Targeting the right crowd for corporate problem solving - A siemens case study with TechnoWeb 2.0

Based on an expert's digital trace in a company, semantic technologies in combination with enterprise social media applications enable the identification of experts in a required field of knowledge. We analyze how this identification of experts can be used to target the right crowd for corporate problem solving and analyze how accurate this expert identification algorithm can be in the case of sparse digital trace data. The case study presents real-world data of the so-called Urgent Requests from Siemens' TechnoWeb, a Siemens-internal crowd sourcing method. The three metrics spam reduction factor, gain factor and conversion rate are defined in order to measure the quality of the semantic message targeting in relation to a simple broadcasting of the Urgent Request to every TechnoWeb user. We apply these metrics to the real-world data of the Urgent Requests and analyze the reasons why some messages are better targeted than others.