Energy Utilities’ Data Explosion - SAS · Energy Utilities’ Data Explosion: Load Analytics and Customer Segmentation ... Segmentation approach and methodology will be discussed

#AnalyticsXC o p y r ig ht © 201 6, SAS In st i tute In c. A l l r ig hts r ese rve d.

Energy Utilities’ Data Explosion: Load Analytics and Customer Segmentation

Prasenjit ShilSr. Forecasting and Load Research SpecialistAmeren

Tom AndersonPrincipal Systems EngineerSAS

#analyticsx

C o p y r ig ht © 201 6, SAS In st i tute In c. A l l r ig hts r ese rve d.

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.

#analyticsx


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.

#analyticsx


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

• 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.

#analyticsx


• 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 Missouri 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

(2013-2017) at Ameren Illinois

Rate-regulated Operations

Ameren at a Glance

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

#analyticsx


Fast Forward to 5-10 years ago

#analyticsx


Six Essential Elements to Optimize Customer Engagement 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

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

Right Service

Customer

Service

Right PlaceOmni-Channel

Optimization

Right TimeCustomer

Insight

Right CustomerCustomer

Segmentation

Right every timeContextual

Understanding© 2015 Ameren

Customer awareness

To be the

Trusted

Energy

Advisor

But, HOW?

Smart grid and Internet of things

have changed how utilities view

its customers

#analyticsx


360 Degree View of Customer: Aspirations to Understand Each Customer as an Individual

Load researchers are able

to help in determining

WHATs

Armed with new analytics, we

can better understand WHAT

customer wants and decide

HOW to deliver them.

Transaction

data

Interaction

data

Behavioral

data

In-

store

POS

Meter

data

Website

Search

Online Advertising

MobileEmails

SMS/

MMS

Social

Media

Custome

r Serv ice

Call Center

s

Ev ents

Direct

MailKiosks

Transactions

Orders

Payment

history

Usage

history

Purchase

stage

E-mail / Chat

Call center

notes

Web

click-streamsIn-person

dialogs

Opinions

Preferences

Desires

Needs

Characteristics

Demographics

Attributes

Descriptive analytics

Predictive analytics

Prescriptive analytics

© 2015 Ameren Confidential

#analyticsx


Enhanced customer intelligence is key to the successful execution of strategies

Derived

Business Value

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

Customer Data

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

Taking Customer Data to the Next levelHow we integrate it and how we use it

Campaign Management

Business Reporting

Regulatory Knowledge

Enhanced Segmentation

Building Blocks

360°View of

Customer

© 2015 Ameren

Predictive Analytics

#analyticsx


Segmentation Example: HVAC

#analyticsx


Ameren has large amounts of available or accessible data it can use towards generating insights to improve performance

Large amounts

of data is avail-able and/or accessible, insight gene-

ration is key to unlocking value

Select sources and type of data

▪ 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

▪ Socio-economic data – e.g., age, people in

household, income, education, work

▪ Ameren customer data – e.g., address, billing

options used, energy usage, premise start date, online account

▪ Real-estate data – e.g., home values, price trends

#analyticsx


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

#analyticsx


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

#analyticsx


Where Do We Go From Here?

Draft: Please do not publish without prior consent

Part 2: Building Analytical Capability within Utilities

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&docid=e1oVcvPsYfT9MM&tbnid=cMVulVRCDW1GfM:&ved=0CAUQjRw&url=http://www.branded3.com/blogs/6-solid-social-media-predictions-2014/&ei=ScbPU9L7CcqlyAT1goDwAQ&bvm=bv.71667212,d.aWw&psig=AFQjCNEPv9ec4Ig3kNOwn0J0UazDci5Xow&ust=1406211894603778

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&docid=e1oVcvPsYfT9MM&tbnid=cMVulVRCDW1GfM:&ved=0CAUQjRw&url=http://www.branded3.com/blogs/6-solid-social-media-predictions-2014/&ei=ScbPU9L7CcqlyAT1goDwAQ&bvm=bv.71667212,d.aWw&psig=AFQjCNEPv9ec4Ig3kNOwn0J0UazDci5Xow&ust=1406211894603778

#analyticsx


Learning from Ameren’s Journey: Time for a Shift.

• 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)

Information is a vital company asset

80%

Finding talents for projects

76%

Gathering data from multiple sources

76%

Finding right tools

75%

Time to work on

the project

73%

Understanding platforms

Big Hurdle

Source: WSJ

#analyticsx


Ameren’s Business Analytics Vision“To provide access and visibility into trusted business information to support critical

decision making in a timely fashion.”

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

Business OutcomeService

Data Management for “Big Data”

Trusted Data

Business Analytics

Information Delivery

Enabler

Business Analytics & Business Intelligence

Consistent Data that is Timely, Accurate, Trusted, Secured, and Managed

Secure and Scalable Platforms for Enterprise Data Management of Big Data

Fully Integrated Business View of Information

Governance

People

Technology


#analyticsx


From Then to Now

Enabling People, Process and Technology

Understand People, Acknowledge Process and Use Technology

Roadmap

Increasing Analytic Maturity

Transforming an Analytics Culture

#analyticsx


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. */

proc summary 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. */

proc transpose data=pred2 out=pred3 (drop=_name_);

by trt hospital;

id sev erity;

v ar _pred_;

run;

Then – SAS Only for Programmers Now – SAS is for Business! (and Programmers :)

#analyticsx


• Sas Analytical Enablement

Tools

Meta

Tools Solutions

Enable Enable

Now

#analyticsx


Tools

MetaTools

Solutions

Base SAS SAS/STAT SAS/ETS

SAS/OR SAS/IML

SAS Enterprise Guide

SAS Enterprise Miner

SAS Forecast Server

SAS Visual Analytics/Visual Statistics

SAS Asset Performance Analytics

SAS BookRunner

SAS Energy Forecasting

SAS Customer Intelligence

NowSAS ANALYTICAL ENABLEMENT

#analyticsx


METADATA

SAS Visual Analytics SAS Visual Statistics

SAS In-Memory

Statisticsfor Hadoop / SAS

Studio

SAS Enterprise

Miner / SAS Text Miner / SAS

Contextual Analysis /

SAS Forecast Studio

SAS Model Manager

/ SAS Scoring Accelerator

#analyticsx


DATA MINING& STATISTICS

FORECASTING & OPTIMISATION

TEXT ANALYTICS

MODEL MANAGEMENT

PROCESSES TECHNOLOGYPEOPLE

#analyticsx


UnderstandData Exploration

Data VisualizationAnalytic Analysis

Report CreationReport Consumer

Exploratory Analysis

Descriptive SegmentationPredictive Modeling

Data Scientist/

Statistician

Business

AnalystsDomain Expert

Formulate Strategy/Makes Decisions

Evaluates Processes and ROI

Business

Sponsor

SAS administrators

Hadoop/DBASecurity administrators

Hadoop/Database developerEnterprise metadata personnel

ETL personnelEnterprise architects

Data and

Architecture

TrainingSkills

ExperienceDRIVE!!!

#analyticsx


AcknowledgePROCESSES

#analyticsx


UseTECHNOLOGY

• It is about creating a flexible infrastructure with high-performance computing, high-performance 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

#analyticsx


RoadmapPredictive

Enterprise

Credit and

Collections

• Conversion Model

• Gas Leak / Equipment Failure

Model

• Peak Demand Forecasting

• Market Research

Gas

Revenue Leakage• Customer Fraud

• Customer Theft

Smart Grid

• Customer Segmentation

(consumption based)

• Customized dynamic pricing

models

• Targeted Energy Eff iciency

Programs • Heat Rate reduction Models

• Dow n Time Anomalies Model

• Equipment Failure Prediction Model

• Predictive Asset Maintenance Model

• Vehicle Maintenance Models

Electric

T&D

Customer

Service

• Call Volume prediction models

• Staff ing Models

• Customer Complaint Predictive

Models

• IVR Pattern Recognition Models

Generation

• Optimization

modelsSupply

Chain

• Credit Risk Scoring Model

• Week-Ahead Disconnect

Forecast Model

• Retention Model, Acquire /

Attract Model

Other

• Large Customer Billing

• Peak day Model

#analyticsx


Source: Competing on AnalyticsThomas Davenport and Jeanne Harris

Proactive

Reactive

Analytics Maturity Model

#analyticsx


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. 2007.

Competing on Analytics: The New Science of Winning. Boston: Harvard Business School Press.

Analytics Maturity Model

#analyticsx


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

Analytics Maturity Considerations


#AnalyticsX

Documents

Energy Utilities’ Data Explosion - SAS · Energy Utilities’ Data Explosion: Load Analytics and Customer Segmentation ... Segmentation approach and methodology will be discussed