D435 Task 2 People Analytics Report Example

First-Year Nurses on Nights: A People Analytics Report That Reads Turnover Data and Exit Interview Comments Together at a Composite Health System

Student Name

School of Business, Western Governors University

D435: HR Technology and People Analytics, Task 2

Course Instructor

Month Day, Year

First-Year Nurses on Nights: A People Analytics Report That Reads Turnover Data and Exit Interview Comments Together at a Composite Health System

The Question

Bayview Health, a composite health system with 2,050 registered nurses, saw nurse turnover rise from 15% to 20% over two years. Executives asked HR a practical question: where is turnover concentrated, and why? With its new HR information system, Bayview can now combine employee records, scheduling data and exit interview responses for the first time. People analytics, the use of workforce data and analysis to inform decisions about people, promises this kind of insight, but a review of the research on HR analytics found that evidence of its adoption and impact remained limited, and that its value depends on asking business questions and linking data to decisions (Marler & Boudreau, 2017). This report is built around one question and ends with actions.

Metrics and Definitions

Turnover rate is calculated as the number of nurses who separated during the 12-month period divided by the average number of nurses employed during that period, multiplied by 100. Voluntary turnover includes only resignations and retirements, excluding terminations. First-year turnover is the share of nurses hired during a period who leave within 12 months of hire. Rates are calculated for the most recent 12 months, with 410 separations, 352 of them voluntary.

Quantitative Findings

Voluntary turnover varied sharply by tenure and shift.

First-year nurses on night shift left at twice the rate of first-year nurses on days and nearly four times the rate of experienced nurses. Although they made up 8% of the nursing workforce, they accounted for 19% of voluntary separations. Two further patterns stood out. Among first-year night nurses, 71% of departures came in months four through nine, the stretch that begins once orientation is over. And units where schedules were posted fewer than 14 days in advance had first-year turnover of 38%, compared with 24% on units that posted four weeks ahead.

The numbers have limits. Some groups are small, so rates can move sharply with a few departures; the analysis covers one year; and associations, such as the one with schedule posting, do not prove cause. The numbers show where turnover is concentrated, but not why.

Qualitative Findings

To understand why, HR analyzed 96 exit interview comments from first-year nurses and conducted 20 stay interviews with first-year night nurses who remained. Comments were coded into themes by two HR analysts working independently, who then reconciled differences.

Four themes emerged. The most common, in 58% of exit comments, was feeling alone at night: new nurses described being the least experienced person on a unit with no educator, fewer charge nurses and difficulty reaching physicians. The second, in 44%, was unpredictable schedules posted late, making child care and school impossible to plan. The third, in 31%, was the abrupt end of orientation: preceptor support stopped at week 12, often just as nurses began taking full patient loads. The fourth, in 22%, was a promise not kept: several said they had been told they could move to days after six months, but day positions rarely opened. Stay interviews confirmed the first three themes and added what helped: an experienced night charge nurse who checked in, and a peer group of other new graduates.

What the Two Kinds of Data Show Together

Together, the data tell a consistent story. The numbers locate the problem, first-year night nurses, and its timing, the months after orientation ends. The comments explain it: isolation at night, late schedules and support that stops too soon. The association between late schedule posting and turnover in the numbers is supported by what nurses said. Research on newcomers is consistent with this picture; Bauer et al. (2007), pooling 70 samples of new employees, found that how clearly newcomers understand their role, how confident they feel and how accepted they are by coworkers carry much of the effect of socialization on whether they stay. Turnover research also notes that departures reflect factors employers can influence, including the job itself and how people are treated (Hom et al., 2017).

Data Quality and Ethics

Two cautions shaped how the analysis was done. First, data quality: before the new system, some transfers between units were recorded as separations and rehires, which would have inflated turnover. The team matched employee identification numbers across systems and removed 23 internal transfers from the separation count. Exit interviews were completed by 61% of departing first-year nurses, so the comments may not represent those who left without an interview, and stay interviews were added partly to fill that gap. Second, ethics: the analysis reports results only for groups of at least ten nurses, so no individual can be identified, and comments were stripped of names and unit details before coding. Managers receive their unit's turnover data but not individual exit comments. These safeguards matter because people analytics depends on employees' trust; if nurses believed their candid comments could be traced back to them, future exit and stay interviews would become far less useful.

Recommendations

First, extend support beyond orientation: pair each first-year night nurse with a night preceptor or resource nurse through month nine, and add a night-shift clinical educator at each hospital.

Second, post schedules at least four weeks ahead on every unit, using the new system's scheduling module, and track compliance on managers' dashboards.

Third, create a new graduate peer cohort that meets monthly, with sessions scheduled for night staff.

Fourth, make only promises the system can keep: tell new hires honestly how long day positions typically take to open, and create a transparent transfer list.

HR will track first-year night turnover monthly, with a target of cutting it from 42.5% to 25% within 18 months, and repeat the stay interviews in six months.

Conclusion

Bayview's rising nurse turnover is concentrated among first-year nurses on night shift, in the months after orientation ends. Exit and stay interviews explain why: isolation, late schedules and support that stops too soon. Neither kind of data alone would have produced targeted actions. Read together, they point to specific, affordable changes and a clear measure of success.

References

Bauer, T. N., Bodner, T., Erdogan, B., Truxillo, D. M., & Tucker, J. S. (2007). Newcomer adjustment during organizational socialization: A meta-analytic review of antecedents, outcomes, and methods. Journal of Applied Psychology, 92(3), 707-721. https://doi.org/10.1037/0021-9010.92.3.707

Hom, P. W., Lee, T. W., Shaw, J. D., & Hausknecht, J. P. (2017). One hundred years of employee turnover theory and research. Journal of Applied Psychology, 102(3), 530-545. https://doi.org/10.1037/apl0000103

Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR analytics. The International Journal of Human Resource Management, 28(1), 3-26. https://doi.org/10.1080/09585192.2016.1244699