WGU C207 Task 2 Guide: Decision Tree Analysis + Example (2026 Edition)

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WGU C207 Task 2 Guide: Decision Tree Analysis (2026 Edition)

WGU’s C207 (Data-Driven Decision Making) Task 2 asks you to perform a decision tree analysis on a dataset that’s randomized per student ID, then write a report explaining your method, your results, and your recommendation. Because the dataset is unique to you, no single “answer key” can actually help you pass this task — what helps is understanding how decision tree analysis works well enough to apply it to whatever numbers you’re given.

This guide walks through every rubric requirement (A through G) in plain language, explains the expected-value method step by step using a fictitious illustrative example, and flags the specific mistakes that most often knock students down to “Approaching Competence.” If you get stuck applying this to your actual dataset, our tutors are available on WhatsApp for a walkthrough.

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.

What C207 Task 2 Actually Requires

Task 2 has seven parts (A–G), each scored independently on the rubric:

• Part A — Describe a business question your scenario raises, and justify why decision tree analysis is the right tool to answer it.
• Part B — Identify the relevant data values: demands, profits per unit, and probabilities.
• Part C — Build a complete decision tree diagram with state-of-nature nodes, calculated payoffs, and expected values (each to two decimal places).
• Part D — Explain how expected value is derived from payoffs, and discuss one limitation each for your data and for the decision tree method itself.
• Part E — Recommend a course of action that’s logically supported by your analysis and answers the business question from Part A.
• Part F — Cite any sources you quote, paraphrase, or summarize, in-text and in a reference list.
• Part G — Demonstrate professional, error-free written communication (this gets checked by Grammarly for Education before submission).

The biggest pattern in students dropping to “Approaching Competence”: treating each part as a standalone checkbox instead of a connected argument. Your business question (A) should set up the data you use (B), which feeds the tree (C), which produces the expected values you explain (D), which directly justifies your recommendation (E). Graders are explicitly looking for that chain of logic.

Why Decision Tree Analysis Is the Right Tool (Part A)

Decision tree analysis is a quantitative decision-analysis method that maps choices, uncertain outcomes, and their associated payoffs into a branching diagram, allowing a decision-maker to compare alternatives using probability-weighted expected values rather than intuition alone (Raiffa, 1968). It is appropriate whenever a decision involves:

1. A choice between two or more alternatives (e.g., which product line to pursue, whether to expand or hold)
2. Uncertain future outcomes with assignable probabilities (e.g., high demand vs. low demand)
3. Quantifiable payoffs for each combination of choice and outcome

When you justify the method in Part A, don’t just say “decision tree analysis is good for decisions under uncertainty” — tie it specifically to your scenario. Name the actual decision being made, point to the actual source of uncertainty in your dataset (which demand variable, which probability split), and explain why a probability-weighted comparison beats a simpler approach like just picking the option with the highest single payoff. Graders read dozens of these every cycle — the ones that name specifics from the scenario stand out from the ones that paraphrase the textbook definition.

Identifying Your Data Values (Part B)

Your dataset (accessed via your student ID in the “Decision Tree Analysis Resources” spreadsheet) will give you three categories of values:

• Demands — the different levels of customer demand the business might face (e.g., low, medium, high)
• Profits per unit — what each option earns per unit under each demand scenario
• Probabilities — the likelihood assigned to each demand scenario occurring

Part B is graded on accuracy and completeness, not interpretation — so this section of your report should clearly list each value with a one-line description of what it represents in your scenario. Don’t bury these numbers inside a paragraph; present them in a way the grader can verify against your spreadsheet at a glance (a small table works well).

Example: Building the Decision Tree Diagram (Part C)

Illustrative example only — uses fictitious data, not WGU’s actual dataset.

Below is a worked example using a fictitious company, “GreenLeaf Beverages,” deciding whether to launch a new product line. This walks through the method — your actual numbers, scenario, and decision will be different.

Fictitious scenario: GreenLeaf Beverages is a regional beverage company that has built its reputation on traditional iced teas sold through grocery and convenience retailers. Market research suggests a growing segment of health-conscious consumers is shifting toward lower-sugar, carbonated alternatives, and GreenLeaf’s leadership team is weighing whether to enter this space with a new sparkling tea line. Launching the line would require upfront investment in production retooling and a marketing campaign, with no guarantee that demand will materialize at the level needed to justify the cost.

Leadership has asked the analytics team to evaluate the decision using the demand and probability estimates compiled from a recent market survey and competitor benchmarking exercise: market demand could be High (60% probability, based on the proportion of surveyed consumers expressing strong purchase intent) or Low (40% probability, reflecting the risk that the new line underperforms against established competitors). Two outcomes, two probabilities, one upfront choice — that’s the structure decision tree analysis maps onto.

Step 1: Lay out the decision node. Start with a square decision node representing the choice (“Launch” vs. “Don’t Launch”).

Step 2: Add state-of-nature nodes. From the “Launch” branch, add a circular chance node splitting into “High Demand” and “Low Demand,” each carrying its assigned probability.

Step 3: Calculate payoffs at each branch end. Multiply the payoff per unit by relevant volume assumptions from your scenario to get the dollar payoff at each branch endpoint. Round to two decimal places.

Step 4: Calculate expected value. At the chance node, multiply each branch’s payoff by its probability, then sum:

Expected Value (Launch) = (0.60 × $4.50) + (0.40 × $1.20) = $2.70 + $0.48 = $3.18

Expected Value (Don’t Launch) = $0.00

Step 5: Compare expected values at the decision node. The branch with the higher expected value ($3.18 for “Launch”) is the one your tree should recommend rolling back to the decision node.

Apply this same five-step structure to your own dataset’s demand levels, probabilities, and payoffs — the mechanics don’t change, only the numbers do.

A note on scope: This example deliberately stops at the calculation (Parts B–D1). It doesn’t write out a finished business question and justification (Part A), a stated limitation (Part D2), a recommendation paragraph (Part E), or a sample reference list (Part F) — those sections of your report need to be built around your scenario and dataset, not adapted from someone else’s. The sections above explain what each of those parts is asking for and how to approach it.

Calculating Expected Value (Part D1)

When you explain this step in your report, walk through the formula explicitly rather than just stating the result:

Expected Value = Σ (Payoff at each outcome × Probability of that outcome)

For each chance node, multiply every possible payoff by its corresponding probability, then add those products together. Do this for every branch stemming from your decision node, then compare the totals — the branch with the highest expected value is generally the one your recommendation should support (unless your scenario specifies other constraints, like risk tolerance or budget limits, that you should also address).

This “fold-back” process — working from the endpoints of the tree backward to the decision node — is the standard approach in decision analysis (Raiffa, 1968), and remains the core mechanic taught in modern business statistics courses (The Open University, n.d.).

What Students Say; For Part D

Limitations to Discuss (Part D2)

You need one limitation tied to a data value from Part B, and one limitation of decision tree analysis as a method. The trap most students fall into is treating their assigned probabilities as if they were certain facts rather than estimates — your dataset’s probabilities came from somewhere (historical sales, market research, expert judgment), and that source is exactly where the limitation usually lives. Categories worth considering (adapt to your actual scenario rather than copying generically):

Data value limitations:

• Probabilities are often estimates based on historical data or judgment, not certainties — actual outcomes can diverge
• Profit-per-unit figures may not account for variable costs that shift at different volumes
• Demand estimates can be affected by external factors (seasonality, competitor actions) not captured in the dataset

Decision tree method limitations:

• The model assumes payoffs and probabilities are known with confidence, which is rarely fully true in practice
• It doesn’t easily account for risk aversion — a lower expected value option might be preferable if it has less downside variance
• Complex real-world decisions often involve more interdependent variables than a tree can cleanly represent

Pick one from each category that’s genuinely supported by your scenario’s data, and explain why it’s a limitation rather than just naming it.

Writing Your Recommendation (Part E)

Your recommendation needs two things to hit “Competent”: it has to follow logically from your expected value results, and it has to directly answer the business question you posed in Part A. A common mistake is recommending the higher-expected-value option without explicitly reconnecting it to the original business question’s wording — make that link explicit in your closing paragraph.

Citations & Professional Communication (Parts F–G)

• Use APA format for any in-text citations and your reference list. Even course materials or textbooks you paraphrase need a citation.
• Run your draft through Grammarly for Education before submitting — Part G won’t pass if Grammarly flags substantial unresolved issues, and WGU checks this automatically before evaluation.
• Read your final draft out loud once before submitting. It’s the fastest way to catch the awkward phrasing and run-on sentences that Grammarly sometimes misses but a grader will still notice.

WGU C207 Task 2 Complete Example

This example uses a fictitious scenario to demonstrate how to achieve a perfect score on each section of the rubric.

A. Business Scenario Summary

A1. Business Question

A mid-sized technology company, “InnovateTech,” is facing a critical decision regarding its flagship product, the “SmartHome Hub.” The current model is experiencing a decline in sales due to increased competition. The company’s leadership must decide whether to invest a significant amount of capital to develop a next-generation “SmartHome Hub 2.0” or to forgo the investment and discontinue the product line. The core business question derived from this scenario is: Should InnovateTech invest in developing SmartHome Hub 2.0 to maximize expected profit, or should it discontinue the product?

A2. Justification for Decision Tree Analysis

Decision tree analysis is the most appropriate technique for this scenario because it allows for a structured and logical evaluation of decisions under uncertainty. As noted by Sishi and Telukdarie (2021), a decision tree is a tool that supports decision-making through a tree-structured modeling approach to map the possible outcomes of a chain of interconnected choices. InnovateTech’s choice involves a series of sequential decisions and uncertain future outcomes. The company must first decide whether to invest. If it invests, the success of the new product is uncertain and can result in either high market demand or low market demand.

Each of these outcomes leads to a different level of profit. The decision tree framework is ideal for modeling this sequence of events: an initial decision, followed by “chance” (or state-of-nature) nodes representing uncertain market reactions. It then calculates the expected value of each branch, providing a clear, quantitative comparison between the “Invest” and “Don’t Invest” alternatives. This directly addresses the business question by quantifying the financial risk and reward, allowing management to make an informed, data-driven decision that maximizes expected profit.

B. Relevant Data Values

Based on the fictitious scenario provided in the “Decision Tree Analysis Resources” spreadsheet, the following data values are identified as essential for the analysis:

• Demands (State of Nature):

  • High Demand: 10,000 units

  • Low Demand: 4,000 units

• Profits per Unit:

  • Profit per unit in High Demand: $100

  • Profit per unit in Low Demand: $60

• Probabilities:

  • Probability of High Demand: 0.70

  • Probability of Low Demand: 0.30

• Costs (Additional Data Value):

  • Investment Cost for Development: $500,000

• Profit (Payoff) for “Don’t Invest” Alternative:

  • $0 (If the product is discontinued, there is no profit or loss from this line).

C. Data Analysis Using Decision Tree Analysis

A decision tree diagram was constructed in the provided “Decision Tree Analysis Resources” spreadsheet. The analysis follows the “Invest” and “Don’t Invest” alternatives to determine the expected monetary value.

C1. Decision Tree Diagram Components

Below is a textual representation of the calculated components. The full diagram and all calculations are included in the attached Excel spreadsheet.

Decision Node 1: Investment Decision

• Alternative 1: Invest in SmartHome Hub 2.0

  • Investment Cost: -$500,000

  • State-of-Nature Node: Market Demand

    • Branch 1: High Demand

      • Probability: 0.70

      • Payoff Calculation: (Demand * Profit per Unit) – Investment Cost = (10,000 units * $100) – $500,000 = $1,000,000 – $500,000 = $500,000.00

      • Expected Value Calculation: $500,000 * 0.70 = **$350,000.00**

    • Branch 2: Low Demand

      • Probability: 0.30

      • Payoff Calculation: (Demand * Profit per Unit) – Investment Cost = (4,000 units * $60) – $500,000 = $240,000 – $500,000 = -$260,000.00

      • Expected Value Calculation: -$260,000 * 0.30 = **-$78,000.00**

  • Total Expected Value for “Invest” Alternative: $350,000 (High) + (-$78,000) (Low) = $272,000.00

• Alternative 2: Don’t Invest (Discontinue Product)

  • Payoff Calculation: $0 (No revenue or costs)

  • Expected Value Calculation: $0 * 1.00 = **$0.00**

Selected Decision: Based on the comparison of expected values ($272,000 > $0), the best decision is to Invest.

D. Implications of the Decision Tree Analysis

D1. Steps to Determine Expected Value

The expected value is the weighted average of all possible payoffs for a given decision alternative. The process is as follows:

1. Identify Payoffs: For each possible outcome (state of nature), calculate the net payoff. In this case, for each demand scenario, the net profit was calculated by multiplying the demand (units sold) by the profit per unit and then subtracting the initial investment cost.

2. Identify Probabilities: Assign a probability to each state of nature. This probability represents the likelihood of that specific outcome occurring. In the analysis, High Demand was assigned a 0.70 probability, and Low Demand a 0.30 probability.

3. Calculate Expected Value of Each Branch: Multiply the net payoff of each state of nature by its assigned probability. This weights the payoff by how likely it is to occur. For example, the High Demand payoff of $500,000 * 0.70 = $350,000.

4. Sum the Expected Values: Add all the weighted branch values together for a single decision alternative. The sum represents the expected monetary value (EMV) of that choice. For the “Invest” alternative, the sum was $350,000 + (-$78,000) = $272,000.

D2. Limitations of the Analysis

• Limitation of a Data Value (Probabilities): The probabilities assigned to High and Low demand (0.70 and 0.30) are subjective estimates. They are based on market research and expert opinion, but they do not guarantee the actual outcome. If these estimates are inaccurate, the expected value will be significantly different, potentially leading to a different decision. Ansari et al. (2021) highlight that an investor’s risk-taking behavior significantly changes the outcome of the decision-making process, underscoring the importance and potential subjectivity of these probability assessments. Sensitivity analysis should be performed to see how changes in these probabilities affect the result.

• Limitation of the Decision Tree Analysis Itself: A significant limitation is that the decision tree analysis simplifies a complex reality. It assumes that the key outcomes (High Demand and Low Demand) are discrete and that the associated payoffs and probabilities are fixed. In reality, market demand exists on a spectrum. Furthermore, the analysis does not account for intangible factors that are difficult to quantify, such as the impact on brand reputation, the potential for future market share gains, or the ability to pivot if the product fails. As Liao and Liu (2022) note, the objective environment is characterized by uncertainty, and decision-makers have a limited ability to understand it fully, which can lead to an enterprise crisis if not properly anticipated. The decision tree provides a valuable financial framework but should not be the sole basis for the final decision.

E. Recommended Course of Action

Based on the results of the decision tree analysis, InnovateTech should invest in the development of the SmartHome Hub 2.0. The expected value of this decision is $272,000, which is significantly higher than the expected value of discontinuing the product ($0). This recommendation logically addresses the business question by showing a quantifiable financial benefit to the investment. The positive expected value indicates that, on average, the investment will be profitable given the current estimates of demand and costs. This result provides a strong quantitative justification for pursuing the new product development and allocating the necessary capital.

F. Sources

Ansari, Z., Hejazi, R., Zeraatkish, Y., & Khani Masoum Abadi, Z. (2021). Financial Performance Evaluation of Companies Using Decision Trees Algorithm and Multi-Criteria Decision-Making Techniques with an Emphasis on Investor’s Risk-Taking Behavior. Advances in Mathematical Finance and Applications, *6*(3), 555-566.

Hsu, Y.-L., & Reid, G. C. (2021). Two-stage decision-making within the firm: Analysis and case studies. Managerial and Decision Economics, *42*(6), 1355-1373. https://doi.org/10.1002/mde.3317

Liao, S., & Liu, Z. (2022). Enterprise Financial Influencing Factors and Early Warning Based on Decision Tree Model. Wireless Communications and Mobile Computing, *2022*, 1-11. https://doi.org/10.1155/2022/6260809

Sishi, M., & Telukdarie, A. (2021). The application of decision tree regression to optimize business processes. In Proceedings of the International Conference on Industrial Engineering and Operations Management, 2021 (pp. 48-57). IEOM Society.

Common Mistakes That Drop You to “Approaching Competence”

• Part A: Justification is generic (“decision trees help with uncertainty”) instead of tied to specific scenario details.
• Part B: Listing data values without specifying which decision/outcome each one belongs to.
• Part C: Missing state-of-nature nodes, or payoffs/expected values not rounded to two decimal places.
• Part D1: Describing the result of the expected value calculation without explaining the step-by-step method.
• Part D2: Limitations are vague or not logically tied to the specific data value or method used.
• Part E: Recommendation doesn’t explicitly reference the business question from Part A.
• Part F: Reference list present but missing matching in-text citations, or vice versa.

References

Open University. (n.d.). Decision trees and dealing with uncertainty. OpenLearn. https://www.open.edu/openlearn/money-business/decision-trees-and-dealing-uncertainty/content-section-4.1

Raiffa, H. (1968). Decision analysis: Introductory lectures on choices under uncertainty. Addison-Wesley.

tutor2u. (n.d.). Decision trees. Business Reference Library. https://www.tutor2u.net/business/reference/decision-trees

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