BU 351 Unit 8 end-to-end analytics case example

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Closing the course, this BU 351 Unit 8 end-to-end analytics case runs a composite climbing gym chain's problem through every stage once, from a two-sentence request through framing, cleaning, description, exploration, prediction and optimization to a recommendation, and shows at each join what was carried forward and what was left behind. Herzing BU 351 grades the seams between stages in its final unit, and the sample places a short handoff note between each pair so business analytics students can see the chain of reasoning. It summarizes the cleaning record, the finding that memberships grew while cancellations rose, the exploration of early leaving, the logistic regression ranking and a linear program expected to retain about 36 members each month. The closing section lists what the owner received and what remains unanswered, citing Provost and Fawcett, Wang and Strong, Tukey and Hillier and Lieberman.

CourseBU 351 Introduction to Business Analytics
UnitUnit 8
Paper typeEnd-to-end analytics case
LengthAbout 1,060 words, 6 pages
FormatAPA 7 student paper
SchoolHerzing University
ProgramBS in Business Analytics
UpdatedSeptember 2026

Free sample paper for BU 351 Unit 8

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From Two Loose Sentences to a Retention Plan: An End-to-End Analytics Case for a Climbing Gym Chain, With the Handoff Between Every Stage Shown

Student Name

Business, Herzing University

BU 351: Introduction to Business Analytics

Instructor Name

April 29, 2026

What this page is doingThe title names the starting point, the endpoint and the feature that makes the case more than a long report: the handoffs. Showing what passes from one stage to the next is what the final unit grades.
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From Two Loose Sentences to a Retention Plan: An End-to-End Analytics Case for a Climbing Gym Chain, With the Handoff Between Every Stage Shown

The Request and Its Framing

In January 2026 the owner of Basecamp Climbing, the invented Front Range gym business this course has followed, wrote: "Memberships are slipping," followed by a request for the cause and a fix. That request contained four questions of different kinds. A descriptive one asked whether memberships were in fact falling. A diagnostic one asked which members leave. An implied predictive one asked who is likely to leave next. A prescriptive one asked where to spend money to keep members. The case below answers them in that order, because each answer depends on the one before. Provost and Fawcett (2013) describe this movement from a vague business need to a defined analytic task as the step most projects skip and most regret skipping.

Handoff 1: Framing to Cleaning

Carried forward: four questions, each with a measure, a level and a period, and a list of the fields each would need. Set aside: any question about why members feel as they do, since the file holds no survey or exit comments. The cleaning stage therefore had to make every field those four questions use trustworthy, and it did not need to touch fields none of them use.

Cleaning

The membership export had 4,238 rows. Profiling found 38 duplicate rows, 61 records with a gym written as a name instead of a code, 250 missing age bands, three negative visit counts and five members whose join date fell after their cancellation date. Duplicates were removed, keeping the more complete copy; names were mapped to codes; negative visits were set to missing; age was left blank where missing, since no question required it; and the five impossible records were set aside for the billing team. The profile also revealed a subtler problem: the visits field was measured at each member's last date rather than on a common date, which would leak the outcome into any prediction. The cleaned file held 4,195 members. Wang and Strong (1996) argued that data are fit or unfit for a purpose rather than good or bad in general, and this stage judged each problem by what it would do to the questions ahead.

What this page is doingThe cleaning stage reports what was changed and how many records each change touched. That record is what allows a later reader, or the same analyst months later, to reproduce every downstream figure.
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Handoff 2: Cleaning to Description

Carried forward: one member per row, consistent gym codes and a note that sign-ups after November 30, 2025 are missing from the file. Set aside: the visits field for any purpose other than description, and the five unresolved records.

Description

Active members rose from 1,459 at the end of 2024 to 1,590 at the end of 2025. New joins from January through November were nearly flat, 1,339 and 1,324. Cancellations rose from 910 to 1,193. The average monthly cancellation rate, computed on each month's opening membership, moved only from 5.83 to 5.90 percent, but the spring of 2025 stood out, with April at 6.7 percent and May at 7.7, the worst months in the two years. Aurora's 2025 rate, 6.84 percent, was the highest of the seven gyms. The owner's sense of slipping came from rising cancellation counts and slowing growth, not from a shrinking membership.

Handoff 3: Description to Exploration

Carried forward: three patterns to explain, namely the early concentration of cancellations, the spring spike and the Aurora gap. Set aside: the headline worry that memberships were falling, which the data did not support.

Exploration

Of the 1,901 people who signed up in the sixteen months from June 2024, 29.4 percent left within 90 days. Early leaving was far more common after autopay failures, 43.5 percent with one failure and 68.8 percent with two or more, against 23.5 percent with none. It was less common after the introductory class, 24.8 percent against 34.9 percent, a gap that held at six of seven gyms but vanished at Lakewood and shrank among members with payment failures. Aurora's gap was partly its larger share of students. The spring spike affected members of every tenure about equally, and age showed no clear pattern. Tukey (1977) urged analysts to let the data suggest questions before trying to confirm anything, and this stage ended with questions, including whether the class itself keeps members and what caused the spring spike.

Handoff 4: Exploration to the Forward-Looking Steps

Carried forward: seven inputs known on a fixed date, namely the first three months of membership, the introductory class, referral, autopay failures, student and family plans and the Aurora gym, plus rough effect sizes for the class and for payment problems. Set aside: recent visits as a model input, because of the timing problem found in cleaning, and any claim that the class causes retention.

Prediction and Prescription

A logistic regression fitted on 70 percent of the 1,589 members active on June 30, 2025 ranked the held-back 30 percent by their risk of leaving over the next two months. Its accuracy fell below a rule that predicts nobody leaves, as is common when leaving is rare, but its ranking was useful: the top fifth of the test group canceled at 30.5 percent, against 12.8 percent overall, and held 47.5 percent of all cancellations. A linear program then divided a $5,000 monthly budget among free introductory classes, payment-failure calls and guest passes, within limits on front-desk hours, classroom capacity and how many members qualified for each action. The best plan, 95 classes, 180 calls and about 129 passes, keeps an expected 36 members a month; the budget, class seats and the number of members with failed payments were the binding limits (Hillier & Lieberman, 2015).

What the Owner Received, and What Was Left Behind

The owner received a recommendation: call members within two days of a failed payment, keep the class at current capacity, send guest passes with the remaining budget, track the cancellation rate rather than the count and run a two-gym test of the class. Left behind, and stated as such, were the cause of the spring spike, the true effect of the class and the accuracy of the staff's estimates for calls and passes. An end-to-end case is only as honest as its handoffs, and naming what was not carried forward keeps the final recommendation from claiming more than the stages earned. Basecamp Climbing and every figure in this case are composites created for this course.

References

Hillier, F. S., & Lieberman, G. J. (2015). Introduction to operations research (10th ed.). McGraw-Hill Education.

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.

Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5-33. https://doi.org/10.1080/07421222.1996.11518099

Reading the BU 351 Unit 8 instructions

The final BU 351 assignment asks students to complete an analytics case from start to finish. Most versions want a vague business question framed, the data cleaned with changes recorded, descriptive and exploratory results reported, and at least one forward-looking step, predictive or prescriptive, carried out and interpreted. Many rubrics ask for explicit handoffs showing what each stage passed to the next. Instructors grade the case as a connected argument rather than a set of separate exercises. Earlier units may be reused if condensed and linked. Expect a longer paper of several pages, citing sources on analytics methods in APA 7. Keep a running list of decisions as you go, since the handoff notes are easier to write from it.

Inside the BU 351 Unit 8 example

The sample opens with the owner's request and its four framed questions. Short handoff notes then separate each stage: framing to cleaning, cleaning to description, description to exploration and exploration to the forward-looking steps. Each stage section condenses its results, from the 4,195-member cleaned file to membership growth, early-leaving patterns, the model's ranking performance and the optimal retention plan. Each handoff says what was carried forward and what was set aside, such as the visits field or the belief that memberships were falling. The final section lists what the owner received and what remains unknown. The case reads in order from question to answer.

Where the points sit in the BU 351 Unit 8 rubric

The rubric for the end-to-end case generally weighs the framing, the completeness of stages, the visibility of handoffs and the quality of the final recommendation. Framing earns full points when the vague request is turned into specific questions. Completeness is judged by cleaning, description, exploration and at least one forward-looking step, each reported briefly but clearly. Handoffs carry heavy weight and reward explicit statements of what moved between stages and what was dropped, as the sample provides. The recommendation scores highest when it follows from the stages and states what remains uncertain. Clear organization and APA citations complete the grade. A case that admits what it could not answer usually scores better than one that claims everything.

BU 351 Unit 8 help before you submit

Stapling old assignments together, with nothing showing how one stage feeds the next, is the weakness instructors see most in Unit 8. Another is skipping cleaning or exploration and jumping to a model. Students often carry forward assumptions an earlier stage disproved, such as the idea that memberships were falling. Some cases end with a recommendation that goes beyond what the analysis showed. Others fail to say what was left unresolved. Finally, write the handoffs even when they feel obvious; they are what shows an instructor that the case is one piece of reasoning rather than several. Check that numbers match across stages before submitting.

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Attach the Unit 8 instructions, the BU 351 rubric your section posts, the dataset and the business question the case begins from. We write a custom example that frames the request, documents the cleaning, runs summary and exploration, adds the forward step the framing calls for, and writes a handoff note at every seam. First custom sample free, returned in 24 to 48 hours.

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BU 351 Unit 8 questions, answered

Does the case have to use every technique from the term?

No, and forcing them in usually hurts. The framing decides which stages the question needs, and a case that runs cleaning, summary and one forward looking step properly reads better than one touching everything shallowly. Where the instructions require particular stages, include them and say honestly which contributed to the conclusion and which were run because they were asked for.

Can I reuse work from the earlier units?

Usually yes, and most sections expect the case to build on what the term produced, though check whether yours wants fresh work. Reusing is not the same as pasting: results have to be reconnected, the handoff notes written new, and anything that changed since it was submitted brought up to date. The seams are the deliverable, and they cannot be reused from anywhere.

What if the case leads to a recommendation I doubt?

Say that, with the reason, because a case honest about a weak conclusion scores better than one asserting a result the evidence will not hold. Name the stage the doubt comes from, whether that is a cleaning decision, a small segment or a short history, and say what would have to be collected to settle it. Doubt located precisely reads as competence.

Where can I find a free BU 351 Unit 8 sample paper?

Everything in the BU 351 Unit 8 case, from framing to the final recommendation, can be read above free. If your final case uses a different business problem, send the prompt and your data description, and you will get a free case built on it, generally in a couple of days.

Can I reuse my earlier BU 351 work in Unit 8?

Usually, if you condense and connect it. The sample summarizes each earlier stage in a paragraph and adds handoff notes between them. Pasting full earlier assignments tends to repeat material and hide the links instructors are looking for.