WGU C207 Task 2 Guide: How to Nail Every Rubric Line on Your Decision Tree Analysis

Updated on: Jul 21, 2026

WGU C207 Task 2: One Thing Worth Knowing Before You Start

Every C207 Task 2 student receives the identical scenario — you’re the COO of a pharmaceutical company deciding between developing a new drug, expanding an existing one, or making no changes — with one of three shared datasets assigned by your student ID. A full worked answer using that scenario would function as an answer key for a meaningful share of everyone in this course, so this guide teaches the method using a fictitious, structurally identical scenario instead: a coffee roasting company weighing a new product launch against expanding an existing line. Same structure, same calculations, entirely different numbers.

Quick Facts

The Assignment

Introduction

Managers are required to organize, interpret, and display data that is reliable and relevant to the real-world decisions they must make in their businesses. The use of analytical tools will improve your ability to use data to make informed decisions.

In this task, you will address the business situation in the attached scenario. You will access the scenario and dataset by entering your student ID number in the “Start” tab of the “Decision Tree Analysis Resources” document found in the Supporting Documents section. The scenario and dataset are located in the “Decision Tree Scenario” tab. Using this dataset, you will perform a decision tree analysis and recommend a solution.

This recommendation will be included in a report you will write summarizing the key details of your analysis.

For full functionality of the scenario and data attachment, you must use Microsoft Excel, which is available via the Microsoft Office 365 subscription service provided to all WGU students. It can be downloaded using the “Microsoft Office 365” link in the Web Links section.

Scenario

Refer to the scenario located in the supporting document, “Decision Tree Analysis Resources.”

Requirements

Your submission must represent your original work and understanding of the course material. Most performance assessment submissions are automatically scanned through the WGU similarity checker.

Students are strongly encouraged to wait for the similarity report to generate after uploading their work and then review it to ensure Academic Authenticity guidelines are met before submitting the file for evaluation. See Understanding Similarity Reports for more information.

Grammarly Note:

Professional Communication will be automatically assessed through Grammarly for Education in most performance assessments before a student submits work for evaluation. Students are strongly encouraged to review the Grammarly for Education feedback prior to submitting work for evaluation, as the overall submission will not pass without this aspect passing. See Use Grammarly for Education Effectively for more information.

Microsoft Files Note:

Write your paper in Microsoft Word (.doc or .docx) unless another Microsoft product, or pdf, is specified in the task directions. Tasks may notbe submitted as cloud links, such as links to Google Docs, Google Slides, OneDrive, etc. All supporting documentation, such as screenshots and proof of experience, should be collected in a pdf file and submitted separately from the main file. For more information, please see Computer System and Technology Requirements.

You must use the rubric to direct the creation of your submission because it provides detailed criteria that will be used to evaluate your work. Each requirement below may be evaluated by more than one rubric aspect. The rubric aspect titles may contain hyperlinks to relevant portions of the course.

Complete your decision tree analysis and create a report by doing the following:

Note: The supporting document “Decision Tree Analysis Resources” contains a scenario, data set, and template. While you must use the scenario and data set provided in the supporting document, the template is optional. You are encouraged to use the template to complete your analysis. Please see supporting document, “QUM3 Task 2 Getting Started,” for help accessing the scenario and dataset.

A. Summarize the business scenario by doing the following:

1. 1. Describe a business question that could be answered by applying decision tree analysis and is derived from the scenario in “Decision Tree Analysis Resources.”

   2. Justify why decision tree analysis is the appropriate analysis technique, and include relevant details from the scenario to support your justification.

B. Identify the relevant data values required for your decision tree analysis, including the following:

• demands

• profits per unit

• probabilities

C. Report how you analyzed the data using decision tree analysis by completing a decision tree diagram that includes each of the following:

• state-of-nature nodes

• calculated payoffs, each expressed out to two decimal places

• expected values, each expressed out to two decimal places

Note: Include “Decision Tree Analysis Resources.” spreadsheet with your task submission for evidence of your calculations and decision tree diagram.

Note: Refer to “Prepare for the Performance Assessment Task2″ in the course of study to examples of acceptable output.

D. Summarize the implications of your decision tree analysis by doing the following:

1. 1. Explain each step required to determine the expected value based on

   2. List one limitation for each of the following:

      • any one of the data values listed in part B

      • the decision tree analysis

E. Recommend a course of action that addresses the business question from part A and is based on the results of your decision tree analysis.

F. Acknowledge sources, using in-text citations and references, for content that is quoted, paraphrased, or

G. Demonstrate professional communication in the content and presentation of your submission.

A1 — Describing the Business Question

What evaluators check: A business question that’s accurate, relevant to the scenario, and specifically suited to decision tree analysis — not just any strategic question.

How to get this right: A decision-tree-appropriate business question always centers on choosing between discrete alternatives under uncertain conditions, where the uncertainty can be assigned probabilities.

Fictitious walkthrough — Alderbrook Coffee Roasters: “As COO of Alderbrook Coffee Roasters, which alternative – developing a new cold brew concentrate line, expanding the existing roast line into new markets, or maintaining current operations — produces the highest expected value given uncertain market demand conditions?”

A2 — Justifying Decision Tree Analysis as the Right Technique

What evaluators check: A logical explanation, backed by specific scenario details, for why decision tree analysis fits this situation — not a generic definition.

How to get this right: Point to the three specific features that make decision tree analysis appropriate: multiple discrete alternatives, uncertain future conditions with assignable probabilities, and quantifiable payoffs for each combination.

Fictitious walkthrough: “Decision tree analysis is appropriate because Alderbrook faces three discrete alternatives, each with two possible market conditions (favorable and unfavorable) with estimated probabilities, and each combination produces a quantifiable payoff based on demand and profit per unit — exactly the structure decision tree analysis is built to evaluate.”

B — Identifying the Relevant Data Values

What evaluators check: All three required elements — demands, profits per unit, and probabilities — accurately identified. Missing any one caps this at “Approaching Competence.”

How to get this right: Pull these directly from your dataset for each alternative, not just one. Organize them before you build your tree; trying to build the diagram and identify the data simultaneously is where errors creep in.

Fictitious walkthrough:

(All figures fictitious, built to mirror the structure of the real assignment’s data without using any of its actual values.)

C — Building the Decision Tree Diagram

What evaluators check: Every element present and accurate — state-of-nature nodes, calculated payoffs to two decimal places, and expected values to two decimal places.

How to get this right: Structure your tree with a decision node branching into your three alternatives, each alternative then branching into a state-of-nature node (the market condition uncertainty), and each of those branches ending in a payoff. Calculate every payoff as demand × profit per unit, and round to exactly two decimal places throughout — a common, avoidable point loss is rounding inconsistently or too early in the calculation.

Fictitious walkthrough — calculated payoffs:

• Develop new product: Low = 1,200 × $0.65 = $780.00 | High = 4,000 × $0.65 = $2,600.00
• Expand existing line: Low = 1,800 × $0.50 = $900.00 | High = 5,200 × $0.50 = $2,600.00
• Maintain current operations: Low = 300 × $0.80 = $240.00 | High = 750 × $0.80 = $600.00

D1 — Explaining How Expected Value Is Determined

What evaluators check: A logical, accurate explanation of every step in the expected value calculation — not just the final numbers.

How to get this right: Walk through the process explicitly: multiply each payoff by its corresponding probability, then sum those weighted values for each alternative. Show this as a stated process, not just a results table.

Fictitious walkthrough: “For each alternative, expected value is calculated by multiplying each state-of-nature payoff by its probability, then summing the results. For the new product alternative: (0.30 × $780.00) + (0.70 × $2,600.00) = $234.00 + $1,820.00 = $2,054.00. This same process — multiply, then sum — applies to each alternative.”

Fictitious walkthrough — full expected values:

D2 — Identifying Limitations

What evaluators check: One accurate limitation tied to a specific data value from part B, and one accurate limitation of decision tree analysis as a technique — both required.

How to get this right: For the data-value limitation, focus on where the number came from and how reliable that source is. For the technique limitation, decision tree analysis has a well-documented structural weakness worth knowing: it’s sensitive to small changes in its inputs. Recent research on decision tree methodology in business analytics has specifically noted that minor variations in underlying data can produce disproportionately different outputs — meaning the technique’s conclusions are only as stable as the estimates feeding it.

Fictitious walkthrough:

• Data value limitation: “The probability estimates (0.30/0.70, etc.) are based on market research projections rather than confirmed outcomes, and may not accurately reflect actual future conditions.”
• Technique limitation: “Decision tree analysis is sensitive to its input assumptions — small inaccuracies in the estimated probabilities or demand figures could shift which alternative produces the highest expected value, potentially changing the recommended course of action.”

E — Recommending a Course of Action

What evaluators check: A recommendation that’s logically supported by your actual expected value results and directly addresses your Part A business question. The scenario also typically expects both a primary and a backup recommendation.

How to get this right: Name the highest expected value alternative as your primary recommendation, and the second-highest as your backup — explicitly using the numbers, not just stating a preference.

Fictitious walkthrough: “Based on the expected value analysis, Alderbrook should pursue developing the new cold brew concentrate line as its primary course of action, given its highest expected value of $2,054.00. Expanding the existing roast line, with an expected value of $2,005.00, is a close second and serves as a strong backup option should new-product development face unforeseen obstacles.”

F & G — Sources and Professional Communication

F (Sources): Cite any material you reference beyond your own analysis, with both in-text citations and a matching reference list — an incomplete or missing reference list caps this rubric line regardless of how strong your citations otherwise are.

G (Professional Communication): Same built-in check as Task 1 — Grammarly for Education scans your submission before you can submit it, and the assessment won’t pass without clearing it. Review every flagged issue rather than skimming past the summary score.

Still not sure your tree structure matches what’s expected? An Assignment Clarity Session walks through your actual diagram and numbers with you — not a hypothetical.

Common Mistakes to Avoid

• Building the tree before organizing the underlying data, leading to mismatched probabilities and payoffs
• Rounding at different points in the calculation, producing expected values that don’t match a consistent two-decimal standard
• Writing D1 as a results table instead of an explained process
• Providing a limitation that’s vague (“the data might be wrong”) instead of tied to a specific value or a documented weakness of the technique
• Recommending an alternative without explicitly connecting the recommendation to your calculated expected values
• Forgetting the backup recommendation the scenario specifically asks for

Perfect-Score Self-Check

• Does my business question name specific alternatives and fit the “which option maximizes expected value” shape?
• Have I identified demands, profits per unit, and probabilities for every alternative in part B?
• Are all my payoffs and expected values rounded consistently to two decimal places?
• Does my D1 explanation walk through the calculation steps, not just show the answer?
• Do I have one limitation tied to a specific data value and one tied to the technique itself?
• Does my E recommendation explicitly reference my calculated expected values, including a backup option?
• Have I cleared the Grammarly for Education check?

References & Further Reading

• Lee, C. S., Cheang, P. Y. S., & Moslehpour, M. (2022). Predictive Analytics in Business Analytics: Decision Tree. Advances in Decision Sciences, 26(1), 1–29. — A peer-reviewed paper on decision tree methodology in business analytics, including the technique’s sensitivity to input variation — the academic basis for this guide’s D2 technique-limitation discussion.

Frequently Asked Questions

Is the pharmaceutical company scenario used in this guide’s examples? No. Every C207 QUM3 Task 2 student receives the same Major Pharmaceutical Company scenario and one of three shared datasets, so this guide uses a fictitious coffee roasting scenario (Alderbrook Coffee Roasters) to demonstrate the method without answering the actual shared prompt.

Does this guide calculate my actual expected values for me? No. Your dataset is one of three assigned by your student ID and isn’t visible to us. This guide walks through the calculation process on fictitious numbers so you can apply the identical steps to your real data.

Do I need both a primary and backup recommendation? Check your specific scenario document, but this task’s scenario typically asks the decision-maker for both an immediate action and a backup — worth including both even if the rubric language doesn’t spell it out explicitly, since it directly addresses what the scenario’s stakeholder is asking for.

Is getting help with this task against WGU’s academic integrity policy? Tutoring, coaching, and editing support are a normal, accepted part of studying — most university policies explicitly support students getting this kind of help. Our role is to help you understand and interpret your own output, which fits well within that.