HRM 7640 Module 4 Final Project: An HR Analytics Plan That Links Activity to Outcomes Example

From Activities to Outcomes: A Two-Year HR Analytics Plan for Shoreline Home Health

Student Name

MBA in Human Resource Management, Thomas Edison State University

HRM-7640: Technology, Data, and Analytics as Change Agents

Mentor Name

Month Day, Year

From Activities to Outcomes: A Two-Year HR Analytics Plan for Shoreline Home Health

Executive Summary

Shoreline Home Health employs about 1,100 home health aides serving patients in Ocean and Monmouth counties. Its strategy depends on continuity of care, since patients and referring hospitals value aides who stay, and on growth, since demand for home care is rising faster than the supply of aides. Early turnover undermines both. This plan uses HR analytics to link HR activities, such as mentoring, scheduling and pay, to HR outcomes, such as retention, and to business outcomes, such as continuity of care and patient satisfaction. Over two years it aims to reduce ninety-day turnover from 38 to 28 percent and to make evidence a routine part of HR decisions.

Strategic Objectives

The agency's leadership has set three objectives for the next two years: improve continuity of care, measured by the share of patients served by the same aide for at least three months; grow the number of patients served by 15 percent; and maintain patient satisfaction above 90 percent. Each depends on recruiting and keeping aides. HR analytics is justified only if it helps meet these objectives.

The objectives also define what analytics should not do. It should not become a research program that produces interesting findings with no link to these goals, and it should not be used to monitor individual aides in ways that would damage trust. Each analysis in the plan must answer a question that, if answered, would change a decision about recruiting, onboarding, scheduling, pay or supervision.

The HR Value Chain

Analytics creates value when it shows how HR activities affect outcomes that matter to the business. The plan uses a value chain that links three levels.

Fitz-enz and Mattox (2014) stress that predictive analytics should connect HR investments to organizational results; the value chain makes those connections explicit and testable.

Priority Questions

The plan focuses on four questions, chosen for their link to the objectives and the availability of data. First, which new aides are at highest risk of leaving within ninety days, and which interventions reduce that risk? Module 3's logistic model provides a starting answer. Second, does peer mentoring cause better retention, or are mentored aides different? A randomized pilot will answer this. Third, how do commute distance and scheduling affect retention and attendance? Fourth, which recruiting sources produce aides who stay and receive good patient feedback?

These four questions were selected from a longer list that also included predicting patient complaints, forecasting demand by territory and measuring supervisor effectiveness. Those questions matter but were deferred because the data are weaker or because the decisions they would inform are less urgent. Focusing on four questions keeps the first two years manageable for a half-time analyst and gives leaders early, usable answers that build support for later work.

Data and Methods

Data will come from the payroll, scheduling and applicant tracking systems, linked through a common employee identifier established by the data governance group from Module 2.

Governance and Ethics

A small analytics steering group, chaired by the director of operations and including HR, finance and a field supervisor, will approve questions, review results and decide actions. Marler and Boudreau (2017) emphasize that analytics affects decisions only when it is connected to leaders who use it; the steering group provides that connection. Ethical rules will govern all work: data use will be explained in a statement to employees; access to identifiable data will be limited; predictions will be used to offer support, not to screen applicants or discipline employees; and every model will be checked for accuracy and outcomes across demographic groups before use.

Governance also includes a decision log. When the steering group acts on an analysis, it records the question, the finding, the decision and the expected effect, and it returns to the log six months later to see whether the effect occurred. This simple practice builds the habit of using evidence and lets the agency learn which pieces of analysis actually improve results. The steering group also follows the advice of Minbaeva (2018), who argues that analytics becomes credible only when data quality, analytical skill and the ability to act on findings develop together.

Implementation Roadmap

Each stage depends on the previous one. Without clean, linked data, the model cannot be trusted; without the pilot, the agency cannot know whether mentoring causes better retention; and without supervisors' dashboard, the findings will not reach the people who schedule aides and coach them. The roadmap therefore treats data governance and the pilot as the foundation for everything that follows.

Measures of Success

Cost and Return

The plan requires a half-time data analyst position, analytics software and the mentoring stipends, about $140,000 a year. If ninety-day turnover falls by ten points, the agency would avoid about 50 early departures a year from its roughly 500 annual hires. At an estimated $3,500 per early departure in recruiting, training and lost productivity, savings would be about $175,000 a year, before counting improvements in continuity of care and growth.

Conclusion

This plan connects HR analytics to Shoreline's strategy through a value chain that links mentoring, scheduling, pay and recruiting to retention, continuity of care and patient satisfaction. With four priority questions, sound methods, governance, ethical rules and a roadmap, the agency can move from monthly turnover reports to decisions that measurably improve its ability to serve patients.

References

Fitz-enz, J., & Mattox, J. R., II. (2014). Predictive analytics for human resources. John Wiley & Sons.

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

Minbaeva, D. B. (2018). Building credible human capital analytics for organizational competitive advantage. Human Resource Management, 57(3), 701-713. https://doi.org/10.1002/hrm.21848