Social

CRM@Telekom

Dr. Marco Hetterscheidt

Zürich, März 2013

Deutsche Telekom –

profile.

2011

Germany.

35 m

mobile customers

Leading

(V) DSL-provider

in Germany

EUR 24.0 bn

revenueEUR 9.6 bn

Ebitda

76,028 employees97,522 employees

(incl. Headquarters/GHS)

1.8 m

IPTV customers

12 m

broadband

connections

Data basis financial figures: DT annual report 2011

Deutsche Telekom.

Modern Broadband

& Mobile experience.

Television & Cloud Services: „Cloud for

everyone“.

CRM –

In focus

CRM acts as an enabler for Sales and Service.

The Right OfferSelection of relevant

Sales and Service offerings

In the Right ChannelDisplay of individual

offer recommendations

for each customer

Sales and ServiceMarketing

IT systems and processes

For Each Customer

Customer

Insight; e.g. analyses,

segmentations, customer affinities

At the

Right TimeManagement of sales and loyalty as well as

inbound and outbound measures

100+ processes/programs

50+ input tables on 40 million contracts and other characteristics

1000+ different variables from the Jewel-Box

Thousands of lines of program code

Approx. 100 affinity/retention models

Over 200 different product recommendations

Model Prediction

Processor

Target Scoring

Mining Quality

Value Prediction

Mining Manager

Jewelbox

Model Report

Mining Team

800 Million Pearls(are used for steering)

What happens in the Mining Factory every month?

Heart of scoring and value prediction: „Mining Factory“. It ensures the right offer at the right time in the right channel for each customer.

€

Facebook: >50k Fans

Youtube: >3k Views

Facebook: >250k Fans

Youtube: >500k Views

Telekom and Social

Web.

Telekom Erleben Telekom hilft Liga total!

>600k fans

in several

facebook

channels

Facebook: >150k Fans

Youtube: >7.7 Mio. Views

Network

Analysis:

In-, Out-Degree

Hub-& Authority

score

Communities

Indentification

of multipliers

& communities

for

special

sales

& service offers

Command

Center:

Shitstorm

alert

„Like“

and „Share“

count

Identification

of Shitstorms

and rapid response

functionality

Sentiment Analysis:

Tag Clouds

Identification

of service topics

Proactively

inform

customers

about

solutions

product

improvement

processes

improvment

Social

Media –

first

steps.

+

Processing

unstructured

data

of internal

& external sources.

KNIME

Proccessing

unstructured

data

with

KNIME to find golden nuggets

example

flow

General Social

Media network

–

definitions.

Author

network

analysis

–

undirected, weighted

digraph

32

14

Aij

0 1 0 00 0 1 11 0 0 00 0 0 0

The

authors

of a social

media page

form an undirected, weighted

digraph

The

number

of authors

to whom

a given

author

has incoming/outgoing

connections

are

given

by

the

in-/and

out-degrees

The

authority

and hub scores

represent

the

leaders

and followers

of a network

The

adjacency

matrix

is

asymmetric

and is

0 where

no connection

between

authors

exists

Adjacency

Matrix:

Author

2 has two

out-

and one

in-degree

Link between

author

1 and 2 is

directed

and weighted

3

In-

& Out degree

distribution

Degree

characteristic

of a fan

page.

Authors

In-degreeOut-degree

Only

a few

authors

have

several

in-

and out degrees

Most authors

have

one

1 or

two

in-

or

out degrees

Sparse

network!

exemplary

012345678910111213141516171820212225273031363739488910

0423

280

16

37

0

5

10

15

20

25

30

35

40

45

50

1234611131537

Likes

and posts

of a fan

page.

Example:

1 Author

who

received

in total 6 likes

for

11 posts.

PostsLikes

Authors

0

1000

2000

3000

4000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37

Post with

8.900 Likes

Likes

per post of a „fan

page“(23.280 Likes

-

in total)

Authors

vs. posts

vs. likes

exemplary

Network

of a fan

page.

network

analysis

of authors

–

nodes

and links: identification

of multipliers

Author

with

a strong

leader

characteristic

-multiplier:

1 Post with

1.004 Likes

76 In-Degree

& 3 Out-Degree

111 Total-In-Comments

Authority

Score = 1; Hub Score = 0,01

Author

with

a strong

follower

characteristic

1 Post with

0 Likes

0 In-Degree

& 45 Out-Degree

57 Total-Out-Comments

Authority

Score = 0; Hub Score = 0,013

Sparse

network

with

little

cross linking

few

„leaders“

but

with

high influence

exemplary

The

process

of sentiment

analysis

with

KNIME

Social

Media & Textmining.

MessageTransf. intodocuments

Extraction

of sentences

Breakdowninto

terms

Detection

of sentiments

Classification

Challenges:

every

community

has its

own

language

every

topic

has its

own

taxonomy

identification

of irony, sarcasm

sentiment-determination

of single

messages

with

high viral

potential

Development

of sentiment

score

over

time

Social

Media & Textmining.

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TelekomVODAFONEYOURFONE2 week

zoom-in

weeks

sentiment

score

sentiment

score

days

Positive Storm on a Telekom fan

page

on one

day

+

–

competitor

1competitor

2

Thanks for your attention!