Mystery Basket Challenge | Market Basket Analysis Activity

🎯 Activity Goal

Your team is a data analytics consulting firm hired by a business to uncover hidden purchasing patterns. You'll analyze real-ish transaction receipts, discover association rules, and pitch an actionable business strategy based on your findings.

🏬 Choose Your Client

Select the business whose transaction data you'll analyze:

☕

Bean & Brew Café

Coffee shop with food, drinks & merch

🔨

FixIt Hardware

Home improvement & tool store

📚

PageTurner Books

Bookstore with gifts & café

🥦

FreshMart Grocery

Full-service grocery store

📖 How It Works

Round 1 — Explore (10 min): Examine 20 customer receipts. Identify which items frequently appear together. Calculate support, confidence, and lift for your top 3 rules.

Round 2 — Quiz Challenge (8 min): Test your understanding of association rule concepts with scenario-based questions.

Round 3 — The Pitch (12 min): Design a business recommendation — a store layout, a bundle deal, a marketing campaign, or an app feature — backed by your association rules. Present to the class!

⏱️ 10:00

🧾 Round 1 — Explore the Receipts

Below are 20 customer transaction receipts. Your job: find which items are frequently bought together. Manually count co-occurrences and identify your top 3 association rules.

💡 Quick Formulas Refresher:
Support(A→B) = # transactions with both A and B / total transactions
Confidence(A→B) = # transactions with both A and B / # transactions with A
Lift(A→B) = Confidence(A→B) / Support(B) — if Lift > 1, there's a positive association

✍️ Record Your Top 3 Rules

Write your team's best association rules below:

Rule 1 (e.g., "Latte → Muffin"):

Rule 2:

Rule 3:

Most Surprising Discovery:

⏱️ 8:00

🧠 Round 2 — Concept Quiz Challenge

Test your team's understanding. Discuss each question together, then click your answer. First answer counts!

0

Quiz Points

0 / 0

Correct Answers

⏱️ 12:00

🎤 Round 3 — The Business Pitch

Using your association rules, develop ONE actionable business recommendation and prepare a 2-minute pitch to the class. Choose your pitch type:

🗺️

Store Layout Redesign

Rearrange the store to increase basket size

🎁

Bundle Deal

Create a product combo at a special price

📱

Marketing Campaign

Targeted cross-sell via email, app, or display

💡

Recommendation Engine

Design an "Often bought together" feature

📝 Pitch Worksheet

Team Name:

Recommendation Title (catchy!):

Which association rule(s) support this?

What specifically do you recommend?

Expected business impact:

What minimum support/confidence thresholds did you use and why?

🎯 Pitch Presentation Tips

Open with the insight: "We discovered that X% of customers who buy A also buy B..."
Show the numbers: State your support, confidence, and lift values
Make the recommendation: Be specific and actionable
Quantify the impact: Estimate revenue, basket size, or customer satisfaction gains
Address thresholds: Explain why your min support/confidence make business sense

🏆 Scoring & Leaderboard

0

Rule Discovery (R1)

0

Quiz Score (R2)

0

Pitch Score (R3)

0

Total Points

📊 Scoring Rubric

Category	Points	Criteria
Rule Discovery (R1)	Up to 30	+8 per valid rule with correct metrics, +6 for surprising finding
Quiz (R2)	Up to 25	+5 per correct answer
Pitch Quality (R3)	Up to 30	Creativity +10, Data-backed +10, Actionability +10
Career Connection	Up to 15	+5 for naming industry application, +5 for explaining how it scales, +5 for identifying limitations

💼 Career Connection Discussion

After pitches, each team shares one answer to:

Industry Application: Where would market basket analysis create the most value in your target career?
Scale: How would you apply this to millions of transactions? (Hint: Apriori algorithm, FP-Growth)
Limitations: What can association rules NOT tell you? (Correlation ≠ causation, temporal patterns, context)
Beyond Retail: Name a non-retail application (healthcare co-diagnoses, web clickstream, course enrollment patterns, cybersecurity event correlation)

🛒 Mystery Basket Challenge