Reading Global Clients’ Signals
By taking a big data approach to analyzing collaborations with large clients, highly distributed global companies can gain valuable and timely insights into client satisfaction.
Keeping tabs on the health of client relationships is an important activity for any company hoping to promote stability and growth. Knowing how customers feel about your products and services is particularly vital for organizations specializing in “extended-enterprise” services such as back office operations, decision support, and engineering, technology and asset support for large operations. In the last 20 years, the popularity of such so-called “shared services centers,” those shared either internally or externally through outsourcing, has grown exponentially. Shared services centers now employ hundreds of thousands of people worldwide and constitute a ubiquitous backbone across the largest and most complex enterprises. Although this organizational model has created significant economic benefits and is a cornerstone of organizational scalability and cost effectiveness, it also presents significant governance challenges: The large-scale, global nature of the service delivery and the complex, often matrixed client organizations such companies serve make it harder to detect client dissatisfaction.
Many companies monitor customer satisfaction through customer satisfaction surveys such as the Net Promoter Score pioneered by management consultant Bain & Co. An NPS score is obtained by (1) asking customers to answer a single question (“How likely is it that you would recommend our company to a friend or colleague?”) on a scale from 0 to 10 (where 10 is “extremely likely” and 0 is “not at all likely”); and (2) subtracting the percentage of “detractors” (scores 0-6) from the percentage of “promoters” (scores 9-10).1 However, such methods are not necessarily timely (because they are survey-based) and often do not enable companies to drill down into detail (because of the size of the sample). Therefore, they are not sufficient for continuous measurement of customer satisfaction or for informing timely and targeted corrective actions.
As it turns out, there are two specific challenges to behavior analysis in global organizations such as shared services centers. The impersonal, remote nature of many of the exchanges and the high volume of interactions make it extremely difficult for senior management to document and analyze them using traditional means.
The way people interact with each other and what they say to each other offer an important window into how they feel about each other.
1. F.F. Reichheld, “The One Number You Need to Grow,” Harvard Business Review 81, no. 12 (December 2003): 46-54.
2. A.S. Pentland, “Honest Signals: How They Shape Our World” (Cambridge, Massachusetts: MIT Press, 2008).
3. P.A. Gloor, K. Fischbach, H. Fuehres, C. Lassenius, T. Niinimaki, D.O. Olguin, A. Pentland, A. Piri and J. Putzke, “Towards ‘Honest Signals’ of Creativity — Identifying Personality Characteristics Through Microscopic Social Network Analysis,” Proceedings of COINs 2010, Collaborative Innovation Networks Conference, Savannah, Georgia, October 7-9, 2010, in Elsevier Procedia - Social and Behavioral Sciences 26 (2011): 166-179.
4. F. Merten and P. Gloor, “Too Much E-Mail Decreases Job Satisfaction,” Proceedings of COINs 2009, Collaborative Innovation Networks Conference, Savannah, Georgia, October 8-11, 2009, in Elsevier Procedia - Social and Behavioral Sciences 2, no. 4 (2010): 6457-6465.
5. S.L. Hybbeneth, D. Brunnberg and P. Gloor, “Increasing Knowledge Worker Efficiency Through a ‘Virtual Mirror’ of the Social Network“ (paper presented at the Fourth International Conference on Collaborative Innovation Networks COINs13, Santiago, Chile, August 11-13, 2013).
6. S. Aral and M.W. Van Alstyne, “Network Structure & Information Advantage,” Proceedings of the Academy of Management Conference, Philadelphia, (2007).
7. Y.H. Kidane and P. Gloor, “Correlating Temporal Communication Patterns of the Eclipse Open Source Community with Performance and Creativity,” Computational & Mathematical Organization Theory 13, no. 1 (March 2007): 17-27.
8. P. Gloor, D. Oster, J. Putzke, K. Fischbach, D. Schoder, K. Ara, T. Kim, R. Laubacher, A. Mohan, D. Olguin Olguin, A. Pentland and B. Waber, “Studying Microscopic Peer-to-Peer Communication Patterns,” Proceedings of the AMCIS Americas Conference on Information Systems, Keystone, Colorado, August 9-12, 2007.
9. E.M. Whitener, S.E. Brodt, M.A. Korsgaard and J.M. Werner. “Managers as Initiators of Trust: An Exchange Relationship Framework for Understanding Managerial Trustworthy Behavior,” Academy of Management Review 23, no. 3 (July 1998 ): 513-530.
10. P.A. Gloor, R. Laubacher, S.B.C. Dynes and Y. Zhao, “Visualization of Communication Patterns in Collaborative Innovation Networks: Analysis of Some W3C Working Groups” (paper presented at ACM CIKM International Conference on Information and Knowledge Management, New Orleans, Louisiana, November 3-8, 2003).
11. Merten and Gloor, “Too Much E-Mail Decreases Job Satisfaction.”
12. Aral and Van Alstyne, “Network Structure & Information Advantage.”
14. Hybbeneth et al. “Increasing Knowledge Worker Efficiency.”
15. X. Zhang, P.A. Gloor and F. Grippa, “Measuring Creative Performance of Teams Through Dynamic Semantic Social Network Analysis,” International Journal of Organisational Design and Engineering 3, no. 2 (2013): 165-184.
16. Gloor et al., “Studying Microscopic Peer-to-Peer Communication Patterns.”
17. Zhang et al., “Measuring Creative Performance of Teams.”
i. D. Brunnberg, P.A. Gloor and G. Giacomelli, “Predicting Client Satisfaction Through (E-Mail) Network Analysis: The Communication Score Card” (paper presented at the Fourth International Conference on Collaborative Innovation Networks COINs13, Santiago, Chile, August 11-13, 2013).
ii. Merten and Gloor, “Too Much E-Mail Decreases Job Satisfaction.”
iii. Hybbeneth et al., “Increasing Knowledge Worker Efficiency.”
iv. Aral and Van Alstyne, “Network Structure & Information Advantage.”