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MKT 202 · Unit 5 of 6

Experiments and Causal Evidence

Customer Analytics and Market Research

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Learning objectives

After completing this unit, you will be able to:

  • Apply frameworks from \
  • Apply the frameworks in "Experiments and Causal Evidence" to a real management decision
  • Make progress on your Customer Analytics and Market Research applied project applied project

Why this matters

Experiments and Causal Evidence is essential to Customer Analytics and Market Research. Complete all lessons in this unit, then finish the assessments on the unit page.

Lesson

Unit overview

Work through each lesson in order. This unit covers Experiments and Causal Evidence as part of Customer Analytics and Market Research. Lessons build on one another; complete the knowledge checks and applied work on the unit page after finishing all lessons.

Connection to applied work

This unit feeds directly into Customer Analytics and Market Research applied project. As you read, capture notes, examples, and data you can reuse in that deliverable. Strong students finish each unit with a draft section of their project, not just highlights.

Practice

  1. Write a one-page summary of this unit in your own words without looking at the lesson.
  2. Find a real company example (public filing, news article, or personal experience) that illustrates the main concept.
  3. Draft one paragraph recommending an action a manager should take based on this unit.
  4. Add at least three terms from this unit to your course glossary.

Knowledge check

Answer these without notes before marking the unit complete:

  1. What is the central idea of "Experiments and Causal Evidence"?
  2. What mistake do beginners most often make when applying this material?
  3. How does this unit help you complete Customer Analytics and Market Research applied project?
  4. What is one decision you face this month where this unit applies?

Key takeaways

  • Apply frameworks from \
  • Business concepts only matter when they change a decision.
  • Your MKT 202 assessment (Customer research, surveys, analytics, experiments, and evidence-based marketing decisions.) rewards applied understanding, not memorization.

Unit assessment

Complete each section below. Score 80%+ on the quiz to finish this unit's assessment.

40% applied project35% knowledge checks25% reflections

Exercises

Apply what you learned in this unit with structured practice.

ExerciseApplied practice: Experiments and Causal Evidence45 min
Complete a focused practice exercise on **Experiments and Causal Evidence**. 1. Choose a real company, product, or situation you know. 2. Apply one core framework from this unit to analyze it. 3. Write your analysis in 300–500 words with a clear recommendation. 4. Cite at least one credible source.

Deliverable

300–500 word analysis document saved to your portfolio under MKT 202.

Rubric

  • • Framework applied correctly (not just named)
  • • Specific evidence from a real example
  • • Clear recommendation with tradeoffs acknowledged
  • • Professional writing with source citation
ExerciseDrill: Experiments and Causal Evidence30 min
Work through the practice problems in the unit lesson without looking at notes. Then check your work against the lesson and write a short reflection: - What you got right - One mistake you caught - One concept to review before the next unit

Deliverable

Problem solutions + 150-word reflection in your MKT 202 workbook.

Rubric

  • • Attempted all practice items before checking answers
  • • Honest reflection on errors
  • • Identifies a specific review action

Model / spreadsheet

Build or extend a spreadsheet model tied to this unit.

ModelStructured model: Experiments and Causal Evidence60 min
Create a structured analytical model for **Experiments and Causal Evidence**. Document your assumptions, calculations, and conclusions in a format appropriate to MKT 202 (diagram, table, or written model). Connect outputs to a decision a manager would make.

Deliverable

Structured model document (2+ pages) · One-paragraph summary of key insight from the model · Screenshot or export saved to portfolio

Rubric

  • • Assumptions stated explicitly
  • • Logic is auditable (formulas or steps visible)
  • • Output answers a specific business question
  • • Sensitivity or scenario considered

Knowledge quiz

Check your understanding before marking the unit complete.

1. BrightBrew onboarding A/B: A churn 5.0%, B churn 4.3%, ITT n≈10k per arm. Approximate absolute churners saved per 10k assigns rolling out B?

2. Pre-run power: baseline churn 5%, MDE 0.5pp, ≈9,800 per arm. BrightBrew gets 4,000 signups/week. Minimum runtime about?

3. Expected 50/50 split shows 54% mobile users in B; SRM p=0.003. What should Priya do?

4. Churn difference −0.4pp with 95% CI [−0.9, +0.1]. Pre-registered rule: rollout only if CI entirely below zero. Decision?

5. Why report intent-to-treat when 30% never open onboarding emails?

6. 2×2 factorial SMS×video tests four cells with n=2,500 each. Main advantage over four sequential one-factor tests?

7. Stopping onboarding test at n=1,200/arm with MDE 0.5pp likely yields:

8. Primary metric improved but refunds +15% in treatment. Best action?

Next unit: Decision Support →