Thomas H. Davenport Babson College/MIT/International Institute for Analytics

Customer Analytics 3.0 Integrating Big and Small Data for Fast Impact

Big data begins at

online firms

& startups

No technical or

organizational

infrastructure to

co-exist with

Working wonders for

Google, eBay, & LinkedIn

…but what about

everyone else?

What happens in

20 big companies when

marketing analytics are

well-entrenched?

Findings show evolution

of a new analytics

paradigm

“Big Data in Big Companies” Study

• How new? “Not very” to many –continually

adding data over time

UPS – Started building telematics capabilities in 1986

• Excited about new sources of customer data,

new processing capabilities

• Familiar rationales for customer-facing big data:

Same decisions faster – Macy’s, Caesars

Same decisions cheaper – Citi

Better decisions with more data – United Healthcare

Product/service innovation – GE, Novartis

• Need new management paradigm

Customer Analytics 1.0 Traditional Marketing

• Primarily descriptive analytics

and reporting

• Internally sourced, relatively small, structured

data

• “Back room” teams of analysts

• Internal decision support focus

• Slowly-developed batch scoring models

• Warehouse-centric storage

1.0

IT and Marketing are

enemies

How dare you question the

value of my campaign?

Our scores last forever!

Let’s give our segments

cute personas!

Customer Analytics 2.0 The Big Data era

• Complex, large, unstructured data

about customers

• New analytical and computational

capabilities needed—i.e., Hadoop

• “Data Scientists” emerge

• Online and digital marketing firms create

data-based products and services

2.0

2.0 Data Products

• Google—Search, AdSense, Books, Maps, Scholar…and now Nest

• LinkedIn—People You May Know, Jobs You May Like, Groups You May Be

Interested In, etc.

• Netflix—Cinematch, Max, etc.

• Zillow—Zestimates, rent Zestimates, Home Value Index, Underwater Index, etc.

• Facebook—People You May Know, Custom Audiences, Exchange

We need to be “on the bridge”

Agile is too slow

Decision consulting = dead zone

We don’t need to talk to a customer

Customer Analytics 3.0 Fast, Pervasive Analytics for Customer Decisions and Offerings

• A seamless blend of traditional analytics and big data

• Analytics integral to marketing and all other functions

• Rapid, agile insight and model delivery

• Analytical tools available at point and time of decision

• Analytics are everybody’s job

• Industrialized marketing processes

3.0

TODAY

Customer Analytics 3.0 Competing in the Data Economy

• Every company – not just online firms – can create data

and analytics-based products and services

• Start with data opportunities or start with business

problems? Answer is yes!

• Need “data products” team good at data science,

customer knowledge, new product/service development

• Continuous, real-time customer analytics

• Customer analytics embedded into decision processes

and circulated widely

• More speed, more scale, more granularity of models

Products/

Services

Decisions

Customer Analytics 3.0: Data Types

• Customer profiles

• Organization

contacts

• Billing

• Marketing

• Contracts/orders

• Shipping

• Claims

• Call center

• Customer service

• Purchase history

• Segmentation

• Customer value

• Purchasing behavior

• Recommendations

• Sentiment analysis

• Target marketing

• Satisfaction

• Customer

experience

management

• Service tiers

Clickstream logs

Images

RSS Videos

Hosted applications

Spatial GPS

LinkedIn

Device sensors

Email

Articles

Text messages

Cloud

Mobile devices XML

Presentations

Blogs

Website activity

Social Feeds

Twitter

Documents

• Heavy reliance on machine learning

• In-memory and in-database analytics

• Integrated and embedded models

• Delivery to multiple channels, specifically mobile

• Hadoop, EDW, marts, data discovery, etc.

• Blended data science/marketing/IT teams

• Chief Analytics Officers, IT as key Marketing partners

Customer Analytics 3.0 Technology & people

3.0

•

• Caesars—real-time offers at slot machines

• CVS—over a billion customized, optimized

ExtraCare offers a year

• Microsoft—targeted Bing offers in 200

milliseconds

• Macy’s—repricing of all SKUs in 19 minutes

• P&G—”Decision cockpits” on 58K desktops,

with real-time social media sentiment analysis

for “Consumer Pulse” application

• Cisco—30,000 propensity models per year on

170 million companies

3.0 Customer Analytics Decisions in

High Gear

• Monsanto—FieldScripts, ClimatePro, Precision

Planting

• Elanco—poultry productivity from AgriStats

• Nest—selling thermostat data to utilities

• Fitbit—bundling activity data for employers

• GE—predictive asset maintenance for turbines

• Intuit—data products on personal and small

business finance

• MarketShare—Planner for marketing

optimization comparisons

• Medidata—clinical trial productivity

3.0 Customer Analytics Products

and Services

Corporate Executive Board survey of 800 Fortune 1000 marketers

• Marketing executives depend on data for just 11% of all customer-related decisions

• When asked what types of information supported a specific recent decision about customers, data was last on the list, after conversations with colleagues, expert advice, and interactions with single customers

• 6% of the marketers could answer five basic statistics questions, and 5% owns a stats textbook

The Biggest 3.0 Obstacle Marketers with blinders

Recipe for a 3.0 World 1. Start with an existing

capability for customer data management and analytics

2. Pick a customer analytics target

3. Add some unstructured, large-volume customer data

4. Throw some product/service innovation into the mix

5. Add a dash of Hadoop and a pinch of NoSQL

6. Cook up some applications in a high-heat convection oven

7. Train your sous chefs in digital marketing, customer analytics

Thanks! tdavenport@babson.edu