Big data and customer analytics examples

Big Data & Big Data Analytics
Experiences in building a 360°Integrated Customer View

Pietro Leo
Executive Architect
Member of IBM Academy of Technology Leadership Team

@pieroleo www.linkedin.com/in/pieroleo
© 2012 IBM Corporation

IBM Institute for Business Value

What are the main Factors impacting marketing & CMO?
Big Data related
dimensions!
Marketing Priority Matrix 1 Data explosion
Underpreparedness
Percent of CMOs reporting
2 Social media
underpreparedness
1 3 Growth of channel and device choices
70
4 Shifting consumer demographics
2
3
5 Financial constraints
4 6 Decreasing brand loyalty
60
5 7 Growth market opportunities
Mean 6
8 ROI accountability
10 7 8 9
11
9 Customer collaboration and influence
50 12 10 Privacy considerations
13
11 Global outsourcing
Factors impacting
marketing 12 Regulatory considerations
40 Percent of CMOs selecting
as “Top five factors” 13 Corporate transparency
0 20 40 60
Mean IBM
2 © 2012 IBM Corporation


A new paradigm targeting a 360°Integrated Customer View in order
to leverage the Customer Empowerment

The new profession paints a predictive
picture of each customer by harnessing
data on a massive scale

• Instruments all key touch points to gather
the right data about each customer.
• Connects social media data, transaction data
and other information to paint a more vivid
picture of each customer.
• Runs the right analytics at the right time on
the right customer to generate new ideas
about whom to serve and how best to serve
that person.
• Generates insights that are predictive,
not just historical.
• Builds capabilities to do this on a massive
scale.


How Big Data Analytics can help?
Create an integrated view of Customers Data & Content from ALL enterprise
contact points including internal and external sources... social business!

Enterprise Contact Points
Customer
Branch office Call Center Self Service IVR Social

Agent Web

Unstructured Structured
Structured
Data & Content Agent Data
Call logs, Transcripts Customer/Product
Emails, Surveys Transaction Data

Big Data Enterprise
Business Integrate and Analyze Structured and Unstructured Data products
Analytics and services

Insights  Customer Intelligence  Agent Performance  Dissatisfaction Drivers
Distribution  Process Understanding  Social Drivers  Sales Drivers
& Utilization



A new paradigm targeting a 360°Integrated Customer View in order
to leverage the Customer Empowerment

Customer Virtual
Ex2. Rebuild

measuring
marketing
campaign
Profiles &
Ex1. Collect customers
longitudinal point of views
And correlete them with
internal data


Example 1: Big Data Analytics to collect Customer longitudinal point of
views from Web 2.0 and correlate them with internal data
Business challenge
Social media is considered a new and relevant source to understand the consumer
and improve service levels; measure its own as well as the competition’s brands and
“Big Data is a great
products; and compare results with traditional TV research data for better market
awareness. Particularly, the company needed a social media analytics solution that opportunity for TV
could: accurately measure the echo on Social Media about the efficacy of its products innovation in the next
and campaigns; provide insight into competitors; years. TV viewing is
Solution transforming into a
multiplatform and
IBM helped the client to analyze unstructured data across a number of social media
channels and assess the company’s corporate brands, with respect to its competitors, participative experience:
as well as to discover and statistical measure signals and alerts about viewer the better we know and
preferences and experiences about TV contents. Specifically, among others, included: understand our viewers,
detect hot words and design attitudinal indicators about company products and services the better we can serve
discover new trends and hot words on Social Media in order to compare them and weight them with respect them." – Valerio Motti,
to internal information streams Head of Marketing
analyze findings and evaluate their significance with respect to business priorities Innovation, Mediaset
correlate customer attitudinal attributes results of unstructured data analysis with internal data S.p.A.
Benefits
• Measure as well as discover a number of signals referred to TV viewers that were expressed
in web 2.0 comments and referred to several kind of TV contents and TV ads campaigns.
• A number of interesting correlations were discovered and evaluated among those signals
and with respect to internal customer loyalty parameters that will contribute refine the company
marketing strategy.

6 Nov 14, 2012 © 2012 IBM Corporation

Example 1: Big Data Analytics to collect Customer longitudinal
point of views from Web 2.0 and correlate them with internal data
Information Sources Working Environment OUTPUT

STRONG WEAK
SIGNALS SIGNALS

Content
Provider /
Aggregators

Social Intelligence Workplace Regular Reporting & Monitoring

Model taxonomy
Hotwords Taxonomy
RSS
Sorgenti
FeedRSS
TAXONOMIES RELTATIONS
REPORTING SENTIMENT Specialized
ADVANCED SEARCH: CONCEPT Analysis &
DISCOVERY Studies
DISCOVERY and
ANLYSYS OF ALERTS
INFLUENCERS

7

360-degree Consumer Profiles from Social Media
Personal Attributes
Personal Attributes
• Identifiers: name, address, age, gender,
• Identifiers:
occupation… name, address, age, gender,
• occupation…
Interests: sports, pets, cuisine…
Timely Insights
Timely Insights
• Intent to buy various products
• • Interests:Status: marital, parental
Life Cycle sports, pets, cuisine…
• Life Cycle Status: marital, parental • • Intent to buy various products
Current Location
• • Current Location
Sentiment on products, services, campaigns
• • Sentiment on products, services, campaigns
Incidents damaging reputation
• • Incidentssatisfaction/attrition
Customer damaging reputation
• Customer satisfaction/attrition
Life Events
Life Events
• Life-changing events: relocation, having a
• Life-changing events: relocation, having a
baby, getting married, getting divorced, buying
a baby, getting married, getting divorced, buying
house…
a house…
Products Interests
Products Interests
• Personal preferences of products
• • Personal preferences of products
Product Purchase history
Relationships • • Product Purchase historyservices
Suggestions on products &
Relationships • Suggestions on products & services
• Personal relationships: family, friends and
• Personal
roommates…relationships: family, friends and
• roommates…
Business relationships: co-workers and
• Business relationships: co-workers and
work/interest network…
work/interest network…

Monetizable intent to buy products Life Events
I need a new digital camera for my food pictures, any College: Off to Stanford for my MBA! Bbye chicago!
recommendations around 300? my food pictures, any
I need a new digital camera for College: Off to Stanford for my MBA! Bbye chicago!
recommendations around 300?
Looks like we'll be moving to New Orleans sooner than I thought.
What should I buy?? A mini laptop with Windows 7 OR a Apple Looks like we'll be moving to New Orleans sooner than I thought.
MacBook!??! I buy?? A mini laptop with Windows 7 OR a Apple
What should
MacBook!??! Intent to buy a house
I'm thinking about buying a home in Buckingham Estates per a
Location announcements I'm thinking about buying a home in Buckingham Estates per a
recommendation. Anyone have advice on that area? #atx #austinrealestate
recommendation. Anyone have advice on that area? #atx #austinrealestate
#austin
I'm at Starbucks Parque Tezontle http://4sq.com/fYReSj #austin
I'm at Starbucks Parque Tezontle http://4sq.com/fYReSj

Example 2: Big Data Analytics to expand knowledge about customer profiles and
measuring marketing campaign
• Analysis of social media messages for large Media and Entertainment company to determine reaction
to movie commercials aired during the SuperBowl
• Insights based on 30M+ social media consumer profiles created by analyzing over a Billion messages
• Real-time evolution of insights correlated with the airing of the commercial

Key Business Questions: Consumer demographics by movie
Battleship
How many people are talking about the film ?
Gender Female Male

• Intention to see the movie, Impact of SuperBowl
commercial Top 10 Markets

Who are they ? The Dictator

• Demographics, Influencers, avid movie goers Gender Female Male

Top 10 Markets
What is the reaction ?
• Categorized sentiment (plot, characters, …) Gender
Act of Valor
Female Male

• Comparison with competitive movies Top 10 Markets

10% 20% 30% 40% 50% 60% 70% 80% 90%
Competitive Intelligence
Comparing feedback by important microsegment (avid movie-
Buzz Amongst Avid Movie-Goers Jan -
goers) Amongst Avid Movie-Goers During
Buzz
Feb Super Bowl

Think Like a Man
Act of Valor 21 Jump Street
The Lorax The Dictator Project X
The Lorax
Act of Valor John Carter
Project X
Battleship
The John Carter
Ghost Rider
Avengers
The Dictator
21 Jump Street
The Dark Knight Rises The Dark Knight Rises
Battleship G.I. Joe
@pieroleo www.linkedin.com/in/pieroleo Spider-man The Avengers Spider-man
Ghost Rider G.I. Joe

IBM SDA: Social-media Based Micro-segmentation and Real-time Correlation

InfoSphere Streams Online Flow: Data-in-motion analysis

Social Entity Predictive
Media Data Ingest Text Analytics: Integration: Analytics: Timely
Data & prep. Timely Insights Profile Action
Resolution Decisions
Determination

Text Entity Social Media Entity Predictive
Social Media Social Media Customer
Customer Customer
Social Media Analytics Integration Integration Analytics
Consumer
Consumer & Prospect
& Prospect Models
Data
Data
Profiles
Profiles profiles
profiles

InfoSphere BigInsights
Consumer
Consumer Customer
Customer
Offline Flow: Data-at-rest analysis Lists
Lists Database
Database

 Large-scale data-at-rest analysis using InfoSphere BigInsights
 Large-scale data-at-rest analysis using InfoSphere BigInsights
 Large-scale data-in-motion analysis using InfoSphere Streams
 Large-scale data-in-motion analysis using InfoSphere Streams
 Advanced text analysis, entity integration, and predictive modeling using common analytics
 Advanced text analysis, entity integration, and predictive modeling using common analytics
infrastructure across Streams and BigInsights
infrastructure across Streams and BigInsights

Business Models based on connecting Virtual and Real Words: the AMEX model

A portal that collects special
offers and discounts from
retailers and detail about the
customer segment that is target

Marketing segmentation engine
that evaluate customer profiles
and select the best coupon to
propose
American Express
Moble app and connection with Smart Offer
Twitter, Facebook e
Foursquare to communicate
with the customers and enable
viral effects

Just virtual Coupons are managed! Customers
activate the coupon and receive on montly basis
on the credit card account the equivalent of the
coupon discounts after that transactions were
registred


12

Grazie!

Pietro Leo
Executive Architect
Member of IBM Academy of Technology


Big data and customer analytics examples

Recomendados

Recomendados

Mais conteúdo relacionado

Destaque

Destaque (15)

Mais de Pietro Leo

Mais de Pietro Leo (20)

Último

Último (20)

Big data and customer analytics examples