| Course | QNT 375 Business Data Analytics (QNT/375) |
|---|---|
| Week | 5 |
| Paper type | Analytics conclusions and presentation |
| Length | about 1,011 words, 4 double-spaced pages plus title page and references |
| Format | APA 7 student paper |
| School | University of Phoenix |
| Program | BS in Business |
| Updated | October 2026 |
Free sample paper for QNT 375 Week 5
From Findings to a $600,000 Decision: Presenting Analytics Conclusions to a Car Wash Chain's Leaders
[Student Name]
University of Phoenix
QNT/375: Business Data Analytics
Week 5 Assignment
[Instructor Name]
[Date]
Summit Shine Car Wash, its leaders, data and results are composites written for a model paper.
Over four weeks, this course has followed an analysis for Summit Shine, the invented Colorado car wash company, whose monthly membership losses climbed by more than two points in under a year. The board has approved $600,000 for retention and will decide how to spend it at its next meeting. This paper presents the conclusions and recommendations in the form the board will receive them, with commentary on the choices made.
The Answer First
Summary for the board: Cancellations rose for three main reasons. Members whose washing dropped off were left alone until they canceled; a competitor took members near its five new sites; and the spring price increase pushed out members on the basic plan. Long waits mattered less than expected. We recommend spending most of the $600,000 on reaching members whose usage is falling, a targeted response near competitor sites and a revised basic plan, and testing each so results can be measured within six months.
Conclusions by Research Question
RQ1, patterns: cancellation is highest among new members, basic plan members and members near competitor sites.
RQ2, drivers: a drop in washing is the strongest predictor, tripling the odds of canceling; competitor proximity raises odds about 40 percent; waits have a small effect.
RQ3, events: the competitor's openings added about 1.6 points to monthly cancellations at nearby sites; the price increase raised basic plan cancellations by about a quarter.
RQ4, prediction: the risk model captures 46 percent of next-month cancellations in its top 20 percent of members.
Recommendations and Costs
Usage outreach, $260,000: each month, contact the top 20 percent of members by risk score with a personal message and an incentive such as a free premium wash, testing two incentive levels.
Competitor response, $150,000: at the seven nearby sites, offer a loyalty price lock for members of more than a year and promote features the competitor lacks, such as free vacuums and towel service.
Basic plan revision, $120,000 in forgone revenue: add a lower-frequency plan at $19 for members who wash twice a month, retaining some who would otherwise leave.
Wait time fixes, $70,000: an extra attendant on weekends at the two sites with the longest waits.
Expected effect: reducing monthly cancellations by about one point within six months, worth roughly $230,000 a year in retained revenue for each point sustained, more as retained members stay longer.
Choosing the Charts
Cleveland and McGill (1984) studied how accurately people judge quantities in graphs and found that readers judged values most accurately when they compared positions on a shared axis, followed by length, angle and area, with color and shading least accurate. They recommended designs that use position, such as dot and bar charts, over pie charts. Tufte (2001) urged designers to maximize the share of ink devoted to data and remove decoration that does not inform.
Three charts carry the board presentation: a line chart of monthly cancellation rates over two years, showing the unusual rise; a bar chart comparing cancellation rates across the five drivers, sorted from largest to smallest; and a two-line chart of nearby and distant locations before and after the competitor's openings, which shows the difference-in-differences visually. All other tables move to an appendix.
The board will see three charts; the other forty tables are there if anyone asks.
Anticipating the Board's Questions
Board members are likely to ask three questions, and the presentation answers each. Why not cut prices across the board? Because only basic plan members showed price sensitivity, a general cut would give money away to members who would stay anyway. Why not open a location near the competitor? That is a capital decision outside the retention budget, though the competitor findings could inform it. What if the outreach does not work? The test design will show within three months, and unspent funds can shift to whichever action performs best.
Limitations
The analysis has limits. Falling usage predicts cancellation but may reflect many causes; outreach may not change behavior for members who moved or changed commutes. The competitor estimate rests on comparing locations that may differ in ways no record shows, such as local traffic changes. Survey reasons come from those willing to respond. The model was tested on four months; its accuracy may change as conditions shift.
What We Would Do With More Time
Two further analyses would strengthen the conclusions. First, following members who receive outreach for a full year would show whether retention lasts or merely delays cancellation by a month or two. Second, interviews with members who canceled and later rejoined could reveal what brought them back, which may suggest better offers. Both can begin alongside the tests described below and would cost little, since the data already exist or can be gathered by phone in a few weeks.
Measuring Results
Each recommendation will be tested: risk-scored members will be randomly split into outreach and comparison groups; the loyalty price lock will start at four of the seven nearby sites; the new plan will launch at half the locations first. Cancellation rates will be compared monthly, and the board will review results after six months before committing next year's budget.
Reflection on the Process
The most important step in this project was the first one: turning "figure out what is going on" into a decision about a budget. Davenport and Harris (2017) stress that analytics earns its keep when it informs specific decisions, and this project bears that out. The most surprising finding came from checking data quality: the cancellation reason field, which managers trusted, proved unreliable. And the most persuasive evidence was the simplest comparison, nearby against distant sites, not the most complex model.
Conclusion
The analysis answers the board's question with evidence and a plan. Cancellations rose because of falling usage, a competitor and a price increase, not mainly because of waits. The recommended budget targets those causes, the charts present the evidence clearly and the tests built into each recommendation will show within six months whether the money is working.
References
Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), 531-554. https://doi.org/10.1080/01621459.1984.10478080
Davenport, T. H., & Harris, J. G. (2017). Competing on analytics: The new science of winning (Updated ed.). Harvard Business Review Press.
Tufte, E. R. (2001). The visual display of quantitative information (2nd ed.). Graphics Press.
What the QNT 375 Week 5 instructions ask
The final QNT 375 assignment usually asks students to present the conclusions of a data analysis. Prompts may ask students to summarize findings, draw conclusions tied to the research questions, make recommendations, choose effective charts and tables, discuss limitations and suggest further research, sometimes in the form of an executive report or presentation. A few prompts add a reflection on what the analytics process taught. Write for a specific decision maker, connect each recommendation to evidence from the analysis and cite the textbook and research on data presentation in APA. Close with how the organization will measure whether the recommendations worked and when it will review them.
How this QNT 375 Week 5 example is built
Our model paper opens with a one-paragraph answer for the board: cancellations rose mainly because members who stopped washing regularly were left alone, because a competitor took members near its new sites and because the price increase pushed basic plan members out, while waits mattered less than assumed. It recommends splitting the $600,000 among usage-based outreach to members flagged by the risk model, a targeted offer near competitor sites, a revised basic plan and modest wait-time fixes at two sites. Three charts, chosen using research on how accurately people read different graphs, carry the evidence. A limitations section states what the data cannot show, and a test-and-measure plan sets out how results will be judged within six months.
QNT 375 Week 5 grading rubric: where the points go
Instructors reward conclusions written for decision makers. Strong papers lead with the main answer, tie each conclusion to evidence and research questions and make specific recommendations with costs and expected effects. Credit goes to choosing a small number of clear charts or tables suited to the message, to stating limitations and uncertainty honestly and to a plan for measuring results. Graders also value reflection on what the analysis process taught and what further research would help. Graders also check that charts are few, simple and directly tied to the recommendations. Plain business language, a logical structure and APA citations complete a strong final paper.
QNT 375 Week 5 help: mistakes to avoid
Conclusion papers often repeat every result from the analysis in order, leaving the reader to find the answer. Start with the answer and the recommended decision. Another frequent gap is overstating certainty; a strong association is not proof of cause. Say how confident you are. Students also include too many charts or choose charts that are hard to read, such as 3-D pies. Use a few simple ones. Some papers recommend actions without costs or expected effects; add them. Finally, propose how results will be measured, ideally with a test group, so the decision can be revisited. A tutor can help you choose which findings belong in the summary and which in an appendix, and test whether a reader can find the answer in one minute.
Related QNT 375 sample papers
Other QNT 375 week samples
- QNT 375 Week 1: Framing a Business Problem
- QNT 375 Week 2: Research Design and Data Collection
- QNT 375 Week 3: Validity and Reliability
- QNT 375 Week 4: Analyzing Patterns and Trends
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QNT 375 Week 5 questions, answered
What does QNT 375 Week 5 usually cover?
It usually covers presenting conclusions from a data analysis: summarizing findings, making recommendations, choosing effective charts, stating limitations and planning follow-up.
Where can I find a free QNT 375 Week 5 sample paper?
The Week 5 paper above presents analytics conclusions to a car wash chain's leaders, and the full paper is available here free.
How should data findings be presented to executives?
Lead with the answer and the decision, support it with a few clear charts and key numbers, state uncertainty and put details in an appendix.
Which charts are easiest to read accurately?
Research on graphical perception suggests that charts comparing positions along a common scale, such as bar and dot charts, are read more accurately than pie charts or area-based displays.
Why include limitations in an analytics report?
Limitations tell decision makers how much confidence to place in findings and where further data or testing could change the conclusions.
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