IOP 490 Week 3 Capstone Data Analysis Example

When and Why New Agents Leave: Analyzing a Utility Call Center's Turnover Data

[Student Name]

University of Phoenix

IOP/490: Capstone Project

Week 3 Assignment

[Instructor Name]

[Date]

The student, utility call center, records and interview quotes are composites written for a model paper; research findings come from the sources listed.

The literature review identified three likely contributors to early turnover: expectations formed in recruiting, weak adjustment in the first weeks and a demanding job with little discretion. This week, Tanisha Brooks examined her own center's data to see which contributors appear locally, when departures happen and how they vary across teams.

Data Sources and Permissions

Tanisha used three sources, all approved by her capstone sponsor and the HR director. First, HR supplied a deidentified file for the 210 agents hired last year, showing hire date, training class, team, shift, separation date and separation type. Second, the HR office shared 64 exit survey comments with names removed. Third, she conducted fifteen confidential interviews of twenty to thirty minutes: nine with agents hired within the past six months, none from her own team, and six with former agents who agreed to talk by phone. She took notes rather than recording, read them back to each participant and stored them on an encrypted drive.

When Agents Leave

Of the 210 agents hired, 101 left voluntarily within twelve months, a rate of 48 percent. Plotting departures by week of tenure revealed a sharp pattern. Eleven agents, 5 percent of hires, left during the eight-week classroom training. Then 48 left in weeks nine through twelve, the first month on live calls, and another 11 left in weeks thirteen through sixteen. After that, departures slowed to one or two a month. In other words, nearly half of all first-year quits happened in the four weeks after agents moved from the classroom to the phones.

Weeks 1-8, in the classroom: 11 voluntary departures, 11 percent of first-year quits.

Weeks 9-12, the first month live: 48 departures, 48 percent.

Weeks 13-16: 11 departures, 11 percent.

Weeks 17-52: 31 departures, 31 percent.

Coding the Interviews and Comments

Braun and Clarke (2006) set out a staged way to find patterns in words rather than numbers. The analyst reads everything several times, labels each passage that seems to matter, sorts the labels into rough groupings, tests whether each grouping holds up when the whole set is reread, gives the survivors clear names and boundaries and finally reports them with quotations. Tanisha followed those phases with her interview notes and the exit comments, coding 212 passages. She asked a coworker in the training department to code a sample of thirty passages independently, and they agreed on most codes, discussing the rest until they reached agreement.

Four Themes

The first theme, the shock of the first live week, appeared in twelve of fifteen interviews. Agents described training as polite role plays and then, on the floor, a stream of callers facing disconnection. One former agent said, "My second day live, a woman cried because her power was getting shut off and her kids were home. Training never mentioned that."

The second theme was nobody to ask. Newcomers sit wherever there is a free desk, and team leads may be on another row. Several said they put callers on hold for minutes while searching the knowledge base, which hurt their handle time.

The third theme was getting the worst schedules. The center fills evening and weekend shifts with the newest agents, so newcomers rarely work alongside their training classmates or their own lead.

The fourth theme was handle time pressure. Agents are coached on average handle time from their first day on the floor, and several described being warned in their second week.

Forty-eight of the 101 first-year quits happened in the four weeks after agents left the classroom.

Reading the Pattern Through Theory

Lee and Mitchell (1994) argued that many people do not quit after gradual dissatisfaction alone; instead, a jolting event prompts them to consider leaving, and some leave quickly, especially when the event clashes with their expectations or values. The cluster of departures in the first live month and the stories of a first terrible call fit this view: the move to the phones works as a predictable shock.

Bakker and Demerouti (2007) described how every job carries its own mix of demands that drain energy and resources that help people cope, grow and stay engaged, and how strain rises when demands outrun resources. New agents face heavy demands, including emotional calls, unfamiliar systems and handle time targets, while lacking key resources: a nearby lead, peers from their class and experience with hard calls. The schedule practice removes social resources exactly when demands peak.

Differences Across Teams

First-year quit rates varied across the center's sixteen teams, from 22 to 71 percent. Teams with lower rates tended to have leads who held weekly one-on-one check-ins with newcomers and seated them near experienced agents. However, those teams also worked more day shifts. Because shift and leadership practices overlap, Tanisha cannot say which matters more, and she reports the pattern as a lead for further study rather than proof.

Matching Findings to the Literature

Allen et al. (2010) reviewed common misconceptions about retention and concluded that pay is only one driver, that the people who leave are often not those managers expect and that early relationships, job design and supervisor support are among the levers organizations can control. Tanisha's findings agree. Pay came up in only seven of 64 exit comments, while support, scheduling and the shock of live calls dominated. Expectations from recruiting appeared less than expected: most agents knew the job would be hard, but not how emotional it would be.

Limitations

Fifteen interviews is a small number, and the six former agents willing to take a call could have left for different reasons than the ninety-five who never answered. Only a third of departing agents completed exit surveys. The team comparison is confounded by shift. And the data describe one year; a hiring surge last spring may have strained training resources.

Conclusion

The analysis shows that early turnover at the center is concentrated in the first month on live calls and is linked to a sudden jump in job demands combined with a loss of support. The findings point to three levers for Week 4: a gradual transition from classroom to the floor, protected access to help and peers in the first ninety days and coaching that delays handle time targets until agents are ready.

References

Allen, D. G., Bryant, P. C., & Vardaman, J. M. (2010). Retaining talent: Replacing misconceptions with evidence-based strategies. Academy of Management Perspectives, 24(2), 48-64. https://doi.org/10.5465/amp.24.2.48

Bakker, A. B., & Demerouti, E. (2007). The Job Demands-Resources model: State of the art. Journal of Managerial Psychology, 22(3), 309-328. https://doi.org/10.1108/02683940710733115

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa

Lee, T. W., & Mitchell, T. R. (1994). An alternative approach: The unfolding model of voluntary employee turnover. Academy of Management Review, 19(1), 51-89. https://doi.org/10.5465/amr.1994.9410122008