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

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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

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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

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The shift from CRM to Social CRM

CRM SOCIAL CRM

Co

llab

ora

tive

O

per

atio

nal

A

nal

ytic

al

- Phone

- Email

- Mail

- 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

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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

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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

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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

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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

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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

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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

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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

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Thank you