Market basket analysis

1. Market Basket Analysis

2. Agenda
• Defining Market Basket Analysis
• Strengths of Association
• Example
• Benefits of Market Basket Analysis

3. Defining Market Basket Analysis..
• Market Basket Analysis is a mathematical modeling technique based
upon the theory that if you buy a certain group of items, you are likely
to buy another group of items.
• Also known as Association Rule Discovery and Affinity Analysis
• One looks for combinations of products that frequently co-occur in
transactions. This is done by identifying the frequent items purchased
by the customers.
• For example, maybe people who buy pasta and cheese, also tend to
buy olive oil (because a high proportion of them are planning on
cooking pasta).

4. Applications of Market Basket Analysis
• An association rule is the statement of the form {Item Set A} {Item
Set B}
• Frequently used in electronic point of sale. Amazon uses affinity
analysis for cross-selling
• Analysis of credit card purchases-Fraud detection
• Identification of fraudulent medical insurance claims.

5. Benefits of Market Basket Analysis
• Association rules from a market basket analysis can be used for a
supermarket to manage its shelf space
• Stock associated items close together such that consumers would not forget to purchase
both items
• Stock associated items far apart such that consumers would spend more time browsing
aisle
• Designing promotional strategies
• Idea on product bundling
• Design a cross-coupon program
• Select appropriate items to be loss leaders
• Temporal components can be very useful to various marketers for selecting cross-
selling items

6. Data for Market Basket Analysis
• Customer – Customer ID ties transactions over time
• Order – represents single purchase event by the customer
• Items – Individual items in an order are referred to as line items

7. Measures to Understand Orders
• What is the average number of orders per customer?
• What is the average number of items per order?
Understanding Items
• For a given product, what is the proportion of customers who have
ever purchased the product?
• For a given product, what is the average number of orders per
customer that include the item?
• For a given product, what is the average quantity purchased in an
order when the product is purchase?

8. Measures to Understand Item Popularity
Most common item analysis
• What is the most common item found in a one-item order?
• What is the most common item found in a multi-item order?
• What is the most common item found among customers who are repeat
purchasers?
Item wise popularity
• How has the popularity of particular items changed over time?
• How does the popularity of an item vary regionally?

9. Association Rules
• Expresses how products and services group together
Three types
• Actionable rules
• Trivial – represents common knowledge
• Inexplicable – flukes in the data

10. Strengths of Association

11. Strengths of Association - Example
Invoice Number Items
1 A, B, C
2 A, C, D
3 B, C, D
4 A, D, E
5 B, C, E
Rule Support Confidence
A  D 2/5 2/3
C  A 2/5 2/4
A  C 2/5 2/3
B&C  D 1/5 1/3

12. Strength of Association - Example
500 3500
1000 5000
No
Yes
No Yes
Checking Account
Saving
Account
Support (SVG  CK) = 50%
Confidence (SVG  CK) = 83%
Expected Confidence (SVG  CK) =85 %
Lift (SVG  CK) = 0.83/0.85 < 1

13. Extending Association Rules – Sequential analysis
• All items purchased by a customer are treated as a single order, and
each item retains the timestamp indicating when it was purchased
• The process is the same for finding groups of items that appear
together
• To develop the rules, only rules where the items on the left-hand side
were purchased before items on the right-hand side are considered