How To Use Market Basket Analysis
business analytics
slug: tutorial-how-to-use-market-basket-analysis
There are the three fundamental metrics in market basket analysis.
1. Support - "How often does this appear?"
Formula: Support(X) = (Transactions containing X) / (Total Transactions)
What it measures: Frequency or popularity
Example from your data:
- Milk appears in 10 out of 15 transactions
- Support(Milk) = 10/15 = 0.67 = 67%
For a rule (X → Y):
- Support(Milk → Bread) = Transactions with BOTH / Total = 6/15 = 40%
Why it matters:
- Low support = rare pattern (might be noise, or might be a valuable niche)
- High support = common pattern (but might just be popular items)
Business use: Filter out patterns that happen too rarely to be actionable
2. Confidence - "How reliable is this pattern?"
Formula: Confidence(X → Y) = Support(X and Y) / Support(X)
What it measures: Conditional probability - "If someone buys X, what % chance they also buy Y?"
Example from your data:
- Butter → Milk has confidence = 0.8571 = 86%
- This means: Of the 7 people who bought Butter, 6 also bought Milk (6/7 = 86%)
Why it matters:
- High confidence = strong predictive power
- If confidence(X → Y) = 100%, then EVERY time someone buys X, they buy Y
Business use: Determine how likely a recommendation will be relevant
Important asymmetry:
- Confidence(Butter → Milk) = 86%
- Confidence(Milk → Butter) = 67%
- These are DIFFERENT because different denominators!
3. Lift - "Is this a special relationship?"
Formula: Lift(X → Y) = Support(X and Y) / (Support(X) × Support(Y))
Or equivalently: Lift = Confidence(X → Y) / Support(Y)
What it measures: How much more likely Y is purchased when X is purchased, compared to Y's baseline popularity
Interpretation:
- Lift = 1.0 → No association (items are independent)
- Lift > 1.0 → Positive association (buying X increases likelihood of buying Y)
- Lift < 1.0 → Negative association (buying X decreases likelihood of buying Y)
Example from your data:
Peanut Butter → Jelly: Lift = 15
- Jelly normally appears in 1/15 = 6.7% of transactions
- When someone buys Peanut Butter, Jelly appears in 100% of transactions
- 100% / 6.7% = 15× more likely!
Butter → Milk: Lift = 1.0
- Milk normally appears in 67% of transactions
- When someone buys Butter, Milk appears in 86% of transactions
- But Butter itself is very common, so this is close to what you'd expect by chance
- 86% / 67% ≈ 1.3, but with rounding and the small dataset, it shows as 1.0
Why it matters:
- Lift tells you if an association is meaningful vs just popular items appearing together
- High lift = surprising, actionable insight
- Lift near 1 = items are just both popular
Business use: Focus on high-lift rules for cross-selling and bundling
How They Work Together:
| Metric |
Question It Answers |
Business Concern |
| Support |
How often does this happen? |
Is this pattern frequent enough to act on? |
| Confidence |
How reliable is this prediction? |
Will this recommendation be relevant? |
| Lift |
Is this relationship special? |
Is this insight surprising and valuable? |
Practical Example from Your Data:
Rule: Peanut Butter → Jelly
- Support = 6.7% (happens in 1/15 transactions)
- Confidence = 100% (everyone who buys PB also buys Jelly)
- Lift = 15 (15× more likely than random)
Interpretation:
- ✅ Perfect predictive power (confidence = 100%)
- ✅ Extremely strong association (lift = 15)
- ⚠️ Rare occurrence (support = 6.7%)
Business action: Create PB&J bundle, display together, but don't expect huge volume
Rule: Butter → Milk
- Support = 40% (happens in 6/15 transactions)
- Confidence = 86% (most butter buyers also buy milk)
- Lift = 1.0 (not surprising - both are just popular)
Interpretation:
- ✅ Frequent pattern (support = 40%)
- ✅ Good predictive power (confidence = 86%)
- ⚠️ No special relationship (lift = 1.0)
Business action: Less interesting for targeted promotions, since both items are already bought frequently anyway
The Sweet Spot:
Ideal rules for action:
- Medium-to-high support (frequent enough to matter)
- High confidence (reliable prediction)
- High lift (surprising, meaningful relationship)