Responsible Corporate Problem Solving - a Siemens case study | IEEE International Technology Management Conference - The Hague
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.
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Responsible Corporate Problem Solving - a Siemens case study | IEEE International Technology Management Conference - The Hague
- 1. Responsible Corporate Problem Solving - a Siemens Case Study with TechnoWeb 2.0 IEEE The Hague - June 2013 Isaac Newton Acquah 1
- 3. In the beginning…. there was TechnoWeb 3
- 5. What is an Urgent Request? 5
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- 9. "Isolated knowledge islands" Organizational and hierarchical Business process- and project specific information Local, time, cultural and language Barriers exist in large organizations TechnoWeb breaks down those barriers 9
- 10. Barriers exist in large organizations TechnoWeb breaks down those barriers 10 "Isolated knowledge islands" Organizational and hierarchical Business process- and project specific information Local, time, cultural and language
- 11. 11
- 12. commons.wikimedia.org Everybody gets the Urgent Requests 12
- 13. Imagine… 13
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- 19. Precision Recall TechnoWeb Business slider 19
- 20. 2013 Oct 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 20
- 21. Findings: Ratio of answered Urgent Requests 21 92% 93% 96% 92% 91% 87% 93% 92% 87% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 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
- 23. 30.90 15.30 7.40 4.50 26.40 8.80 4.10 2.90 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 € 1 000 € 10 000 € 50 000 € 250 000 SpamReductionFactor Simulation of the Semantic Target Messaging Algorithm (n=138) Real Life Performance of the Semantic Target Messaging Algorithm (m=477) Findings: Spam Reduction 23
- 24. 2.71 1.71 1.18 1.18 0.58 4.05 2.04 1.09 1.08 0.90 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 € 1 000 € 10 000 € 50 000 € 250 000 € 1 000 000 ConversionRate(perthousand) 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) Findings: Conversion Rate 24
- 25. Saved emails from the new system in a 1 year, 4 months period… 8 235 786 (October 2011 and January 2013)25
- 28. Work Experience About me: Isaac Newton Acquah III acquah@mercurypuzzle.com at.isaacnewtonacquah.linkedin.com twitter.com/isaacnewtonIII Get in touch 28 Education