responsible corporate problem solving - a siemens case study | ieee international technology...
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Abstract of paper: A high maturity in corporate problem solving is a competitive advantage. Companies seek to use the wisdom of the crowd they have internally. One approach is to enable the employees to publish a so-called Urgent Request. For a quick and high-quality response it is helpful to distribute such an Urgent Request either to a high number of employees (broadcasting) or to target the message to those employees which have the highest probability to answer (target messaging). The first approach usually causes crowd fatigue. Therefore we focus on the target messaging approach and demonstrate how this more responsible usage of notifications can reduce the number of notifications by an order of magnitude with almost no loss of the response rate. This paper presents the real-life data of the semantic target messaging algorithm of TechnoWeb, a Siemens-internal social media platform for corporate problem solving.TRANSCRIPT
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Responsible Corporate Problem Solving -
a Siemens Case Study with TechnoWeb 2.0IEEE The Hague - June 2013
Isaac Newton Acquah
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In the beginning…. there was TechnoWeb
5
What is an Urgent Request?
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What is the problem?
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"Isolated knowledge islands"
Organizational and hierarchical
Business process- and project specificinformation
Local, time,cultural and language
Barriers exist in large organizations
TechnoWeb breaks down those barriers
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Barriers exist in large organizations
TechnoWeb breaks down those barriers
"Isolated knowledge islands"
Organizational and hierarchical
Business process- and project specificinformation
Local, time,cultural and language
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What is the problem?
12 commons.wikimedia.org
Everybody gets the Urgent Requests
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Imagine…
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Semantic Solution
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Urgent Request tagging
17commons.wikimedia.org
Targeted Messaging
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What about outliers?
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Precision Recall
TechnoWeb Business slider
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2013Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan
2010 2011 2012
Simulation Broadcasting Target Messaging
Research Timeline
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Findings: Ratio of answered Urgent Requests
Fourth Quarter 2010
First Quarter 2011
Second Quarter 2011
Third Quarter 2011
Fourth Quarter 2011
First Quarter 2012
Second Quarter 2012
Third Quarter 2012
Fourth Quarter 2012
Broadcast Targeted Messaging
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%92% 93%
96%92% 91%
87%
93% 92%87%
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10/2010
11/2010
12/2010
01/2011
02/2011
03/2011
04/2011
05/2011
06/2011
07/2011
08/2011
09/2011
10/2011
11/2011
12/2011
01/2012
02/2012
03/2012
04/2012
05/2012
06/2012
07/2012
08/2012
09/2012
10/2012
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
0
5,000
10,000
15,000
20,000
25,000
30,000
7,911
10,485
15,823 18,166
22,38824,501
Broadcast
Target Messaging
Community Size
Num
ber o
f Urg
ent R
eque
st N
otific
ation
s
Findings: Notifications sent out
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€ 1 000 € 10 000 € 50 000 € 250 0000.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
30.90
15.30
7.40
4.50
26.40
8.80
4.102.90
Simulation of the Semantic Target Messaging Algorithm (n=138)
Real Life Performance of the Semantic Target Messaging Algorithm (m=477)
Spam
Red
uctio
n Fa
ctor
Findings: Spam Reduction
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€ 1 000 € 10 000 € 50 000 € 250 000 € 1 000 0000.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
2.71
1.71
1.18 1.18
0.58
4.05
2.04
1.09 1.080.90
Simulation of the Semantic Target Messaging Algorithm (n=138)
Real Life Performance of the Semantic Target Messaging Algorithm (m=477)
Real Life Performance of the Old Broadcasting Mechanism (n=138)
Conv
ersio
n Ra
te (p
er th
ousa
nd)
Findings: Conversion Rate
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Saved emails from the new system in a 1 year, 4 months period…
8 235 786(October 2011 and January 2013)
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EmployeesSaved Costs
http://wallboom.com/smiley-faces-on-twitter.html
Positive side effects
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Thomas Mayerdorfer
Clemens Wiener
Dr. Michael Heiss
Isaac Newton Acquah
Meet the team
Dr. ManfredLangen
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Work Experience
About me: Isaac Newton Acquah III
at.isaacnewtonacquah.linkedin.com
twitter.com/isaacnewtonIIIGet in touch
Education