Documentation - Basket Analysis

What is Basket Analysis?

Market Basket Analysis identifies buying patterns and discovers which products are often purchased together. It's like finding the 'best friends' of your products!

With this analysis, you discover association rules that reveal hidden trends in customer purchasing behavior.

Usage examples

Create bundled product promotions that are purchased together
Arrange products nearby on the shelves to boost sales
Customize product recommendations for customers
Identify cross-selling and up-selling opportunities

Quick start

Prepare your data in CSV format with transactions and products
Upload the file to the upload page
Set the parameters (minimum support, confidence)
Please wait for processing (usually 2-5 minutes)
Analyze the discovered association rules

How to organize your data

Organize your data in a CSV spreadsheet with two columns:

Column 1

Transaction ID

Unique identifier for each purchase/order. For example: 001, 002, 003

Column 2

Product

Name of the purchased product. For example: Bread, Milk, Coffee

Transaction spreadsheet example

transaction_id	product
001	Bread
001	Milk
002	Bread
002	Butter

Important: Each row represents ONE product in ONE transaction. If a transaction has 3 products, there will be 3 rows with the same transaction_id.

Analysis settings

Minimum Support (Min Support)

Minimum percentage of transactions that must contain the product set to be considered relevant.

Example: 0.01 1% of transactions (recommended default)

Minimum Confidence

Probability that product B will be purchased when product A is purchased.

Example: 0.5 50% confidence (recommended default)

Minimum Lift (Min Lift)

Measures how much better the rule is than a random purchase. Values > 1 indicate positive association.

Example: 1.0 default (accepts all rules)

Understanding the results

The analysis returns association rules in the format: 'If you buy A, then you will probably buy B'.

Rules Metrics

Support

Frequency with which products appear together in transactions.

Example: Support of 0.3 = 30% of transactions contain these products

Confidence

Probability of buying B given that A was purchased.

Example: Confidence of 0.8 = 80% chance of buying B when buying A

Lift

Indicates how much better the rule is than chance.

Lift > 1: Positive association | Lift = 1: Independent | Lift < 1: Negative association

Practical tip: Focus on rules with high confidence (>0.7) and high lift (>2) to identify the strongest and most useful associations for the business.

Basket Analysis