WGU C207 Task 2 Guide and Example: Decision Tree Analysis

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WGU C207 Task 2 Guide and Example: Decision Tree Analysis

WGU C207 Task 2 requires you to build a decision tree analysis for a business scenario of your choice, including probability assignments, expected monetary value (EMV) calculations, a strategic recommendation, and a discussion of limitations. This guide covers every rubric section with detailed instructions and a fully annotated MedCore Pharmaceuticals sample you can study before writing your own.

Task 2 is a Performance Assessment (PA) — you work at your own pace with access to your materials. It applies the same EMV and decision tree concepts tested on the Task 1 OA, but in a written analytical format rather than multiple choice. If you haven’t studied for the OA yet, start with the WGU C207 Task 1 study guide first.

What Is WGU C207 Task 2?

WGU C207 Task 2 is a written decision tree analysis applying expected monetary value methodology to a business decision scenario, submitted as a formal Performance Assessment evaluated by WGU assessors.

You choose your own business scenario and company — real or fictitious. The scenario must involve a decision with at least two alternatives, multiple possible outcomes for each alternative, and quantifiable payoffs. Most students use a fictitious company to avoid using confidential employer data and to maintain full control over the numbers.

The assessor evaluates whether your decision tree is correctly structured, whether your probability assignments are justified with reasoning, whether your EMV calculations are accurate, and whether your recommendation flows logically from the analysis.

What Does the C207 Task 2 Rubric Require?

The C207 Task 2 rubric evaluates five core competency areas:

• Business context and decision description — Clear identification of the organization, the decision being made, and why quantitative analysis is appropriate.
• Decision tree structure — A correctly formatted decision tree with decision nodes, chance nodes, branches labeled with probabilities, and terminal payoff values.
• Probability justification — Explanation of how each probability was assigned, supported by reasoning (market research, historical data, industry benchmarks, or logical estimation).
• EMV calculations — Accurate arithmetic showing the full calculation for each alternative, not just the final EMV figure.
• Recommendation and limitations — A recommendation identifying the highest-EMV alternative, a rationale for the recommendation, and a substantive discussion of the model’s limitations.

How to Choose Your C207 Task 2 Scenario

The best C207 Task 2 scenario involves a business decision with clearly quantifiable alternatives and outcomes — expansion vs. no expansion, launch vs. delay, hire vs. outsource, build vs. buy.

Strong scenario types:

• Product or service launch decision — Should a company launch Product A or Product B (or neither)? Each option has success and failure outcomes with different probabilities and payoffs.
• Market expansion decision — Should a company enter Market X or Market Y? Each market has growth and stagnation scenarios with different revenue projections.
• Capital investment decision — Should a company invest in automation, outsource manufacturing, or maintain the status quo? Each path has cost and revenue implications under different demand scenarios.
• Pharmaceutical or healthcare R&D decision — Should a healthcare company pursue Drug Line A or Drug Line B? FDA approval probabilities and revenue projections create a natural decision tree structure.

Avoid scenarios where outcomes are binary and trivial, scenarios where probabilities cannot be reasonably estimated, or scenarios where payoffs cannot be quantified in dollar terms.

How to Structure the Decision Tree

Build your decision tree with two or three main alternatives, each with two to three possible outcomes — this gives sufficient analytical depth without making the calculation unwieldy.

Recommended structure:

Decision Node (square)
├── Alternative 1
│   ├── Outcome 1A: Probability p₁ → Payoff $X
│   └── Outcome 1B: Probability (1-p₁) → Payoff $Y
├── Alternative 2
│   ├── Outcome 2A: Probability p₂ → Payoff $X
│   └── Outcome 2B: Probability (1-p₂) → Payoff $Y
└── Alternative 3 (Status Quo / Do Nothing)
    └── Outcome: Probability 1.0 → Payoff $0 (or known baseline)

Key structural rules:

• Probabilities on all branches from a single chance node must sum to 1.0.
• Show the payoff at every terminal node — do not show only the EMV.
• Label every branch with both its name and probability.
• If you include costs (investment, launch costs), subtract them from the gross payoff to show net payoff at each terminal node.

How to Justify Your Probabilities

Every probability assignment must be explained — stating “I assigned 60% probability to success” without a rationale is a primary revision trigger.

Acceptable probability justification approaches:

• Industry benchmark data — “FDA Phase III clinical trial approval rates for cardiovascular drugs average 58–64% (Hay et al., 2014). We assigned a 60% probability to CardioPlus approval accordingly.”
• Historical company data — “MedCore’s last three product launches achieved market penetration rates of 12%, 18%, and 14%, yielding an average of 14.7%. We assigned 15% probability to High adoption.”
• Market research estimates — “A preliminary customer survey of 120 hospital procurement directors found 42% expressing high purchase intent for the NeuroClear formulation. We assign 40% probability to Strong adoption.”
• Conservative/optimistic range logic — “Given the nascent market and no direct comparables, we assigned success probability at the conservative end of the 30–50% range reported in literature for novel oncology entrants, using 35%.”

Cite at least one external source per alternative where possible. Peer-reviewed journals, FDA databases, industry reports, and BLS data are all appropriate sources.

How to Calculate EMV

Show the full arithmetic for each alternative — assessors want to see every step, not just the final number.

EMV format for each alternative:

Alternative 1 — [Name]:
  Outcome A: Probability × Net Payoff = Partial EMV
  Outcome B: Probability × Net Payoff = Partial EMV
  EMV(Alternative 1) = Sum of all partial EMVs = $X

Present all three (or two) EMVs in a summary table after the individual calculations, then state which alternative is recommended and why.

How to Write the Recommendation and Limitations

The recommendation must identify the highest-EMV alternative and explain why it is the preferred choice — including any qualitative factors that support or modify the pure EMV result.

Recommendation structure:

1. State the recommended alternative and its EMV.
2. Compare it to the next-best alternative by dollar amount.
3. Note any qualitative factors (strategic fit, risk tolerance, stakeholder considerations) that reinforce the recommendation.
4. Acknowledge if the highest-EMV option also carries the highest risk — and whether that affects the recommendation for a risk-averse organization.

Limitations section must be substantive — not a single sentence. Address at least three of the following:

• Binary outcome simplification — Real outcomes rarely fall into only two discrete categories; the model assumes all uncertainty resolves to success or failure.
• Probability estimation error — Assigned probabilities are estimates; sensitivity analysis would test how much the recommendation changes if probabilities shift by ±10%.
• Payoff estimation assumptions — Revenue projections assume stable pricing, market share, and competitive conditions that may not hold.
• Time value of money — Standard EMV does not discount future cash flows; a net present value (NPV) framework would be more rigorous for multi-year decisions.
• Non-financial considerations — Strategic alignment, reputational risk, employee impact, and ethical obligations are not captured in EMV calculations.
• Single-period model — The decision tree models one round of choices; real decisions often involve sequential options (expand if successful, exit if not) that a more complex tree or real options analysis would capture.

Common C207 Task 2 Revision Triggers

The four most common C207 Task 2 revision triggers are: probabilities that do not sum to 1.0 on individual chance node branches; EMV calculations that show only the final answer without intermediate arithmetic; probability justifications that state a number without explaining the rationale; and a limitations section consisting of one or two generic sentences.

Additional triggers:

• Decision tree that shows only two alternatives when a “do nothing” or status quo option would logically exist.
• Recommendation that does not match the highest EMV without explicitly explaining why a lower-EMV option is preferred.
• Net payoffs that do not account for costs — if launching costs $500,000, the terminal node payoff must be revenue minus cost, not gross revenue.

WGU C207 Task 2 — MedCore Pharmaceuticals Annotated Example

This sample is provided for educational reference only. Do not submit this document as your own work. Need a custom C207 Task 2 written for you? Message us on WhatsApp: +1 564-544-6924

Business Context Example

Organization: MedCore Pharmaceuticals, LLC — a mid-size specialty pharmaceutical company based in Nashville, Tennessee, with $180M in annual revenue and a pipeline of three candidate drug lines awaiting expansion investment decisions.

Decision: MedCore’s executive team must decide which of three drug line expansion alternatives to prioritize for Q3 capital allocation: CardioPlus (cardiovascular), NeuroClear (neurological), or OncoShield (oncology). Budget constraints allow full investment in only one line this cycle. The do-nothing alternative (Status Quo) is also evaluated as a baseline.

Why quantitative analysis is appropriate: Each alternative involves different probability-weighted outcomes depending on regulatory approval, market adoption, and competitive entry timing. Expected monetary value analysis provides a systematic, data-grounded framework for comparing alternatives that cannot be evaluated through intuition alone given the magnitude of the investment and the uncertainty of outcomes.

Decision Tree Structure Example

MedCore Drug Line Expansion Decision
[Decision Node]
|
|-- CardioPlus ($4.2M investment)
|   [Chance Node]
|   |-- High Adoption (p = 0.60) --> Net Payoff: $8,300,000
|   |-- Low Adoption  (p = 0.40) --> Net Payoff: ($1,200,000)
|
|-- NeuroClear ($5.8M investment)
|   [Chance Node]
|   |-- High Adoption (p = 0.45) --> Net Payoff: $11,700,000
|   |-- Low Adoption  (p = 0.55) --> Net Payoff: ($2,400,000)
|
|-- OncoShield ($3.1M investment)
|   [Chance Node]
|   |-- High Adoption (p = 0.35) --> Net Payoff: $14,200,000
|   |-- Low Adoption  (p = 0.65) --> Net Payoff: ($1,800,000)
|
|-- Status Quo ($0 investment)
    --> Net Payoff: $0

Note: Net payoffs at terminal nodes reflect projected three-year cumulative revenue minus total investment cost.

Probability Justifications Example

CardioPlus (p = 0.60 High Adoption): Cardiovascular drug approval rates for Phase III clinical candidates average 60–65% based on FDA Center for Drug Evaluation and Research approval data (FDA CDER, 2022). CardioPlus targets hypertension management, a market with established reimbursement pathways and physician familiarity. We assigned 60% probability to High Adoption — at the conservative end of the benchmark range — to account for market entry timing risk as two competitors are expected to launch comparable formulations within 18 months.

NeuroClear (p = 0.45 High Adoption): Neurological drug approval rates are lower than cardiovascular due to the complexity of CNS clinical trials and stricter FDA efficacy requirements, averaging 40–50% for Phase III candidates (Hay et al., 2021). NeuroClear targets early-onset Alzheimer’s management — a high-need market with no dominant competitor, but also no established reimbursement pathway at launch. We assigned 45% probability, within the benchmark range, reflecting the unmet-need opportunity balanced against reimbursement uncertainty.

OncoShield (p = 0.35 High Adoption): Oncology drug approvals have improved in recent years under FDA Breakthrough Therapy Designation, but market penetration for novel oncology agents averages 25–40% in the first three years due to treatment protocol conservatism among oncologists (American Society of Clinical Oncology, 2023). OncoShield targets a rare indication (Stage III non-small cell lung cancer refractory cases), limiting addressable patient population. We assigned 35% High Adoption probability, at the lower-mid range of oncology benchmarks, reflecting the narrow initial indication.

EMV Calculations Example

Alternative 1 — CardioPlus:

Alternative 2 — NeuroClear:

Alternative 3 — OncoShield:

Alternative 4 — Status Quo:

EMV Summary:

Sample Recommendation

Based on the expected monetary value analysis, CardioPlus is the recommended alternative with an EMV of $4,500,000 — $555,000 higher than NeuroClear (the next-best option) and $700,000 higher than OncoShield.

The EMV advantage of CardioPlus is driven by its higher probability of High Adoption (60%) relative to NeuroClear (45%) and OncoShield (35%). While both NeuroClear and OncoShield offer higher gross payoffs in their success scenarios ($11.7M and $14.2M respectively), their lower success probabilities and higher downside exposure reduce their risk-adjusted expected value below CardioPlus.

Three qualitative factors reinforce the recommendation: (1) CardioPlus operates in an established reimbursement pathway, reducing revenue uncertainty post-approval; (2) the $4.2M investment requirement is lower than NeuroClear ($5.8M), preserving capital for follow-on pipeline development; and (3) the cardiovascular market’s physician familiarity reduces time-to-adoption compared to novel indications in neurology or oncology.

Risk consideration: MedCore’s current leverage ratio suggests moderate risk tolerance. CardioPlus’s worst-case loss of $1.2M is the smallest downside of the three active alternatives, consistent with the organization’s risk profile.

Sample Limitations

1. Binary outcome simplification. The model assigns outcomes to two discrete states — High Adoption and Low Adoption — when actual market penetration exists on a continuum. A revenue simulation using Monte Carlo methods with a full probability distribution would provide a more realistic range of outcomes.

2. Probability estimation uncertainty. All assigned probabilities are point estimates based on industry benchmarks and internal assumptions. A sensitivity analysis testing the impact of ±10% shifts in success probability would reveal how robust the CardioPlus recommendation is. For example, if CardioPlus High Adoption probability fell from 60% to 50%, the EMV would drop to $3,650,000 — below NeuroClear’s $3,945,000, reversing the recommendation.

3. Time value of money excluded. The three-year cumulative payoff figures are not discounted. At a 10% discount rate, a $8.3M payoff received over three years has a present value of approximately $6.2M — reducing all EMVs proportionally. NPV-adjusted EMV analysis would be more rigorous for capital allocation decisions of this scale.

4. Competitive dynamics not modeled. The model assumes static competitive conditions, but the cardiovascular market is expected to see two competitor launches within 18 months of CardioPlus’s entry. Market share erosion from competitive entry is not captured in the current probability structure.

5. Non-financial considerations excluded. Strategic pipeline balance (currently cardiovascular-heavy), patient population need (oncology represents highest unmet need), and reputational positioning as an oncology innovator are not reflected in EMV. A multi-criteria decision analysis (MCDA) framework incorporating non-financial dimensions would supplement the EMV finding.

Frequently Asked Questions About WGU C207 Task 2

How many alternatives do I need in my C207 Task 2 decision tree?

The rubric does not specify a minimum, but two to four alternatives (including a status quo option) is the standard. Three active alternatives plus a do-nothing baseline gives sufficient analytical depth while keeping the calculation manageable.

Does my C207 Task 2 company need to be real?

No — a fictitious company is perfectly acceptable and strongly recommended. Using a fictitious company lets you control all the numbers, avoid confidentiality concerns about your employer, and construct a scenario optimally suited to demonstrating the required analytical techniques.

How do I know if my probabilities are correct?

There are no objectively “correct” probabilities for a fictitious scenario — what the rubric evaluates is whether your probability assignments are reasonable and whether you have justified them with a coherent rationale. Cite an industry source or historical benchmark for at least one probability per alternative.

Can I use Excel to build the decision tree?

Yes — you can include an Excel-formatted decision tree as an embedded table or image in your Word document. Many students find it easier to calculate EMV in Excel and then copy the formatted table into the submission document.

How long should the C207 Task 2 submission be?

Most passing Task 2 submissions range from 8 to 15 pages including the decision tree, probability justification tables, EMV calculation tables, recommendation narrative, and limitations section. Length matters less than completeness — every rubric section must be substantively addressed.

Author Bio

This guide was developed by the Gradevia academic content team — specialists in WGU MBA curriculum, quantitative decision analysis, and performance assessment standards for working adult learners.

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