| Course | BUS 496 Adv. Experiential Learning for Business |
|---|---|
| Module | Module 5 |
| Paper type | undergraduate assignment analyzing a client's data to explain a business problem |
| Length | About 1,000 words, 6 pages |
| Format | APA 7 student paper |
| School | Southern New Hampshire University |
| Program | BS Business Administration |
| Updated | October 2026 |
Free sample paper for BUS 496 Module 5
Quote Conversion Analysis, January to November 2025
[Student Name]
Southern New Hampshire University
BUS 496: Adv. Experiential Learning for Business
Module Five Assignment
[Instructor Name]
[Date]
The organization, setting and figures below are a composite written as a model document. No real employer, client, colleague or patient is described.
Quote Conversion Analysis, January to November 2025
Introduction
The project asks why only about 14 percent of the company's online quote requests become bookings and what would raise that rate. The status memo reported three early findings: faster quotes book more often, incomplete requests delay quotes and airport transfers rarely book. This paper tests those findings with the full data and adds what customers said when called.
The Data
The analysis uses 1,940 quote requests submitted through the website between January and November 2025, of which 1,912 were matched to booking records in the dispatch software using email address and trip date. For each request, the data include the customer type selected on the form, which of the three key form fields were left blank, the time the request arrived and the time the quote email was sent. Response time was measured in business hours. In the spirit of exploring data plainly before modeling it (Tukey, 1977), the first step was to tabulate conversion by each factor on its own. Customer names and contact details were replaced with numbers in the analysis file.
Response Time
Conversion by response time
| Response time | Requests | Booked | Conversion |
|---|---|---|---|
| Within 4 hours | 398 | 107 | 27% |
| 4 to 24 hours | 541 | 96 | 18% |
| 1 to 2 days | 487 | 63 | 13% |
| More than 2 days | 486 | 34 | 7% |
| All | 1,912 | 300 | 16% |
The overall conversion in the matched data is 16 percent, slightly above the 14 percent the owner estimated, because the unmatched requests were mostly unbooked. Conversion falls at every step as response time lengthens.
Is It Really Speed?
An alternative explanation is that school groups, which tend to book, are answered faster because the office manager knows their trips are worth more, so speed might simply reflect who is asking. To check, conversion by response time was compared within each of the four main customer types.
Conversion within customer types, fast versus slow quotes
| Customer type | Answered within 24 hours | Answered after 24 hours |
|---|---|---|
| School groups | 31% | 14% |
| Weddings | 26% | 11% |
| Sports teams | 22% | 10% |
| Corporate | 17% | 7% |
The gap appears in every group, roughly halving conversion when quotes take more than a day. Speed is not just a proxy for customer type. Zeithaml et al. (1996) found that responsiveness is among the service qualities that shape whether customers buy, and these results are consistent with that.
Completeness, Type and Season
Requests missing a trip date, group size or pickup location, 38 percent of the total, converted at 9 percent against 17 percent for complete requests, largely because each missing item required an email exchange that added about a day. Airport transfers converted at 6 percent, against 22 percent for school groups; these are small, price-sensitive trips that customers often book online elsewhere within hours. Season mattered less than expected: conversion was similar in spring and fall, though response times were longest in the busy weeks of April, May and October.
What Lost Customers Said
Sixty customers who requested a quote and did not book were reached by phone. Their main reasons were: 41 percent booked with another company that quoted first, 27 percent found the price too high, 18 percent canceled or changed their trip and 14 percent gave other reasons, such as needing a smaller vehicle. Several said they had sent the same request to three or four companies and booked the first reasonable quote. Price matters, but it was the main reason for barely a quarter of lost customers.
How the Findings Fit Together
Taken together, the findings describe a single process problem more than four separate ones. Quotes are slow because the office manager prepares them by hand in the afternoons after dispatch, and incomplete requests make them slower still, because each missing detail requires an email and a wait for the customer's reply. Customers who send the same request to several companies book the first credible answer, so every hour of delay hands business to a competitor. Airport transfers, which customers can book online elsewhere in minutes, are the extreme case of the same pattern: by the time the company replies, the customer has already booked. Price matters, but mainly for a minority of customers who would have shopped on price whatever the speed.
This reading matters for the recommendations. If the main cause were price, the company would need to cut fares or accept lower volume. Because the main cause appears to be speed, the company can improve conversion by changing how quotes are produced, which costs far less than cutting prices and does not reduce the revenue from customers who would have booked anyway.
What the Office Manager Added
After seeing the full tables, the office manager confirmed that the slowest quotes were usually for trips needing two buses or overnight stays, which require checking driver hours and hotel availability. Those are also among the most valuable bookings. She suggested that a simple mileage and driver-hours calculator would cut the time she spends on each quote by more than half, and that a dispatcher could handle routine quotes in the mornings when dispatch is quieter. Her suggestions shaped the recommendations in the final report and, I think, made her more willing to support them.
Limits
The analysis shows associations, not proof that faster quotes cause more bookings, though the within-group comparisons and the customers' own explanations make speed a strong candidate. Sixty calls are a modest sample, and customers who agreed to talk may differ from those who did not. Requests by phone are excluded, and those callers may behave differently from people who use the website. Knaflic (2015) advises analysts to state their confidence plainly, and the confidence here is moderate to high for speed and completeness, lower for price.
Conclusion
Speed is the strongest factor in whether a quote request becomes a booking, and the effect holds within every customer type. Incomplete requests slow quotes and lower conversion. Airport transfers rarely book. Price matters for about a quarter of lost customers. The final report will turn these findings into recommendations, each with a rough estimate of the bookings it could add.
References
Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. Wiley.
Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley.
Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46. https://doi.org/10.1177/002224299606000203
What the BUS 496 Module 5 instructions ask for
The Module Five assignment in BUS 496 asks you to analyze data from your client organization to answer the project question. Expect to explain where the records came from and how you tidied them, run comparisons or other analysis, present results in tables or charts, interpret them and note limitations. Strong submissions go beyond describing patterns to test whether a pattern might be explained by something else, use qualitative evidence such as interviews alongside the numbers and state clearly how confident the reader should be. They keep the client's question at the center and handle client data responsibly. Some versions also ask you to describe any calls or interviews you conducted.
How this BUS 496 Module 5 data analysis assignment example is built
The paper analyzes 1,912 quote requests matched to booking outcomes. Conversion falls steadily with response time, from 27 percent within four hours to 7 percent after two days. Because school groups both book more often and tend to be answered faster, the paper checks the speed effect within each customer type and finds it holds in all four main groups. Requests missing a trip date, group size or pickup location converted at 9 percent against 17 percent for complete ones. Airport transfers converted at 6 percent. Of sixty lost customers called, 41 percent booked elsewhere because they got a faster quote, 27 percent cited price, 18 percent canceled their trip and 14 percent gave other reasons.
Where the BUS 496 Module 5 rubric puts the points
The rubric for the analysis usually weighs data description and preparation, appropriateness of the analysis, accuracy, clarity of tables, interpretation, consideration of alternative explanations, use of qualitative evidence and limitations. Strong papers check whether a pattern survives when another factor is held constant, combine numbers with what customers or staff said, and avoid claiming more than the evidence allows. Papers lose credit for presenting raw output without interpretation, for ignoring obvious alternative explanations, for skipping limitations and for including identifiable customer information. Instructors also reward a section that ties the findings into one explanation the client can act on. Including the views of the people who run the process usually strengthens the interpretation.
BUS 496 Module 5 help: the mistakes that cost points
The most valuable step in a client data analysis is often the check you were not asked to do: does the pattern hold when you account for another factor? If the fastest-answered requests are also the easiest customers, speed may look more important than it is, so compare within groups. Add what people told you, since numbers show what happened and conversations suggest why. State limitations directly, such as a small number of calls or missing data. Keep customer identities out of anything you submit. Show the client's staff the results before the final report; they often explain patterns the data cannot.
Get BUS 496 Module 5 written to your instructions
Send the BUS 496 Module 5 assignment and your client data or scenario. The paper will describe and clean the data, compare groups, test alternative explanations and report limits plainly. About two days; a first paper is free. The paper above is an original model document written by our desk, not a submitted student paper and not an official Southern New Hampshire University document.
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BUS 496 Module 5 questions, answered
Where can I find a free BUS 496 Module 5 Data Analysis sample?
This page includes a complete BUS 496 Module 5 analysis of a charter bus company's quote requests.
How do you check whether a pattern in business data is real?
By seeing whether it holds within subgroups or after accounting for other factors that might explain it, such as customer type or season.
Why combine data analysis with interviews?
Because records show what happened, while interviews and calls suggest why, and the two together give a more convincing explanation.
What limitations should a client data analysis report?
Missing or unmatched records, small samples for interviews or calls, factors that could not be measured and whether the results show cause or only association.
How should client data be handled in a student project?
Stored on the client's systems, anonymized in anything submitted for grading and used only for the agreed purpose.