Energy Utilities Data Explosion: Load Analytics and Customer Segmentation

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Melvin Arnold
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1 Energy Utilities Data Explosion: Load Analytics and Customer Segmentation Prasenjit Shil Sr. Forecasting and Load Research Specialist Ameren Tom Anderson Principal Systems Engineer SAS #AnalyticsX

2 Load research has arrived at an interesting juncture with increasing quest for understanding customer needs and their (usage) behavior. Traditional utilities are recruiting more marketing and big data analytics professional to help them in this process. Armed with AMI and/or hourly sample data and demographics data, load analytics personnel, traditionally known as Load Researchers, can play a crucial role in various analytical projects within their respective utility. Ameren is no different. This presentation will highlight how Ameren s Load research and Forecasting group has helped energy efficiency and marketing groups identify customer segments with highest propensity for various product marketing. Segmentation approach and methodology will be discussed with a few specific examples. We will also discuss how load researchers are being utilized on various strategic analytical projects at Ameren. Finally, this presentation would like to take the opportunity to discuss the how Ameren is transforming its analytical culture. The heart of this transformation lies on the readiness and abilities of its people and the analytic tools they use. Like other utilities, Ameren too is experiencing explosion of data and eager to take advantage of those data and analytics for better customer engagement and to improve its profitability. We will discuss how the Ameren has been changing its analytical horizon for last three years, tools used and most importantly, lessons learnt in this transformation process. This presentation intends to engage the participants in a group discussion to understand how they are creating and/or enhancing analytical landscape in their respective companies.

3 Disclaimer Some of the information presented are proprietary to Ameren and cannot be presented without prior permission from Ameren. This presentation may also reflect personal views, opinions and insights that do not necessarily reflect those of Ameren and its subsidiaries.

4 Highlight Part 1: Harvesting Load Data for Customer Insight This presentation will highlight how utilities are using multitude of data to enhance customer engagement, create new products and identify target customers for those products Examples from Ameren s customer segmentation work #analyticsx Part 2: Building Analytical Capability within Utilities Discuss Ameren s strategy to transform its analytical culture. Discuss the readiness and abilities of its people and the analytic tools they use. Lessons learnt in this transformation process. Engage the participants in a group discussion to understand how they are creating and/or enhancing analytical landscape in their respective companies.

Ameren at a Glance #analyticsx A diversified regional electric and gas utility 2.

capitalization Component of S&P 500 Rate-regulated Operations Ameren Missouri Generation, transmission and deliv ery business

2 million electric and 127,000 gas customers 10,300 MW of total generation capacity 6,600 MW baseload coal-fired and nuclear

5 Ameren at a Glance #analyticsx A diversified regional electric and gas utility 2.4 million electric and 933,000 gas customers ~10,500 MW total electric generation capacity ~$12 billion equity market capitalization Component of S&P 500 Rate-regulated Operations Ameren Missouri Generation, transmission and deliv ery business Serv es 1.2 million electric and 127,000 gas customers 10,300 MW of total generation capacity 6,600 MW baseload coal-fired and nuclear fleet Ameren Illinois Transmission and deliv ery business Serv es 1.2 million electric and 806,000 gas customers Ameren Transmission Co. FERC-regulated Three MISO-approv ed multi-v alue projects totaling >$1.3 billion (through 2019) Additional projects totaling ~$1.0 billion ( ) at Ameren Illinois

6 Fast Forward to 5-10 years ago

7 Six Essential Elements to Optimize Customer Engagement Right Time Customer Insight Right Customer Customer Segmentation Right Place Omni-Channel Optimization Execute single view of the customer Develop integrated customer insight engine Deploy spectrum of approaches to segmentation Understand how the customer is using the product we are selling Adopt agile and in context real-time profiling Deliver seamless cross channel experience; merge digital/physical data analysis and deployment To be the Trusted Energy Advisor But, HOW? Customer awareness #analyticsx Right Service Customer Service Right every time Contextual Understanding Architect customer experience Build an experience that connects emotionally Providing the customer with the right offer, right time, right place, in real time if necessary 2015 Ameren Smart grid and Internet of things have changed how utilities view its customers

360 Degree View of Customer: Aspirations to Understand Each Customer as an Individual Armed with new analytics, we can

Load researchers are able to help in determining WHATs Characteristics Needs Demographics Call Center s Custome r Serv ice

Usage history Purchase stage Social Media Search Descriptive analytics Predictive analytics Prescriptive analytics Desires

8 360 Degree View of Customer: Aspirations to Understand Each Customer as an Individual Armed with new analytics, we can better understand WHAT customer wants and decide HOW to deliver them. Load researchers are able to help in determining WHATs Characteristics Needs Demographics Call Center s Custome r Serv ice Attributes Ev ents Direct Mail Transactions Kiosks Orders Instore POS Meter data Website Payment history Transaction data Usage history Purchase stage Social Media Search Descriptive analytics Predictive analytics Prescriptive analytics Desires Preferences SMS/ MMS s Mobile Online Advertising Call center notes / Chat Behavioral data Opinions In-person dialogs Web click-streams Interaction data 2015 Ameren Confidential

Taking Customer Data to the Next level How we integrate it and how we use it Customer Data Analytics Building Blocks Derived Business Value #analyticsx Understand Who, What and How Single go-to group

schedule, design, conduct and assess the effectiveness of new initiatives, projects and campaigns before they are rolled out on a system-wide basis Measure uptake and adjust models to increase

9 Taking Customer Data to the Next level How we integrate it and how we use it Customer Data Analytics Building Blocks Derived Business Value #analyticsx Understand Who, What and How Single go-to group with requisite skillset and resources to provide analytics for ongoing and ad hoc research, marketing and communications Single set of documented goals aligned with business objectives Ability to schedule, design, conduct and assess the effectiveness of new initiatives, projects and campaigns before they are rolled out on a system-wide basis Measure uptake and adjust models to increase performance 360 View of Customer Campaign Management Enhanced Segmentation Regulatory Knowledge Business Reporting Predictive Analytics Enables smarter decisions through superior customer analytics Brings offline and online data sources together Enables marketing and communications to do more with data and less money Less reliance on IT department to perform data extracts Helps in creating new products and services for the utility Helps provide a Growth Path to Evolve Enhanced customer intelligence is key to the successful execution of strategies 2015 Ameren

10 Segmentation Example: HVAC

Ameren has large amounts of available or accessible data it can use towards generating

g., address, billing options used, energy usage, premise start date, online account

g., awareness of Ameren EE program, websites visited, social media usage, response to

attitude towards IVR, attitude towards ebill Ameren EE Potential Study e.g.

likelihood of program participation Large amounts of data is available and/or

11 Ameren has large amounts of available or accessible data it can use towards generating insights to improve performance Select sources and type of data Ameren customer data e.g., address, billing options used, energy usage, premise start date, online account Program participation data e.g., participation in refrigeration recycling program Data from CSAT surveys e.g., awareness of Ameren EE program, websites visited, social media usage, response to advertising, attitude towards IVR, attitude towards ebill Ameren EE Potential Study e.g., household size, electric technology installed, high-level purchase data, state likelihood of program participation Large amounts of data is available and/or accessible, insight generation is key to unlocking value Socio-economic data e.g., age, people in household, income, education, work Real-estate data e.g., home values, price trends

12 A practical HVAC segmentation approach relies on prior program participation and other data to create segments #analyticsx

13 Identification of segments was done using Improved CHAID algorithm on internal and public data

14 Part 2: Building Analytical Capability within Utilities #analyticsx Where Do We Go From Here? Draft: Please do not publish without prior consent

15 Learning from Ameren s Journey: Time for a Shift. #analyticsx Pockets of analytical experience, but no cohort approach Ameren formed its first Data analytics Strategy Committee in 2012 Discovered pain points from all the analytics groups Lack of data integration and single points of visibility Significant time and effort required across the organization to access, understand, aggregate, and validate data Constrained capacity (Resources and Data) Big Hurdle 80% 76% 76% 75% 73% Finding talents for projects Gathering data from multiple sources Finding right tools Time to work on the project Understanding platforms Information is a vital company asset Source: WSJ

Ameren s Business Analytics Vision To provide access and visibility into trusted business information to support critical decision making in a timely fashion.

16 Ameren s Business Analytics Vision To provide access and visibility into trusted business information to support critical decision making in a timely fashion. #analyticsx Service Business Analytics Information Delivery Trusted Data Data Management for Big Data Enabler Business Analytics & Business Intelligence Fully Integrated Business View of Information Consistent Data that is Timely, Accurate, Trusted, Secured, and Managed Secure and Scalable Platforms for Enterprise Data Management of Big Data Governance People Technology Business Outcome Predictive Decision Making Capabilities with Greater Business Insight Self-Service Consumption of Shared Business Information Trusted Business Data Accessible in Near Real- Time High Performance Business Analytics Processing

17 Transforming an Analytics Culture From Then to Now Enabling People, Process and Technology Understand People, Acknowledge Process and Use Technology Roadmap Increasing Analytic Maturity

Then SAS Only for Programmers Now SAS is for

(and Programmers :) proc catmod order=data;

erity=trt hospital; run; quit; /* Keep just the

data pred2; set preds; if _ty pe_='prob'; keep

predicted response level (level with highest

*/ procsummary data=pred2 nway; class trt

Transpose the predicted values so that there is

18 Then SAS Only for Programmers Now SAS is for Business! (and Programmers :) proc catmod order=data; weight wt; response / out=preds; model sev erity=trt hospital; run; quit; /* Keep just the predicted values, predictors, and response */ data pred2; set preds; if _ty pe_='prob'; keep sev erity trt hospital _pred_; run; /* Find predicted response level (level with highest predicted probability ) in each sample. */ procsummary data=pred2 nway; class trt hospital; v ar _pred_; output out=predlvl (drop=_type freq_) maxid(_pred_(severity))=predlvl; run; /* Transpose the predicted values so that there is one observation per sample containing predicted values for each response level. */ proctranspose data=pred2 out=pred3 (drop=_name_); by trt hospital; id sev erity; v ar _pred_; run;

19 Now Sas Analytical Enablement Enable Enable Tools Meta Tools Solutions

Now Solutions SAS ANALYTICAL ENABLEMENT SAS Asset Performance Analytics SAS BookRunner SAS Energy Forecasting SAS Customer Intelligence MetaTools

20 Now Solutions SAS ANALYTICAL ENABLEMENT SAS Asset Performance Analytics SAS BookRunner SAS Energy Forecasting SAS Customer Intelligence MetaTools SAS Enterprise Guide SAS Enterprise Miner SAS Forecast Server SAS Visual Analytics/Visual Statistics Tools Base SAS SAS/STAT SAS/ETS SAS/OR SAS/IML

21 SAS Visual Analytics SAS Visual Statistics SAS In-Memory Statistics for Hadoop / SAS Studio SAS Enterprise Miner / SAS Text Miner / SAS Contextual Analysis / SAS Forecast Studio SAS Model Manager / SAS Scoring Accelerator METADATA

22 PEOPLE PROCESSES TECHNOLOGY DATA MINING & STATISTICS MODEL MANAGEMENT TEXT ANALYTICS FORECASTING & OPTIMISATION

Understand Business Sponsor Domain Expert

Processes and ROI Data and Architecture SAS

Hadoop/Database developer Enterprise metadata

Visualization Analytic Analysis Report Creation

23 Understand Business Sponsor Domain Expert Formulate Strategy/Makes Decisions Evaluates Processes and ROI Data and Architecture SAS administrators Hadoop/DBA Security administrators Hadoop/Database developer Enterprise metadata personnel ETL personnel Enterprise architects Training Skills Experience DRIVE!!! Business Analysts Data Exploration Data Visualization Analytic Analysis Report Creation Report Consumer Data Scientist/ Statistician Exploratory Analysis Descriptive Segmentation Predictive Modeling

24 Acknowledge PROCESSES

Use TECHNOLOGY It is about creating a flexible infrastructure with highperformance computing, highperformance analytics and governance in a deployment model that makes sense for

25 Use TECHNOLOGY It is about creating a flexible infrastructure with highperformance computing, highperformance analytics and governance in a deployment model that makes sense for the organization. Move data preparation and analytical processing to the actual data source, taking advantage of the massive parallel processing (MPP) capabilities in some databases

Roadmap Customer Segmentation (consumption based) Customized dynamic pricing models Customer Fraud Customer Theft Credit and Collections Smart Grid Revenue Leakage Gas Predictive Enterprise Targeted

26 Roadmap Customer Segmentation (consumption based) Customized dynamic pricing models Customer Fraud Customer Theft Credit and Collections Smart Grid Revenue Leakage Gas Predictive Enterprise Targeted Energy Efficiency Programs Heat Rate reduction Models Generation Call Volume prediction models Dow n Time Anomalies Model Staffing Models Customer Service Customer Complaint Predictive Models IVR Pattern Recognition Models Credit Risk Scoring Model Week-Ahead Disconnect Forecast Model Optimization models Other Supply Chain Large Customer Billing Peak day Model Electric T&D Equipment Failure Prediction Model Predictive Asset Maintenance Model Vehicle Maintenance Models Retention Model, Acquire / Attract Model Conversion Model Gas Leak / Equipment Failure Model Peak Demand Forecasting Market Research

27 Analytics Maturity Model Proactive Reactive Source: Competing on Analytics Thomas Davenport and Jeanne Harris

28 Analytics Maturity Model Stage 1: Analytically Impaired The company has some data and management interest in analytics. Stage 2: Localised Analytics Functional management builds analytics momentum and executives interest through applications of basic analytics. Stage 3: Analytical Aspirations Executives commit to analytics by aligning resources and setting a timetable to build a broad analytical capability. Stage 4: Analytical Company Enterprise-wide analytics capability under development; top executives view analytic capability as a corporate priority. Stage 5: Analytical Competitor The company routinely reaps benefits of its enterprise-wide analytics capability and focuses on continuous analytics review. Source: Davenport, Thomas H., and Jeanne G. Harris Competing on Analytics: The New Science of Winning. Boston: Harvard Business School Press.

29 Analytics Maturity Considerations 1) Good analytics always begins with quality, pre-staged data 2) Improving analytics maturity involves people, process, and technology considerations and re-engineering, not just having better analytic modeling capabilities 3) There are always things that can be done to improve analytics usage, regardless of where you are maturity-wise 4) Automating data preparation is fundamental to making analytics professionals more productive

30 #AnalyticsX

Louis Bodine IBM STG WW BAO Tiger Team Leader

Louis Bodine IBM STG WW BAO Tiger Team Leader Presentation Objectives Discuss the value of Business Analytics Discuss BAO Ecosystem Discuss Transformational Solutions http://www.youtube.com/watch?v=eiuick5oqdm