Big Data Strategies

Creating Customer Value In Utilities

Valery Peykov

Country CIO Bulgaria

Veolia Environnement

National Conference ICT For Energy And Utilities Sofia, October 2013

17.10.2013 г.

One Core Business: Environmental Solutions

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No. 2 worldwide

No. 1 worldwide

4 Divisions

No. 1 in Europe 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; 96 000 employees worldwide

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

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

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.

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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

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.

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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

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.

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Source: Google trends

In Bulgaria

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Source: Google trends

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

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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.

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Google search results for the term “Big Data”

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Data from Google Trends

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.

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2 Applying Big Data Strategies

To Improve Customer Management

And Reduce Operational Costs

Processes, Technologies and Science Techniques

to Manage Customer Performance

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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.

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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, time-variant and non-volatile collection of data in support of management's decision making process.

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Information Needs at Operational, Tactical and Strategic Level

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Customer Performance Management

360° Customer

View

Insight

Action

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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.

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Customer Debt Management Strategy - part of the Customer Performance Management Cycle

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.

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2 Sofia Water Business Intelligence -

Data Warehouse Project

Turning Data Into Information

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Sofia Water Business Intelligence / DWH Initiative

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ERP SYSTEM CC&Billing

GIS SCADA SYSTEMS

BusinessIntelligence

&Sofia Water

DWH

Water&Sewage

connections

Finance data

DevicesObjects

DATA LOGERSDevicesObjects

REVENUEVAT

Billing data

OPERATIONAL

&

MANAGEMENT

REPORTS

DYNAMIC

DASHBOARDS

EXECUTIVE

MANAGEMENT

&

REGULATOR

REPORTS

STRUCTURED DATA

STRUCTUREDDATA

STRUCTUREDDATA

BUSINESS

ANALYSES

CUSTOMER

ANALYTICS

STRUCTURED

DATA

Budgeting, Financials, Management accounting, Procurement,

Supply Chain, Warehouse Management, Projects, Operation

Management, Assets Management

ClientsAddresses

Assets&Operational

management

STATISTICS &

DATA MINING

ANALYSIS

STRUCTURED

DATA

· Call Center Interactions

· Web Site Statistics

· Customer Service

Centers

· Payment Chanels

Transactions

Customer Interactions

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Network Management Analysis in Business Intelligence Solution

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Customers Debt Aging & Pareto Analysis

Payment Channels Analysis

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Usually companies react to decreased collection rates and search “post factum” improvement measures

Collection methods are limited: phone calls, letters, site visits, court actions, disconnection

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)

Prevent accumulation of the debt, by taking actions before loyal customers turn into debtors

refine customer segmentation, through big data analysis

using the right measure to the right type of situation obtain maximum results from limited methods

identifying causes which make customers stop paying the bills

Customer Debt Management Strategy - current issues and proposed approach

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Customers Risk Analysis & Segmentation

Implementing Real Time Customer Behavior & Performance Analytics.

Customer Risk Management; Customer Debt Internal Controls Monitoring

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Debt Collection Campaigns Initialization based on customer behavior analytics

Customer Reactions Sensitive Analysis based on Customer Analytics

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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.

Campaign Management System - In House Solution

Business Intelligence/

DWH INTERACTIONS

FINANCIAL STATUS

TRANSACTIONS

CHARACTERISTICS

ECONOMICAL & ENVIRONMENTAL

FACTORS

OPERATIONS

Customer

Campaign Management

System

Analysis

Optimization

Data Mining

Statistics

Artificial Intellect

Machine Learning

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When Science Meets Business

Turning Since Into Customer Value

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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“.

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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.

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.

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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.

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.

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Primary Data Analysis (descriptive statistics) on Debtors’ Data

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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 90 90 0.60012 0.6001 0.60012 0.6001

0,000000<x<=5000,000 14906 14996 99.39321 99.9933 99.39321 99.9933

5000,000<x<=10000,00 0 14996 0.00000 99.9933 0.00000 99.9933

10000,00<x<=15000,00 0 14996 0.00000 99.9933 0.00000 99.9933

15000,00<x<=20000,00 0 14996 0.00000 99.9933 0.00000 99.9933

20000,00<x<=25000,00 1 14997 0.00667 100.0000 0.00667 100.0000

Missing 0 14997 0.00000

0.00000 100.0000

Statistical Correlation Analysis on Customer, Operational and Performance Data

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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.

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Hidden clusters of customers

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

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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.

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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.

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Customer Risk Management

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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

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Thank you for your attention