temperature" and "average monthly rainfall." Two-dimensional diagram of correlation fields for the analyzed variables 39

40 Thank you for your attention 40

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