Market Basket Analysis: Anticipating Customer Behavior

Market basket analysis.


Machine Learning is now assisting the retail industry in a variety of ways. You might anticipate that machine learning (ML) has various applications in the retail industry, ranging from projecting sales performance to identifying purchasers. One of the best applications of machine learning in the retail industry is "Market Basket Analysis." We can determine which products are usually purchased jointly by clients by studying their previous purchasing activity.

What is Market Basket Analysis?

Market Basket Analysis is a technique that analyzes patterns of co-occurrence and determines the strength of the link between two products purchased together. When two or more things happen at the same moment, it's called a co-occurrence.
If-Then scenario rules are generated by Market Basket Analysis, such as if item A is purchased, then item B is likely to be purchased. The rules are probabilistic, which means these are developed from the co-occurrence frequencies in the observations. The frequency refers to the percentage of items in the customer's basket that are of interest. The rules can help with pricing, product positioning, and many types of cross-selling efforts.

How Market Basket Analysis works

To make it more understandable, think about Market Basket Analysis in terms of grocery shopping. Market Basket Analysis gathers information at the transaction level, which includes all customer's purchases in a single transaction. This method determines which products were purchased in conjunction with each other. Based on the things purchased, these relationships are used to generate profiles with If-Then rules.

The rules could be written as: If {A} Then {B}

The antecedent is the If part of the rule (the A above), and the consequent is the THEN part of the rule (the B above). The condition is the antecedent, and the result is the consequence.
Support, Confidence, and Lift are three measures in the association rule that express the level of confidence in the rule.

You're in a supermarket, for example, to buy bread. Are you more likely to buy oranges or cheese in the same transaction as someone who did not buy bread, according to the analysis?


You're in a supermarket, for example, to buy bread. Are you more likely to buy oranges or cheese in the same transaction as someone who did not buy bread, according to the analysis?


Let’s take an example for a better understanding of Market Basket Analysis. There are nine baskets in the table below, each with a different combination of items i.e., bread, cheese, oranges, and grapes.


The next stage is to figure out the rules and relationships between these items. There are a total of 22 rules for the nine baskets. The following table illustrates some of the relationships for clarity:



1. Support

The number of transactions that include items in A and B parts of the rule as a proportion of the overall number of transactions is the first metric, known as support. It's a metric that measures the number of times a group of things appears together in a transaction. This is considered as a percentage of all transactions.
- The support formula is:


2. Confidence

The confidence of a transaction can be calculated as a ratio of the number of transactions. Considering the above tables, it includes all items in B as well as the number of transactions that include all items in A to the number of transactions that include all items in A is the second measure called the confidence of the rule.
- The confidence formula is:


3. Lift

In other words, how frequently do items in B appear in transactions that just contain A?

The third metric, known as the lift or lift ratio, is the confidence-to-expected-confidence ratio. The expected confidence is calculated by dividing the confidence by the frequency of B. The Lift indicates the capability of a rule at predicting the outcome rather than simply assuming the outcome. Stronger associations are indicated by higher lift values.
- The lift formula is:


Association rule mining

When you wish to find a connection between distinct objects in a set, find frequent patterns in a transaction database, relational databases, or any other information repository, you utilize Association Rule Mining. Marketing, Basket Data Analysis (or Market Basket Analysis) in retailing, clustering, and classification are some of the uses of Association Rule Mining.

Market Basket Analysis is a significant strategy used by large retailers like Amazon, Flipkart, and others to study customer buying behaviors. It helps detect links between the different things that customers place in their 'shopping baskets'. Also, it is the most popular approach to finding these patterns. By acquiring insight into which things are frequently purchased together by customers, retailers can design marketing strategies based on the finding of these correlations. Some of the popular tactics used by retailers:

  • Changing the store layout according to trends
  • Customer behavior analysis
  • Catalog design
  • Cross marketing on online stores
  • Trending items customers prefer
  • Customized emails with add-on sales

This type of analysis can be used by online merchants and publishers to:

  • Inform the placement of content items on their media sites or products in their catalog
  • Deliver targeted marketing


Types of Market Basket Analysis

Let us delve deeper into the world of Market Basket Analysis. Its approaches are classified based on how the available data is used:

1. Descriptive market basket analysis: This method relies solely on historical data and is the most widely utilized. The study does not make any predictions; rather, it uses statistical approaches to score the relationship between products. Unsupervised learning is the name given to this form of modeling by individuals who are knowledgeable about the fundamentals of data analysis.

2. Predictive market basket analysis: Supervised learning algorithms such as classification and regression are used in this sort of analysis. Its main goal is to imitate the market to figure out what causes things to happen. Purchasing an extended warranty, for example, is more likely to occur after purchasing an iPhone. To calculate cross-selling, it considers things purchased in a specific order. While it isn't as popular as a descriptive MBA, it is still an extremely useful tool for marketers.

3. Differential market basket analysis: This form of study is useful for analyzing competitors. It examines purchase histories across stores, seasons, time periods, days of the week, and other variables to uncover fascinating patterns in consumer behavior. It can, for example, assist in determining why some consumers choose to buy the same product for the same price on Amazon vs. Flipkart — the explanation could be as simple as the Amazon reseller having more warehouses and being able to deliver faster, or it could be something more fundamental like the user experience.

Examples of Market Basket Analysis

Some of the most popular examples of Market Basket Analysis are as follows

1. Retail: is perhaps the most well-known MBA case study. When you go to Amazon to look at a product, the product page will automatically suggest "Items bought together frequently." It's the most straightforward illustration of MBA cross-selling strategies.

BA is widely applicable to the in-store retail market, in addition to e-commerce models. Product placement and shelving optimization are very important to grocery businesses. At the grocery store, for example, you'll virtually always see shampoo and conditioner stocked next to each other.

Market Basket Analysis can also be seen in Walmart's famed beer and diapers association anecdote.

2. Telecom: As the telecom industry is becoming more competitive, corporations are paying particular attention to the services that customers use regularly. The industry, for example, has begun to combine TV and Internet bundles, as well as other cheap online services, to reduce churn.

A McKinsey research on how the telecom business may restructure plans efficiently using MBA can be found here: McKinsey

3. IBFS: For IBFS firms, tracing credit card history is a tremendously beneficial MBA opportunity. Citibank, for example, routinely deploys sales staff at large malls to entice potential customers with appealing on-the-go discounts. They also work with applications like Swiggy and Zomato to display clients a variety of discounts that they may take advantage of by using their credit cards.

Basket analysis is frequently used by IBFS groups to identify fraudulent claims.

Marzieh Vahidi Roodpishi and Reza Aghajan Nashtaei's research paper, published in Management Science Letters, examines how MBA might be utilized to better organize clients.

4. Medicine: Basket analysis is utilized in the medical industry to discover comorbid disorders and symptom analysis. It can also be used to determine the inherited genes or features and which of these are linked to local environmental factors.
The DRDO conducted a comprehensive investigation that linked clinical factors to the diagnosis of brain tumors.


Benefits of Market Basket Analysis

Although a three-decade-old technique, Market Analysis remains a viable option to garner insights in both the brick-and-mortar and eCommerce businesses. Following are some benefits of the analysis:

1. Increasing market share: Once a company has reached its peak, determining new ways to increase a market share becomes difficult. Market Basket Analysis can be used to combine demographic and gentrification data to identify where new retailers or geo-targeted marketing could be located. For example, if you've ever questioned 'why McDonald’s can be found everywhere?', the answer is most definitely MBA.

2. Behavior analysis: Understanding customer behavior patterns is a cornerstone of marketing. MBA can be used for everything from catalog design to UI/UX.

3. Optimization of in-store operations: Market Basket Analysis is useful not only in determining what goes on the shelves but also in determining what goes on behind the scenes. Geographic patterns are important in identifying the popularity or strength of certain products. MBA is increasingly being utilized to optimize inventory for each store or warehouse.

4. Campaigns and promotions: Market Basket Analysis is used to establish not only which items go together, but also which products are keystones in a company's product line. Companies may observe, for example, that often refilling gourmet bread leads to increased purchases of related gourmet jams and jellies.

5. Recommendations: OTT platforms such as Netflix and Amazon Prime benefit from MBA by gaining insight into the types of movies that customers view on a regular basis. A person who enjoyed Money Heist might be interested in other thriller shows as well.


Market Basket Analysis can be utilized in a variety of scenarios in addition to its popularity as a retailer's technique. A growing number of businesses are turning to market basket analysis to learn more about hidden links and associations. As industry leaders continue to investigate the technique's merits, a predictive form of market basket analysis is making inroads across multiple sectors to discover sequential purchases.


  • Market Basket Analysis: Anticipating Customer Behavior

    Srishti Chaudhary

    Srishti is a competent content writer and marketer with expertise in niches like cloud tech, big data, web development, and digital marketing. She looks forward to grow her tech knowledge and skills.

Frequently Asked Questions

The main objective of market basket analysis is to recognise what products are purchased by the customers so that they can plan an effective product placement, pricing, upsell and cross sell strategies.

Product placement in supermarkets and eCommerce stores are one of the most important applications of market basket analysis.

Apriori algorithm is the commonly used association rule algorithm in market basket analysis.

Market basket means a collection of particular products and services that are purchased and sold simultaneously throughout an economic system.

Market basket analysis helps you increase sales by providing you actionable insights into what products are popular among the customers, or are bought together. You can leverage this information to place such products at an easy-sighting location or together to drive sales.

Market basket analysis leverages an apriori algorithm that is useful for unsupervised learning.

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