customer intelligence & analytics - part i

1.1 Introduction

1.2 Competing On Analytics

1.3 The Data Explosion

1.4 Evolving Context of Marketing Analytics & Research

1.5 Questions

Module 1: The World of Marketing Is

Changing - Are You Being Left Behind?

• Debbie Mayville – Sr. Solutions Architect, Communications & Marketing

Analytics, SAS

• David Kelley – Sr. Solutions Architect, Customer Intelligence, SAS

• Suneel Grover – Solutions Architect, Integrated Marketing Analytics, SAS

– Adjunct Professor, Integrated Marketing Analytics,

New York University (NYU)

1.1 Introduction

1.5 Questions

„Old Spice‟ Campaign Case Study

Achieving Success With Business Analytics

What happened?

How many, how often, where?

Where exactly is the problem?

What actions are needed?

Why is this happening?

What if these trends continue?

What will happen next?

What’s the best that can happen?

Optimization

Predictive Modeling

Forecasting

Statistical Analysis

Alerts

Query Drilldown

Ad hoc Reports

Std. Reports

Business Analytics

“The extensive use of data, statistical and quantitative

analysis, explanatory and predictive models, and fact-

based management to drive decisions and actions.”

Davenport and Harris (2007)

Competing on Analytics:

The New Science of Winning

Data Deluge

Three Consequences Of The Data Deluge

1. Every problem will generate data eventually.

2. Every company will need analytics eventually.

3. Everyone will need analytics eventually.

Proactively defining a data collection protocol will

result in more useful information, leading to more

useful analytics.

Proactively analytical companies will compete more

effectively.

useful analytics.

Proactively analytical companies will compete more

effectively.

Proactively analytical people will be more

marketable and more successful in their work.

The Business Analytics Challenge Getting anything useful out of tons and tons of data

Hope For The Data Deluge

= actionable knowledge

+ analytical tools

Changes In The Analytical Landscape

Analytical Modelers Management

Historically…

Historically, analytics have

typically been handled in

the “back office,” and

information was shared

only by a few individuals.

Models

Historical Changes

– Executive Dashboards

• Static reports about business processes

– Customer Relationship Management (CRM)

• The right offer to the right person at the right time

– 360-degree customer view

Relational Databases

Enterprise Resource Planning (ERP)

Point of Sale (POS) Systems

Decision Support Systems

– Reporting and Ad Hoc Queries

– Online Analytical Processing (OLAP)

Performance Management Systems

– Executive Information Systems (EIS)

– Balanced Scorecard

Business Intelligence

CRM Evolution

• Total Quality Management (TQM)

– Product-Centric

• Quality: Six Sigma

• Total Customer Satisfaction

• Mass Marketing

• One-to-One Marketing

– Customer Relationship

• Wallet Share of Customer

• Customer Retention

• Customer Relationship Management (CRM)

– Customer-Centric

• Strategy

• Process

• Technology

Customer Service

Retail

Promotions

Operations

Analytical Modelers

Targeting

Customers

Suppliers

Employees

Now…

Now analytics are being pushed out

to the “front office”. There are clear,

tangible benefits that management

will track. Data mining is a critical part

of business analytics.

Proliferation of

Models

Idiosyncrasies Of Business Analytics

1. The Data

- Massive, operational, and opportunistic

2. The Users and Sponsors

- Business decision support

3. The Methodology

- Computer-intensive adhockery

- Multidisciplinary

Data mining can be defined as

advanced methods for exploring

and modeling relationships in

large amounts of data.

The Data

Experimental Opportunistic

Purpose Research Operational

Value Scientific Commercial

Generation Actively controlled Passively observed

Size Small Massive

Hygiene Clean Dirty

State Static Dynamic

The Data: Disparate Business Units

Marketing Invoicing Risk

Acquisitions Operations Sales

Opportunistic Data

– Operational data

• Typically not collected with data analysis in mind

– Multiple business units

• Silo-based data environment

This makes business analytics different from

experimental statistics and especially challenging

The Methodology: What We Learned Not to Do

• Prediction is more important than inference

1. Metrics are used “because they work”

2. p-values are directional guides

3. Interpretation of a model might be irrelevant

4. The preliminary value of a model is determined by

its ability to predict a holdout sample

5. The long-term value of a model is determined by

its ability to continue to perform well over time

6. Models are retired as behavior and trends shifts

Using Analytics Intelligently

• Intelligent use of analytics

1. Understanding of how

marketplace shifts affect

business performance

2. Ability to distinguish between

effective and ineffective

interventions

3. Efficient use of assets, reduced waste

4. Risk reduction via measurable outcomes

5. Early detection of trends hidden in massive data

6. Continuous improvement in decision making

Simple Reporting

Examples: OLAP, RFM, descriptive statistics, extrapolation

Answer questions such as:

1. Where are my key indicators now?

2. Where were my key indicators last week?

3. Is the current process behaving like normal?

4. What’s likely to happen tomorrow?

Proactive Analytical Investigation

Examples: Data mining, experimentation, empirical

validation, predictive modeling, optimization

Answer questions such as:

1. What does a change in the market mean for my targets?

2. What do other factors tell me about my target?

3. What is the best combination of factors for maximum profit?

4. What is the highest price the market will tolerate?

Data Stalemate

• Many companies have data that they do not use or sell

to third parties. These third parties might even resell the

data and any derived metrics back to the original

company!

• Story: Retail grocery POS card

Every Little Bit…

Taking an analytical approach to only a few key business

problems with reliable metrics tangible benefit

The benefits and savings derived from early analytical

successes managerial support for more analytics

1. Everyone has data

2. Analytics can connect data to

smart decisions

3. Proactively analytical companies

outpace competition

Areas Where Analytics Are Often Used

• New customer acquisition

• Customer loyalty

• Cross-sell / up-sell

• Pricing tolerance

• Supply optimization

• Staffing optimization

• Financial forecasting

• Product placement

• Churn

• Insurance rate setting

• Fraud detection

• …

Which residents in a ZIP

code should receive a

coupon in the mail for a

new store location?

• Churn

• Fraud detection

• …

What advertising strategy

best elicits positive

sentiment toward the

brand?

• Churn

• Fraud detection

• …

What is the best next

product for this customer?

• Churn

• Fraud detection

• …

What is the highest price

that the market will bear

without substantial loss of

demand?

• Churn

• Fraud detection

• …

How many 60-inch HDTVs

should be in stock?

• Churn

• Fraud detection

• …

What are the best times

and best days to have

technical experts on the

showroom floor?

Areas Where Analytics Are Often Used • New customer acquisition

• Churn

• Fraud detection

• …

What weekly revenue

increase can be expected

after the Mother’s Day

• Churn

• Fraud detection

• …

Will oatmeal sell better

near granola bars or near

baby food?

• Churn

• Fraud detection

• …

Which customers are most

likely to switch to a

different wireless provider

in the next six months?

• Churn

• Fraud detection

• …

How likely is it that this

individual will have a claim?

• Churn

• Fraud detection

• …

How can I identify a fraudulent

purchase?

When Analytics Are Not Helpful

• Snap decisions required

• Novel approach (no previous

data possible)

• Most salient factors are rare

(making decisions to work

around unlikely obstacles or

miracles)

• Expert analysis suggests a

particular path

• Metrics are inappropriate

• Naïve implementation of

analytics

• Confirming what you already

Deciding when to run

from danger

data possible)

miracles)

particular path

analytics

Predicting the adoption of

a new technology

data possible)

miracles)

particular path

analytics

Planning contingencies

for employees winning

the lottery

When Analytics Are Not Helpful • Snap decisions required

data possible)

miracles)

particular path

analytics

The seasoned art critic

can recognize a fake

data possible)

miracles)

particular path

analytics

Predicting athletes’

salaries or quantifying

When Analytics Are Not Helpful • Snap decisions required

data possible)

miracles)

particular path

analytics

Only looking at one

variable at a time

• Snap-decisions required

data possible)

miracles)

particular path

analytics

Ignoring variables that

might be important

The Fallacy Of Univariate Thinking

What is the most important cause of churn?

Prob(churn)

International

Daytime

Expectations Leading The Analysis

• Sophisticated analytics are not immune to personal bias

– Selectively fitting models because they place an opinion or

agenda in a positive light

– Ignoring information that might disprove a hypothesis

• Personal bias, whether intentional or not, can diminish

the usefulness of analytics

Trustworthy Analytics

Let the data guide your conclusions

– Are my assumptions about the causes

of the data patterns warranted?

– Should I be trying something different?

Assign a cynic to the analytical team whose purpose is

to question the assumptions

Idea Exchange

Identify several business problems that you could

address with analytics

Describe the goal, whether the variables can be

measured, how the data could be obtained, and what

types of specific questions you would like to address

with analytics

Case Study – US Telco

• Data Deluge: Just Get Started

– Low hanging fruit

– Continue to build and get smarter

– 360 degree view of the customer

• Tools: Efficiency & Effectiveness

– Data management tools

– Analytic tools

• Move to data driven insights versus gut reactions

• Establish measurement system

– Test & Learn Environment

Customer Lifecycle – Touch Points

Obtaining 360 Degree View Of The Customer

360 Degree

Customer View

Activ-ation Firmo-

graphics

Demo-graphics

Point of Sale

Service, Repair

Network

Commu-ni-

cations Collect-ions

Billing

VOD, Games

Hard-ware

Social Network

Large Telco With Industry-leading Churn Rate

Churn Reduction By Reason

Churn Reduction Value ($)

Total 82 bps $912M

Equipment $121M 9 bps

16 bps $163M Usage

15 bps $158M Network

11 bps $110M Active Issue

Resolution

25 bps $273M Contract Renewal

Sales Channel / Credit &

Collections 6 bps $87M

Business Issue • Company-wide initiative to lower the churn rate among customers

• Focus on “high value” or “high value potential” customers

• Improve treatment strategy and relevance

Solution • Data management • Advanced analytics

Results/Benefits • Reduced churn by 40% • Increased customer loyalty and lifetime value • Increase of operational revenues by $1B over 3 years • Ability to uncover dissatisfaction drivers and tailor proactive churn

treatments

US Telco

Case Study

1.1 Introduction

1.5 Questions

Key BUSINESS Trends Affecting Marketing

From Product to Customer

• Customer-centric business strategy

• The customer experience

• 360-degree customer view

Finding the Next Origin of Business Growth

• Consolidation/mergers/acquisitions

• Market expansion

• Efficiency & optimization

The Regulatory Rise

• Increased disclosure and transparency

• Privacy and information sharing

• Consumer contact rules

• Regulatory reform

Key CONSUMER Forces Affecting Marketing

Consumer in Charge

• Rising expectations and more choice

• From right time to “real time”

• Demographic divide

Channel Adoption

• Mobile devices and consumer adoption

• Web 2.0 and the digital age

• Cross-channel usage

Huge Online and Social Adoption

• Social networking

• Consumer-controlled content and channels

• Consumer engagement

A Broadened Definition of “The Customer”

The Consumer

The Citizen

The Subscriber

The Plan Member

The Patient

The Patron

...applicable across B2C & B2B

Customer Intelligence Is Relevant Across Industries

Financial Services Insurance Retail

Hospitality &

Gaming Manufacturing

Government Marketing Service

Providers

Health &

Life Sciences

Utilities

Telco & Cable

The Marketer Has An Evolving Mandate

Responsibilities

The Brand

The Marketing Campaign

Insights and

Analytics

The Customer

Experience

Integrated, multi-channel in/outbound conversations in real-time

Expectation

Sustain brand health in a rapidly changing virtual world

Expectation

Unearth and dynamically manage insights to drive action

Expectation

Deliver a branded customer experience in and outside of marketing

Expectation

Huge Online and Social Consumer

Adoption 2B people online,100B monthly searches and 600MM people on social networks globally

Key Forces Affecting Marketers

Ever-Growing and Converging Marketing

Channels Technology advances and consumer preferences driving new channels at unprecedented rates

Huge Online and Social Consumer Adoption

Ever-Growing and Converging Marketing Channels

Information Explosion

Business information doubling every 18 months with unstructured data representing 70% of it.

The Speed of Business

Information traveling at unprecedented rates, compounded by rising consumer expectations.

Accountability and

Need to do More

with Less Economic and competitive pressures putting

focus on marketing budgets and returns.

Accountability and Need to do

More with Less

Increasingly Competitive &

Converging Markets Parity markets with limited differentiation . Fight for share of wallet.

Accountability and Need to do

More with Less

Increasingly Competitive & Converging Markets

Brand Health Less corporate trust compounded by brands being publicly scrutinized. Traditional mass marketing proving less impactful.

The Marketing Process

Risk Customer Service

Corporate Affairs

Merchandising

Finance

Operations

Online Mobile

In Person

Call Center

Direct Mail

Social

Campaign ERP Social CRM EDW Online

Optimization

Marketing Strategy

Marketing

Marketing Processes

Marketing Campaigns

Analytics

Data Integration

The Data Integration & Management Challenge

The Flood Of Data

• Customer data continues to flood the

organization exponentially

• Progressing from functional to strategic

– Namely how to capture, integrate, manage, analyze,

and apply knowledge/insight about customers

– Google Executive Chairman Eric Schmidt:

“We create as much information in two days

now as we did from the dawn of man through

2003.”

Structured & Unstructured Data

• Company data: billing, usage, collections, set-top box,

customer, web interactions, campaign, and more!

• Consumer-generated data: Social media, blogs,

product reviews, and more!

Structured data

Semistructured data Unstructured data

“Big Data” Myths

• Data Volumes are “Exploding”

– Did Wal-Mart suddenly sell more stuff?

– Did NYSE suddenly do more stock trades?

– Did Netflix suddenly rent more movies?

– Did Amazon suddenly sell more books?

• This is existing data that

previously went un-analyzed:

A. Too large to manage

B. Too costly to store

C. Lack of “analytic chops” to capitalize

“Big Data” - Why Now?

Three Vs?

1. Volume

2. Velocity

3. Variety

Relational

Complex, Unstructured

Source: IDC

The primary driver is Value…

• Cost of storage dropping

“Big Data” - Why Now?

Data - Prerequisite For Everything Analytical

“You can’t be analytical without data, and you can’t be

really good at analytics without really good data”

Davenport, Harris, Morison (2010)

Analytics at Work:

Smarter Decision Better Results

• Structure

• Uniqueness

• Integration

• Quality

• Access

• Privacy

• Governance

Data Structure / Uniqueness / Integration

Structure

• Data structure affects analysis performance

• Transaction systems (tables), data cubes (limitations)

• Data arrays

• Unstructured data

Uniqueness

• Data only your company has access – proprietary

• Commercially available data – be the industry 1st

• Create new metrics and data fields

Integration

• Aggregate data from inside/outside your organization

• Consolidate silos across departments

• Data has to be sourced, cleaned, integrated

• Evolve to “one version of the truth”

Data Quality / Access / Privacy

Quality

• Flawed data causes misleading results

• To fix problems - look at the data source

• Continuous process – data will never be perfect

• Start based on business objectives

Access

• Source data and load in a form for analytics

• Size or complexities can cause user issues

• Speed needs require data warehouse appliances

• Sample populations

Privacy

• Guard the information collected

• Well defined policies

• Privacy laws within territories or industries

• Don’t sell information without permission (opt-in)

Data Governance

Governance

• Ensure data is useful for analysis

• Consistent, defined, sufficient quality, standardized, integrated, accessible

• Standard definitions and terminology

• Decide on investments

• Owners and stewards

• Analytical data advocates

• Business intelligence competency centers, analytical data advocate group, information management

1.1 Introduction

1.5 Questions

„The Greatest Job In The World‟

The Challenge of Digital Marketing

• As digital marketing continues to grow more

significant, new channels add complexity to the

design of a successful integrated campaign.

– It’s both a blessing and a curse for when an

integrated campaign goes viral

– Key Challenge: How do we do it again?

– No repeatable formulas

or clear attribution metrics

Reactive Business Analytics

What happened?

Optimization

Predictive Modeling

Forecasting

Alerts

Query Drilldown

Ad hoc Reports

Std. Reports

Proactive Business Analytics

What happened?

Optimization

Predictive Modeling

Forecasting

Alerts

Query Drilldown

Ad hoc Reports

Std. Reports

Afternoon Workshop Preview

• What if I could?

– Automate the measurement of sentiment relevant to my

business goals from digital channels

– Capitalize on the hidden value in vast amounts of available

structured/unstructured data associated with my brand

– Become strategically more proactive to shifting (dynamic)

consumer trends

The Marketing Process

Risk Customer Service

Corporate Affairs

Merchandising

Finance

Operations

Online Mobile

In Person

Call Center

Direct Mail

Social

Campaign ERP Social CRM EDW Online

Optimization

Marketing Strategy

Marketing

Marketing Processes

Marketing Campaigns

Analytics

Data Integration

1.1 Introduction

1.5 Questions

customer intelligence & analytics - part i

useful analytics

data deluge1

integrated marketing

data explosion

tons of data

business analytics whats

data collection protocol

data deluge analytical

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