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B IG DATA BRING IG VALUE TO THE S B OCIAL CRM
2
What’s it all about?
“Big Data is about the technologies and practice of handling huge data sets that conventional database management systems
cannot handle them efficiently, and sometimes cannot handle them at all. Often these data sets are fast-streaming too, meaning
practitioners don’t have lots of time to analyze them in a slow, deliberate manner, because the data just keeps coming.
Sources for Big Data include financial markets, sensors in manufacturing or logistics environments, cell towers, or traffic
cameras throughout a major metropolis. Another source is the Web, including Web server log data, social media material
(tweets, status messages, likes, follows, etc.), e-commerce transactions and site crawling output, to list just a few examples.”
(Andrew Brust from ZDNet))
Volume
Variety Velocity
In 2005, humankind created
150 exabytes of information.
In 2011, 1.200 exabytes will
be created. (The Economist)
Worldwide digital content
will double in 18
months, and every 18
months thereafter. (IDC)
80% of enterprise data will be
unstructured, spanning
traditional and non traditional
sources. (Gartner)
The “V” drivers
3
Myself
My world
My relations
Myself
My world
My relations
Myself
My world
My relations
Myself
My world
My relations
Myself
My world
My relations
Myself
My world
My relations
Myself
My world
My relations
Wholesale/Retail
Cu
sto
mer
Supplier Partner
Pu
blic au
tho
rity Outsourcer
Company
Big Data sources inside Social Business Ecosystem
4
Let’s get a Social CRM definition
“Social CRM is a philosophy and a business strategy, supported by a
technology platform, business rules, processes and social characteristics,
designed to engage the customer in a collaborative conversation in order to
provide mutually beneficial value in a trusted and transparent business
environment. It is the company's programmatic response to the
customer's control of the conversation.“
Paul Greenberg CRM books author, speaker, consultant, analyst
5
The shift from CRM to Social CRM
CRM SOCIAL CRM
Co
llab
ora
tive
O
per
atio
nal
A
nal
ytic
al
- Phone
- Fax
- Web form
- Face2face
- Social Network
- Micro blogging site
- Blog
- Forum
- Collaborative platform
- Social media monitoring
- Unified Agent Desktop
- Enterprise Collaboration
- Collaborative KM
- VoC
- Data Mining
- Business Intelligence
- Contact & Case Management
- Trouble ticketing Management
- Marketing automation
- SFA
- KM/BPM/ERP integration
6
The Big Data funnel for Social CRM
Real Life
Touchpoint
Data streams
Information
Insight
Location-based data Web & social data Traditional interaction data Transactional data
EXTENDED HUMAN EXPERIENCE
7
Customer
Now we are plenty of “human” data
Myself My world My relations
- Geographic:
Where I live
Where I work
Where I spent my holidays
- Socio-demographic:
My age
My gender
My family size
My income
My occupation
My education
My religion
My nationality
- Psychographic (formal):
My lifestyle
My personality
My values
- Information gathering:
How I compare
What I compare
What drive my choice
What I choose
-Transaction:
What I buy
How I buy
Where I buy
When I buy
- Usage:
How I use
How much I use
Where I use
When I use - Interaction:
Information need
Trouble/problem
Claim
Praise
- Conversation:
Where I discuss
What I discuss about
How I contribute
- Psychographic (outspoken):
What I like
What I believe
What I think about
What I don’t endure
- People:
What people I relate with
Whom I’m influenced by
Who I influence
8
What’s practically changing?
SEGMENTS TRIBES
We are in the presence of a structural shift from To
9
So it’s time to really understand your people
ATTITUDES
EMOTIONS OPINIONS
EXPERIENCES
10
How can we handle it?
"We have free and ubiquitous data, so the complimentary scarce factor is the ability to understand that data and
extract value from it.“
Hal Varian, Google's Chief Economist
“A wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the
overabundance of information sources that might consume it”
Herbert Simon, Economist
Human intervention is fundamental for decision making but we need help
and support to process and understand data because of our cognitive and
time limitations
11
What’s in it for me?
Association rules learning Classification
Clustering & Factoring
Regression Ensemble learning
Natural Language Processing
Met
ho
ds
Dat
a
Scoring
Social Network Analysis
Sentiment analysis
Optimization
Time Series Analysis
Number Free Text Tag Audio Image Video
Behavioral analysis
Spatial information analysis
Opinion extraction & summarization
Ap
plic
atio
n
Real-time question answering
Semantic analysis History information analysis
An
alys
is
Proactive routing Event/Trend detection
Fraud detection
Lead generation
Churn prediction
Proactive selling
Location-based marketing
DE
SC
RIP
TIV
E &
PR
ED
ICT
IVE
AN
ALY
TIC
S
DO
MA
IN
12
Can we trust Analytics?
What do you need to
measure to accomplish
your own business tasks?
What scale and
measurement will help you
translate sentiment into
business decisions?
What accuracy measures
fit your own business
needs?
What is the accuracy
impact on business?
People are quite confident about numbers but are suspicious of “unstructured data” algorithms’ output accuracy
High accuracy doesn’t always mean more positive business impacts
You may want to analyze at
document level (tweet,
email, etc.) or at feature level
(named entity, concept,
topic, etc.)
You may prefer an explicit
class or a score. Or maybe
you need more mood than
valence.
Most people confuse
accuracy with precision. But
accuracy is a function of
precision and recall so
remember that results are
relevant if they can help you
respond to a specific
business challenge.
Tools can normally reach 80% accuracy but you have to express skepticism for >95% values (overfitting)
Not all inaccuracies have
equal business impact. You
may focus your attention
only to some kind of errors
and drop others depending
on your business objective
The 4 “What” on accuracy
13
An example for Social Customer Service
Opinion extraction / Semantic Analysis / Sentiment Analysis
Automatic response (real Q&A)
Customer Claim history Churn Prediction Customer LTV Concept highlighting Polarity highlighting
Most frequent issue (service request)
Most frequent issue (concept)
Behavioral Analysis / History Information Analysis
LTV Score
High Churn High LTV
Conversation / Psychographic / Relations Transaction / Billing / Payment / Usage / Interaction
Automatic routing to selected CSR Most probable issue-related contents
retrieval
Churn Score
14
Great opportunities but pay attention to the issues
Data policies
Change management
Data access
Technology
Massive parallel processing
Distributed architectures
Main issues to address
Privacy
Security
Liability
Talent
Analytical
culture
“Sharing”
obstacles
“Sharing”
incentive
15
Thank you