Reference no: EM133433294

Case Study: Genpact is a global business consulting firm. Its more than 90,000 employees help the world's large corporations improve their business processes. The firm, which started as a division of General Electric, applies data analytics to a wide variety of business problems. To explore its performance in satisfying customers, Genpact brought in a leader in the field, Peter Gloor, a research scientist at MIT who also runs a consulting firm called Galaxy Advisors, which specializes in the field of network analysis, or the study of communications patterns. Gloor observed that Genpact's client communications contained data that Galaxy's Condor system could gather and analyze to identify relevant performance measures. At Genpact, 176 teams of up to several hundred members worked with key accounts. Galaxy would assign 26 teams to its analysis, and the other 150 would be a control group, continuing with its normal practices. Condor would not read the contents of Genpact's messages to clients but would collect information about the e-mail between team members and clients: the frequency of the Genpact employee responding without checking with a supervisor, the simplicity of language in the subject line, the speed of responding, and the extent to which the client dealt with only one employee. The data would become performance feedback that teams could use to identify areas for improvement. Over a two-year period, Condor analyzed more than 4.5 million messages. Twice a year, Genpact conducted a customer satisfaction survey in which clients indicated their likelihood to recommend Genpact. The early results confirmed Gloor's prediction that teams whose communications were direct (no supervisor needed), simple, and speedy and that continued with the same Genpact employee would be associated with greater customer satisfaction. Each month during this period, team leaders met to learn their team's performance on the four measures, and they took this information back to their teams, so all the participating employees could see which measures needed improvement. The employees received performance feedback-data on communication patterns-once each quarter. Initially, results were provided at the level of the individual team member, in order to contribute to individual improvement. However, this raised concerns that lower-performing employees could feel punished for participating in the project. So the feedback shifted to a team-level focus. Even without the individual feedback, Genpact employees could see what needed to improve, and they shifted their communication behaviors to ones associated with client satisfaction. By the end of the test program, client satisfaction scores in the group using the Condor data rose 5%, while they fell by 12% for teams in the control group, resulting in a 17-percentage-point gap in favor of the teams getting the feedback on their communication behavior.

Questions

1. How well do you think Genpact's network analysis approach to performance management met the criteria for effectiveness? Explain.

2. If you were in Genpact's human resource department, would you recommend that the company roll out the same kind of performance feedback to the remaining teams? Why or why not?

3. In addition to identifying effective communications behaviors, what other insights could use of this data provide?