chief analytics officer fall usa 2017 - john carter

Leveraging Big Data Analytics for Business Value

John Carter CAO Conference, October 2017 SVP, Analytics & Business Insight

Charles Schwab

Agenda

What is happening in industry

Schwab approach and journey

− Defining a big data analytics journey

− Delivering use cases that deliver value

− How Data Science is evolving analytics

− Key partnerships with data and technology organizations

Critical success factors

Chief Analytics Officer Conference, Oct 2017 - John Carter Presentation

Charles Schwab Chief Analytics Officer Conference, Oct 2017 - John Carter Presentation

Data, analytics & technology is exploding!

Weblogs

Speech/Text

Artificial Intelligence

Big Data

Python and R

Hadoop & Spark

Machine Learning

Social Data Feeds Real-Time Decisions

Pattern Recognition

Cognitive Computing

In-Memory DB

Charles Schwab

Executives are expecting much more from analytics…


Big Expectations for Big Data in 2016

Splice Machine

Big Data Predictions…Companies want to personalize

cross-channel experiences based on real time

information and not day old data….

How companies are using big data and analytics

McKinsey & Company

Just how do major organizations

use data and analytics to inform strategic and

operational decisions?

Ten Ways Big Data Is Revolutionizing Marketing and

Sales

Forbes May 9, 2016

Customer Value Analytics (CVA) based on Big Data is

making it… A Forrester study found that 44% B2C marketers

are using big data and analytics to…

Charles Schwab

Companies are investing in big data….

..for greater insights and faster decisions


*Source: NVP Big Data Executive Survey

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0%

Greater business insights

Faster time-to-answer

Faster speed-to-market

Greater analytics capabilties

Create a data-driven culture

Business Drivers of Big Data

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

$1B+ $500M+ $100M+

Big Data Investment Last 5 Years Big Data Investment Last 5 Years


Yet, many analytical investments are NOT driving business value or outcomes!

• Accenture research shows that 1/3 of

companies are now using analytics

aggressively across the enterprise. Yet, for

many, the ROI is elusive.

— Only 22 percent of companies are

“very satisfied” with the outcomes of

their analytics investment

• Actionable insight to drive business often

appears to be the missing link between data

and business outcome

– According to Forrester, 74% of firms

say they want to be “data-driven,” but

only 29% are successful at

connecting analytics to action

Charles Schwab

Today we will share our Big Data Analytics Journey


Charles Schwab

We started our Big Data Analytics journey 5 years ago by identifying the key data issues


Inconsistent data definitions

Difficulty in accessing data

Key data about clients and prospects not captured

Proliferation of different system implementations across the firm

Limited documentation and metadata

Lack of change control processes

Limited funding for major data infrastructure projects

Charles Schwab

Our primary focus was chosen to deliver the greatest value in the most timely manner


Inconsistent data definitions

Difficulty in accessing data

Key data about clients and prospects not captured

Proliferation of different system implementations across the firm

Limited documentation and metadata

Lack of change control processes

Limited funding for major data infrastructure projects

Charles Schwab

Our initial ‘Big Data’ repository was developed to provide a comprehensive view of client & prospect interactions


Big Data enables us to understand how customers and prospects engage with Schwab and relate

their engagement to business outcomes

USE CASES

Shopping

Journey

Analytics

Cross

Channel

Attribution

Attrition /

Big Mover

Next

Best

Conversation

Deeper

Insights

Online Interactions

Schwab.com visits

Display Ad impressions & clicks

Paid Search clicks

Organic search clicks

Chat sessions & transcripts

Mobile web visits

Mobile app interactions

Post-Conversion Events

Assets In, Assets Out, Trades

Conversion Events

Account opens, New-to-Firm

households, offer enrollments

Account open channel

New-to-Firm household

segmentation

Offline Interactions

Branch appointments

Branch activities & opportunities

Direct Marketing campaigns

Phone interactions – who & when

Phone interactions – why via

Speech Analytics

Charles Schwab

We selected specific use cases to drive business value


Shopping Journey

Analytics

• Increase acquisition and funding by providing key insights to change

interaction model with prospects and new clients

Cross-Channel

Attribution

• Improve ROI by determining how to optimize marketing campaigns

across channels

Attrition / Big Mover • Prevent attrition and/or large competitive outflows through pro-active

actions

Next Best

Conversation

• Improve client service and cross-sell opportunities by providing relevant

messages and offers to clients

Deeper Insights • Drive product innovation and enhance client experiences with new client

insights

Charles Schwab

Initial use case provided deep insights to address key

questions about the Prospect Shopping Journey


Key Questions

What are the key prospect touchpoints prior to conversion?

How long does it take for prospects to convert from their first Schwab

interaction?

What are the most common paths prior to conversion?

Are there differences in how Affluent vs. non-Affluent prospects choose to

interact with Schwab?

Are there certain interactions that happen most often at the start of the

conversation with Schwab? Or near the end, closer to conversion?

How do post-account opening interactions drive funding?

Similar analyses have been conducted for other client journeys

Charles Schwab

Cross Channel Attribution uses advanced analytics to optimize marketing by campaign and touch point


Interacted online

& offline

Richard

Johnson Base

Jan 15-19 Jan 22 Jan 19 Jan 23 Jan 23

Site

Display Site Visit

Inbound

Call Outbound

Call

Branch

Meeting

Schwab Assets = $285 k

Online Offline

The model allocates value to each touch point within the journey

15% 15% 10% 10% 20% TOT: 70% FRACTIONAL

CREDIT

(ILLUSTRATIVE)

Charles Schwab

We leveraged big data and machine learning to

deliver personalized messages


Leverages real-time

decisioning engine that

continually learns based

on prior activity

Delivers best “offer” (sales

and service) from over

200 offers

Conversion rates are

significantly higher than

control

Intelligent Targeting

Targeted banners on

Schwab.com

Next Best

Conversation

Aims to deliver “next

best conversation” in

call center

Leverages real-time

decisioning engine

Selects offers /

messages tuned to live

interactions

Future

More unstructured and

real-time data

More sophisticated

machine learning

algorithms

More channels

Charles Schwab

Data Science is the next transformational evolution in our analytics journey


Monitoring

Insights

Optimization

Transformation

Traditional Analytics

• Descriptive Analytics

• Structured Data

• Large data sets

• Leverages tried & true technologies

– Teradata

– SAS

– Business Objects

Data Science

• Predictive and Prescriptive Analytics

• Unstructured Data, Text and Voice

• Huge volumes of data processed at scale

• Open source, open architecture

• Encompasses leading technologies

– Hadoop distributed environment that can

store and process all kinds of data at scale

– Python, Spark, R

– Natural Language Processing

– Machine Learning/ AI

– Deep learning

– Neural Networks


We are building and implementing Data Science capabilities at Schwab

Hired a small group of talented PhD data scientists

Deep expertise in AI, Machine learning, Natural language processing, Deep

learning, etc. with focus on latest open source tools and technologies

Cross-trained existing advanced analytical team on data science capabilities

Contracted with an outsourced analytical firm in India, to build out enhanced data science capabilities

Building an enhanced technology and data foundation with our data and technology organizations to create, store and deliver massive amounts of user and interaction level data and create scalable platform


We partner closely with Global Data Office and Technology to drive analytics & insights from data

Provisioning data Driving insights from data

Delivering new

capabilities and

managing data operations

Global Data Office Analytics & Business Insight

Schwab Technology

Leverage data to create

competitive advantage


We are building a robust Data Science platform to scale Machine Learning successfully at Schwab

1. Data exploration — requires easy/automated access and connectors to various raw and composite data sources

2. Model development — involves iterative explorations on algorithms, techniques and outcomes to arrive at final models

3. Model testing — test final candidate models with live production data and select best model

4. Model deployment — deliver finalized model into production systems via APIs or other methods

5. Model monitoring — tracking and visualization of model performance for the business and MRM

Our objective is to ultimately make all 5 stages as automated as possible

A typical ML model has five stages:

Machine

Learning

Model

Lifecycle

I

5

2

Exploratory Data

Analysis &

Aggregation

Model Testing in

Production

Monitoring,

Tracking &

Visualization

Model

Deployment

Production

Model

Development

& Approval

3

4


We are focused on delivering a variety of Data Science projects to drive business impact

Financial Consultant Practice

Assignment Optimization

Lead Scoring Machine Learning

Call Center Routing and Rep

Optimization

Lifetime Customer Value

First Year Client path analysis and

lead routing

Early Warning System of Retention

Sales and Service Optimization Client Experience

New to Firm

Employee Attrition Model

Real-time deliver of client insights for

rapid response

Enterprise Fraud Engine

Corporate Support (HR, Finance)

Prospect segmentation and shopping

journey analysis

Social intelligence through NLP/NLG

Machine-learning for retargeting and

digital media buying


We have made significant improvements with our attrition model using Machine Learning

69

112

134

154

172

182

196

206

218

225

57

79

97

120

136 147

157

167

177

196

0

50

100

150

200

250

New Machine Learning models

Traditional models

# of Households

# o

f A

ttrito

rs

69


Critical Success Factors…

1. Gain executive support and align projects to top initiatives

2. Select projects that deliver immediate value, i.e. put points on the board early

and often

3. Ensure end-to-end implementation of analytics including measurement and

feedback

4. Involve and gain support of stakeholders in the project

5. Hire, develop and motivate your team

6. Partner with data and technology to build capabilities to support full analytics

lifecycle

7. Communicate accomplishments widely and often

8. Recognize that big data analytics is a journey

Charles Schwab

Q&A


Thank you!

John Carter [email protected]


chief analytics officer fall usa 2017 - john carter

Data & Analytics