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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
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
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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5
15
25
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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