| Course | GB546 Talent Acquisition and Retention |
|---|---|
| Unit | Unit 7 |
| Paper type | Recruiting funnel analysis |
| Length | About 920 words, 6 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | MBA |
| Updated | October 2026 |
Free sample paper for GB546 Unit 7
From 2,960 Applications to 214 Operators at Day Ninety: A Unit 7 Recruiting Funnel Analysis
[Student Name]
Purdue University Global
GB546: Talent Acquisition and Retention
Unit 7 Recruiting Funnel Analysis
[Instructor Name]
[Date]
The plant, its funnel and every count are composite and illustrative; the sources cited are real.
Funnel Table
Each stage gives the count, the conversion from the previous stage and the number lost.
1. Applications: 2,960.
2. Past the knockout screen: 1,540; 52.0 percent; 1,420 lost.
3. Completed the online assessment: 910; 59.1 percent; 630 lost.
4. Interviewed: 612; 67.3 percent; 298 lost.
5. Offered: 402; 65.7 percent; 210 lost.
6. Accepted: 338; 84.1 percent; 64 lost.
7. Cleared background and drug screens: 301; 89.1 percent; 37 lost.
8. Started: 262; 87.0 percent; 39 lost.
9. Employed at day ninety: 214; 81.7 percent; 48 lost.
The funnel is carried to day ninety because a new operator who leaves in the first weeks must be recruited again, and the reasons often trace back to how the funnel worked.
Designed and Unintended Drops
Largest is defined here as the largest unintended loss, because some stages are meant to remove people. The knockout screen removes the most, 1,420, but by design: it rejects applicants who cannot work twelve-hour shifts, are not authorized to work or cannot pass cleanroom gowning requirements. The interview and offer stages also remove people by design, since selecting is their purpose.
The unintended drops are the ones where qualified, interested people leave the process: the assessment, offer declines, screen failures and no-shows before the start date, and departures before day ninety. Ranked by absolute count, the assessment is the largest by far: 630 applicants who passed the knockout screen, 40.9 percent of those invited, never finished it.
The stage that loses the most people is not the problem; the stage that loses the most people the plant wanted is.
Why the Assessment Loses So Many
The assessment vendor records the device each applicant used. The breakdown explains the loss.
Invited on a phone: 1,020 applicants; 470 completed; 46.1 percent.
Invited on a desktop or laptop: 520 applicants; 440 completed; 84.6 percent.
Two-thirds of applicants opened the assessment on a phone, and fewer than half of them finished. The assessment was built for a desktop screen, with drag-and-drop items and a timed section that does not resize well. Applicants for hourly roles are more likely to apply by phone, so a desktop-first assessment screens out people for their device rather than their ability. Breaugh (2008) notes that the applicant's experience of the hiring process influences whether qualified candidates stay in it, and a form that does not work on the device in an applicant's hand is about as poor an experience as a process can offer.
Projection
If mobile completion rose from 46.1 to 70 percent, about 714 phone applicants would finish instead of 470, adding about 244 completers a quarter. Downstream, 214 of 910 completers, about 23.5 percent, are still employed at day ninety. Applying that rate, the extra completers would add roughly 57 operators employed at day ninety each quarter, against the 96 the plant now needs to hire every quarter.
This assumes the extra completers are as qualified as current completers. Some applicants who quit the mobile assessment may have been less committed, so the projection is an upper estimate, and the plant should measure the actual downstream rate after the fix.
What the Funnel Says About the Hiring Target
The plant needs about 96 operators a quarter. Today the funnel produces 262 starts and 214 operators at day ninety from 2,960 applications, so applications are not the constraint: the plant already attracts far more applicants than it needs. The constraint is the share of qualified applicants who make it through the process and stay. That changes where recruiting money should go. Spending more on advertising would add applicants at the top of the funnel, most of whom the knockout screen would remove and many of whom would then quit the assessment. Fixing the assessment and the first three weeks would raise the yield of applicants the plant already has, at far lower cost. For a plant whose operator demand is rising with the second floor, improving yield is the cheaper path to the forecast.
Other Unintended Losses
Offer declines, 64. Exit surveys of decliners most often cite a competing offer received during the wait for a decision.
Screen failures and no-shows, 76. About half of the screen losses are drug test failures, which the plant does not intend to change; no-shows before the start date may reflect the gap between acceptance and the first day.
Departures before day ninety, 48. These cluster in the first three weeks, which the Unit 6 reflection links to the dropped gowning walkthrough and night-rotation preview. Hom et al. (2017) report that early departures often reflect a mismatch between what new hires expected and what they found, which fits that timing.
Recommendations
Rebuild the assessment for phones. Ask the vendor for a mobile-first version or replace the timed drag-and-drop section, and test completion by device for one month.
Track the downstream rate. Measure how many of the new completers are offered, start and stay, so the projection is replaced with a result.
Shorten the gap after acceptance. Offer start dates within a week where tool capacity allows.
Earnest et al. (2011) suggest that honest previews reduce early exits, so the restored walkthrough should also be tracked through day ninety.
References
Breaugh, J. A. (2008). Employee recruitment: Current knowledge and important areas for future research. Human Resource Management Review, 18(3), 103-118. https://doi.org/10.1016/j.hrmr.2008.07.003
Earnest, D. R., Allen, D. G., & Landis, R. S. (2011). Mechanisms linking realistic job previews with turnover: A meta-analytic path analysis. Personnel Psychology, 64(4), 865-897. https://doi.org/10.1111/j.1744-6570.2011.01230.x
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
GB546 Unit 7 instructions, in plain terms
GB546 Unit 7 asks you to analyze a recruiting funnel from application to an early-tenure milestone. Build a funnel table with each stage's count, conversion and loss. Define how you judge the largest drop, separating stages meant to remove people from those that lose people the organization wanted. Investigate the largest unintended loss with whatever data can explain it. Project what fixing it would add downstream, stating the assumption behind the projection. Discuss what the funnel says about where recruiting effort should go. Review the other unintended losses briefly. Recommend changes and how to measure them. Expect about five pages with a funnel table, a breakdown table and a projection, citing recruitment research in APA 7.
Inside the GB546 Unit 7 example
The table carries nine stages, from 2,960 applications through 1,540 past the knockout screen, 910 completers, 402 offers and 262 starts, to 214 at day ninety, chosen because early leavers must be recruited again. Designed drops are separated from unintended ones, and the highlighted sentence explains the difference. The device breakdown shows 1,020 phone and 520 desktop invitations, and Breaugh links process experience to applicant retention. The projection adds 244 completers and about 57 operators, labeled an upper estimate. A section argues that yield, not applicant volume, is the constraint, with Hom and colleagues on early departures, Earnest and colleagues on previews and three recommendations.
Reading the GB546 Unit 7 grading rubric
Instructors grading a GB546 funnel analysis look for a clear rule separating designed drops from unintended ones before the largest loss is named. A complete funnel table with conversions and losses earns points. Carrying the funnel past the start date shows understanding of early turnover. Using device or other data to explain the largest unintended loss shows evidence-based diagnosis. A projection with its downstream rate stated shows analytical skill. Labeling it an upper estimate shows honesty. Concluding that yield rather than volume is the constraint shows strategic insight. Recommendations with a way to measure them show follow-through. Breaugh, Earnest and colleagues, and Hom and colleagues form the APA 7 list.
GB546 Unit 7 help: mistakes that cost points
Many GB546 Unit 7 analyses name the stage that removes the most people as the problem, even when that stage is a knockout screen working as intended. Others end the funnel at the offer or start date, missing early departures that send hiring back to the beginning. A frequent gap is cause: a large drop at the assessment is a symptom, and the analysis should look for data, such as device type, that explains it. Some writers project improvements without stating the downstream rate. Build the full table, separate designed from unintended drops, explain the largest unintended loss, project carefully, consider where effort should go and recommend measurable fixes. Proposing data collection is legitimate when evidence is missing.
Get a GB546 Unit 7 example written to your instructions
Counts at each hiring stage are the core input, as the Unit 7 data gives them or as a requisition you can describe without naming anyone would show them, along with the rubric. The analysis returns inside 24-48h with drops classified, the largest leak explained by evidence and a projection stated conservatively. No fee for a first sample. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
More GB546 papers and MBA samples
- GB546 Unit 1: Discussion Board Post
- GB546 Unit 2: Workforce Demand Forecast
- GB546 Unit 3: Labor Market Scan
- GB546 Unit 4: Employer Value Proposition Brief
- GB546 Unit 5: Sourcing Channel Analysis
- GB546 Unit 6: Seminar Reflection
- GB546 Unit 8: Turnover Cost Model
- GB546 Unit 9: Retention Plan
- GB546 Unit 10: Staffing Business Case
- GB545 Unit 7: Incentive Plan Proposal
- GB542 Unit 7: Knowledge Transfer Plan
- GB541 Unit 7: Investigation Protocol
GB546 Unit 7 questions, answered
Why carry a recruiting funnel all the way to day ninety?
Because a hire who leaves in the first weeks must be recruited again, and the reasons often trace back to how the funnel worked. Ending at the start date hides that loss. The example carries the funnel to day ninety and notes where early departures cluster. If your data stops at hire, say so and explain what the missing stage might show.
How is the largest drop decided?
Define largest before measuring it. The knockout screen removes the most people, but intentionally, so the example separates designed drops from unintended ones and ranks only the second group. Some analyses rank by percentage lost, others by absolute count. State which your paper uses, since the choice can change which stage gets the attention.
Where would device data come from?
Most assessment and applicant tracking vendors record the device or browser used, and some report completion rates by device directly. In the example this data is composite. If your case lacks it, you can propose collecting it as part of the recommendation, which is a legitimate finding when the evidence needed to test a cause does not yet exist.
Where can I find a free GB546 Unit 7 sample paper?
This page carries a full GB546 Unit 7 recruiting funnel analysis tracing 2,960 operator applications to day ninety, with device data, a projection, recommendations and APA 7 references.
Why does the assessment lose so many applicants?
It was built for desktop screens, and two-thirds of applicants open it on a phone. Only 46.1 percent of phone users finish it, against 84.6 percent on a desktop.