| Course | BU 351 Introduction to Business Analytics |
|---|---|
| Unit | Unit 3 |
| Paper type | Descriptive analysis |
| Length | About 920 words, 6 pages |
| Format | APA 7 student paper |
| School | Herzing University |
| Program | BS in Business Analytics |
| Updated | September 2026 |
Free sample paper for BU 351 Unit 3
More Members, More Cancellations, Fewer New Sign-Ups: A Descriptive Analysis of a Climbing Gym Chain's 2025 Memberships Against 2024
Student Name
Business, Herzing University
BU 351: Introduction to Business Analytics
Instructor Name
February 18, 2026
More Members, More Cancellations, Fewer New Sign-Ups: A Descriptive Analysis of a Climbing Gym Chain's 2025 Memberships Against 2024
Purpose
This analysis takes up the first of the four questions from Unit 1 for Basecamp Climbing, the invented gym chain in this course: what happened to the size of the membership, to sign-ups and to the pace of cancellations between 2024 and 2025, for the chain and for each gym? It uses the cleaned file of 4,195 members from Unit 2. Every measure is defined before it is computed, every figure is set beside a comparison, and the analysis says nothing about why anything moved. Explanations belong to the exploratory work in Unit 4, since a summary that mixes description with guesses about causes invites a manager to act on the guesses (Provost & Fawcett, 2013).
Definitions
Active member on a date: a member whose join date is on or before that date and who has no cancellation date or a cancellation date after it. Active members are counted at the level of the individual member; a family plan counts as one membership.
New joins in a period: members whose join date falls in the period. The file covers sign-ups through November 30, 2025, so December 2025 joins are not in it; comparisons of joins therefore use January through November in both years.
Cancellations in a period: members whose cancellation date falls in the period.
Monthly cancellation rate: cancellations during a month among members who were active on the last day of the previous month, divided by the number of those members. Members who join and cancel within the same month are excluded from both numerator and denominator. The annual figure is the average of the twelve monthly rates.
Net change: active members at the end of a period minus active members at the start.
Two further choices shape every figure. First, counts are taken at the level of the membership, not the person; a family plan that covers three climbers is one membership. Second, the monthly rate uses the start-of-month base rather than an average of the start and end of the month, because the start-of-month base is fixed before anyone joins or leaves during the month, which keeps the rate from moving simply because many people joined.
The Year in Summary
The table compares the two years on the defined measures. The comparison period for joins is January through November.
| Measure | 2024 | 2025 | Change |
|---|---|---|---|
| Active members on December 31 | 1,459 | 1,590 | +131 (+9.0%) |
| Active members on November 30 | 1,429 | 1,675 | +246 (+17.2%) |
| New joins, January to November | 1,339 | 1,324 | minus 15 (minus 1.1%) |
| Cancellations, full year | 910 | 1,193 | +283 (+31.1%) |
| Average monthly cancellation rate | 5.83% | 5.90% | +0.07 points |
| Net change in active members, full year | +539 | +131 | minus 408 |
What the Summary Shows
Active memberships did not fall. Year-end membership reached 1,590, up 9 percent, and the November 30 count was 17 percent above its 2024 level. The year-end comparison understates growth somewhat, because December 2025 sign-ups are missing from the file, while December 2024 sign-ups, 110 of them, are included.
What changed is the balance between joining and leaving. New joins through November were almost flat, down 15. Cancellations rose by 283, or 31 percent. The average monthly cancellation rate barely moved, from 5.83 to 5.90 percent, so most of the rise in cancellations reflects a larger base of members rather than a higher rate of leaving. The result is that net growth fell from 539 members in 2024 to 131 in 2025. To an owner watching the cancellation reports each month, that pattern would look like slipping, even though the membership base grew.
A reader should also notice what the summary does not include. It gives no figures by plan or by age, because the descriptive question framed in Unit 1 asked about the chain and its gyms, not about types of members. Adding every available cut would bury the answer the owner needs under figures nobody asked for. Those cuts belong to exploration, where they can be tested as possible explanations rather than displayed as results.
Month by Month
The monthly rates show where 2025 departed from 2024. For most months the two years track within about a point of each other. The exception is April and May 2025, when the rate reached 6.7 and 7.7 percent, the two highest months in either year. June and July remained somewhat elevated before the rate returned to the range seen in 2024.
| Month | 2024 rate (%) | 2025 rate (%) | Difference (points) |
|---|---|---|---|
| January | 6.2 | 5.0 | -1.2 |
| February | 6.5 | 4.9 | -1.6 |
| March | 6.4 | 5.5 | -0.9 |
| April | 5.2 | 6.7 | +1.5 |
| May | 5.9 | 7.7 | +1.8 |
| June | 6.3 | 6.0 | -0.3 |
| July | 6.0 | 6.5 | +0.5 |
| August | 6.1 | 5.5 | -0.6 |
| September | 5.0 | 6.6 | +1.6 |
| October | 5.3 | 5.5 | +0.2 |
| November | 5.6 | 5.8 | +0.2 |
| December | 5.3 | 5.1 | -0.2 |
By Gym
Six of the seven gyms had an average monthly cancellation rate between 5.5 and 6.0 percent in 2025. Aurora stood apart at 6.84 percent, nearly a point above the chain average of 5.90. Aurora also grew least, from 192 to 199 active members, while Denver Central, the largest gym, grew from 302 to 344. Golden ended the year exactly where it began, at 190 members.
Descriptive work like this sets up questions rather than answering them. The April and May spike, the flat joins and the Aurora gap are all facts to be explained, and a chart of the monthly rates for both years, with the two spring months highlighted, would show the pattern at a glance. Few (2012) recommends choosing a display that makes the intended comparison easiest to see, and a two-line chart by month does that here. Why the spring spike occurred, and why Aurora differs, are questions for the next unit (Tukey, 1977). No real gym or member stands behind any of these figures.
The gym figures should be read with care. Each gym's monthly rate rests on a base of between about 130 and 350 members, so a handful of cancellations can move a single month by a point or more. The twelve-month average smooths much of that noise, which is why the comparison uses the annual figure rather than any one month.
References
Few, S. (2012). Show me the numbers: Designing tables and graphs to enlighten (2nd ed.). Analytics Press.
Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. O'Reilly Media.
Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley.
Reading the BU 351 Unit 3 instructions
For Unit 3, students produce a descriptive analysis answering the framed descriptive question. Most versions want each measure defined before it is computed, the level of aggregation stated, results presented in a small number of tables or charts and every figure compared with something meaningful, such as a prior period or a group average. Many rubrics explicitly forbid explaining causes in this unit, reserving that for later work. Instructors check whether definitions are precise enough to reproduce the numbers. The data may be supplied or carried from earlier units. Expect a few pages with two or three tables, citing sources on data presentation in APA 7.
How the BU 351 Unit 3 example is put together
The sample restates its descriptive question and promises not to explain. A definitions section writes each measure as a formula in words and adds two choices that shape the figures: counting memberships rather than people and using a start-of-month base. A summary table compares the two years on six measures. The following section interprets the table, showing that memberships grew while cancellations rose because the base was larger. A month-by-month table sets the two years side by side with a difference column, and a gym section compares each location with the chain average, noting how small bases make single months noisy. Every table carries its comparison in the same row, so no figure stands alone.
Reading the BU 351 Unit 3 rubric
Herzing's rubric for descriptive analysis generally weighs the precision of definitions, the accuracy and presentation of results, the use of comparisons and restraint from causal claims. Definitions earn full points when a reader could reproduce each figure from them. Presentation is judged by a few focused tables or charts rather than every possible cut. Comparisons score highest when every figure sits beside a prior period or benchmark. Restraint rewards papers that describe patterns without explaining them, which the sample does by passing the spike and the Aurora gap forward. Instructors also credit notes on data limitations, such as the missing December sign-ups. Clean formatting and APA citations complete the grade.
BU 351 Unit 3 help before you submit
The most common Unit 3 mistake is slipping into explanation, for example blaming a price increase for a spike, before any analysis has tested that idea. Another is reporting numbers without definitions, so readers cannot tell what counts as a cancellation or an active member. Students often present every available table, burying the answer. Figures without a comparison leave the reader unsure whether a number is good or bad. Some papers miss data limits, such as an incomplete final month, and draw false conclusions from them. Finally, check whether the headline matches the data; the sample finds memberships growing even though the owner believed they were falling. Keep one sentence per table explaining what the reader should notice.
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BU 351 Unit 3 questions, answered
Is it really wrong to suggest a reason for a change?
It costs marks in this unit specifically, because the criterion is testing whether you know what a summary can support. Where you spot something that looks like a cause, record it as a question for the next stage rather than as a finding. That reads as discipline instead of omission, and it hands you material for the exploratory work that follows.
How many charts belong in the document?
Fewer than the tool will offer. Three or four that each answer a framed question beat a dozen produced because the software made them easy, and the extra ones dilute the marks rather than adding to them. Where a table would be clearer than a chart, use the table. Every visual should have a sentence saying what the reader is meant to take from it.
Which comparison should the figures be set against?
Whichever one the framed question implied, and say why you chose it. A prior period suits a measure expected to move over time; another segment suits a question about difference; a target suits anything with a stated goal behind it. Where the case gives you none of these, build the comparison from the file itself and label it as constructed rather than external.
Where can I find a free BU 351 Unit 3 sample paper?
The finished BU 351 Unit 3 descriptive analysis, with definitions and both tables, is posted above for free. If your dataset or question differs, send the unit prompt and your framed question, and a custom descriptive analysis will be prepared free, usually within about two days.
Is it really wrong to suggest a cause in BU 351 Unit 3?
In this unit, yes. Descriptive work reports what happened; a cause suggested here tends to be treated as fact before anyone tests it. The sample notes the spring spike and the Aurora gap as questions for the exploratory unit instead.