Market Basket Analysis: What is it?
Market Basket Analysis identifies products purchased together. A practical guide with support, confidence, lift, and real retail examples.
Market Basket Analysis is a data mining technique used to analyze relationships and interactions within consumption data. It is mainly applied in retail to understand customer behavior patterns and extract actionable insights to drive sales.
Part of this analysis involves processing large volumes of data to identify the main product combinations chosen by consumers in a given store, as well as their preference for one or more categories and products.
How Market Basket Analysis is used
Market Basket Analysis (or "MBA") emerged from the need to understand consumer purchasing preferences. This type of analysis was enabled by the adoption of digital sales systems, which replaced manual inventory and purchase records.
Today, it is possible to collect a high volume of highly complex and accurate information about what is sold across one or more stores, and run deeper analyses on all scenarios involved in the choice of specific items.
Dataset mining helps generate insights about transactional and relational elements within the analyzed context. This practice is used by companies across multiple industries to boost sales by extracting relevant information from available data and applying it to key decision-making, such as: promotions, optimal product bundles to offer, product placement at points of sale, and even whether to continue selling one or more product lines.
This analysis helps identify consumer buying habits by using statistical and mathematical techniques to find associations between items present in customers' shopping baskets, making it possible to understand preferences across different sales channels, from a small grocery store to a large e-commerce platform.
Types of Market Basket Analysis
Predictive Analysis: This type of analysis considers which items were purchased in sequence to determine product cross-selling opportunities.
Descriptive Analysis: Even though the goal of Market Basket Analysis is to extract insights from consumer shopping baskets, descriptive techniques are applied during data collection: who bought what, when, how much was spent, etc.
Differential Analysis: Differential market basket analysis considers data from different stores or sellers, as well as purchases from different customer groups on different dates and at different times, along with other information considered relevant to understanding sales motivation. This set of information supports the identification of consumption patterns that may be favorable to sales, enabling data analysts to find the factors responsible for driving results.
Using Algorithms for Market Basket Analysis
Algorithms and programming languages that include association rules are commonly used to run MBA because they provide mechanisms to understand the frequency of elements that occur together in a dataset and seek to find their relationship with others that occur more frequently than expected. Some algorithms that use this rule and can be applied in this type of analysis are:
Apriori
AIS
SETM
NBMiner
Opusminer
RKEEL
Examples of Market Basket Analysis in practice
A good way to contextualize something that may seem distant is by using examples we see every day. One of the best-known use cases today is online retail and marketplaces, which use algorithms to understand consumer preferences and offer the most suitable products based on needs or interests.
Some examples of this analysis are Brazil's most popular e-commerce platforms, such as Mercado Livre, Magazine Luiza, and Amazon, which include sections on their websites showing potential buyers the most purchased items and also the items frequently bought together with what has been added to the shopping cart.
Another clear example is using this analysis in marketing to deliver more personalized ads, showing products the consumer is more likely to prefer.
A well-known example of effective MBA use involved major U.S. retailer Target, which was involved in a controversy after predicting a teenager's pregnancy before her own father knew. By analyzing purchase patterns among pregnant customers, the retailer was able to identify common products in their baskets and build a statistical system capable of even predicting the likely baby due date.
But this does not happen only in digital stores. We also frequently see the outcomes of this analysis in physical retail. For example: if a bookstore notices that consumers buy a specific book together with pens or a specific bookmark, it will place these items near the books to improve the customer experience, increasing average ticket size and each store's productivity.
Market Basket Analysis is a strong example of how a well-defined data strategy and data culture can help drive sales and improve customer relationships.
Large retail companies already use this strategy to guide decision-making, driven by the need to deeply understand their customers in order to offer what they are looking for quickly and conveniently.
Want to learn more about how to become data-driven? Join Erathos and start your data journey now!
Market Basket Analysis is a data mining technique used to analyze relationships and interactions within consumption data. It is mainly applied in retail to understand customer behavior patterns and extract actionable insights to drive sales.
Part of this analysis involves processing large volumes of data to identify the main product combinations chosen by consumers in a given store, as well as their preference for one or more categories and products.
How Market Basket Analysis is used
Market Basket Analysis (or "MBA") emerged from the need to understand consumer purchasing preferences. This type of analysis was enabled by the adoption of digital sales systems, which replaced manual inventory and purchase records.
Today, it is possible to collect a high volume of highly complex and accurate information about what is sold across one or more stores, and run deeper analyses on all scenarios involved in the choice of specific items.
Dataset mining helps generate insights about transactional and relational elements within the analyzed context. This practice is used by companies across multiple industries to boost sales by extracting relevant information from available data and applying it to key decision-making, such as: promotions, optimal product bundles to offer, product placement at points of sale, and even whether to continue selling one or more product lines.
This analysis helps identify consumer buying habits by using statistical and mathematical techniques to find associations between items present in customers' shopping baskets, making it possible to understand preferences across different sales channels, from a small grocery store to a large e-commerce platform.
Types of Market Basket Analysis
Predictive Analysis: This type of analysis considers which items were purchased in sequence to determine product cross-selling opportunities.
Descriptive Analysis: Even though the goal of Market Basket Analysis is to extract insights from consumer shopping baskets, descriptive techniques are applied during data collection: who bought what, when, how much was spent, etc.
Differential Analysis: Differential market basket analysis considers data from different stores or sellers, as well as purchases from different customer groups on different dates and at different times, along with other information considered relevant to understanding sales motivation. This set of information supports the identification of consumption patterns that may be favorable to sales, enabling data analysts to find the factors responsible for driving results.
Using Algorithms for Market Basket Analysis
Algorithms and programming languages that include association rules are commonly used to run MBA because they provide mechanisms to understand the frequency of elements that occur together in a dataset and seek to find their relationship with others that occur more frequently than expected. Some algorithms that use this rule and can be applied in this type of analysis are:
Apriori
AIS
SETM
NBMiner
Opusminer
RKEEL
Examples of Market Basket Analysis in practice
A good way to contextualize something that may seem distant is by using examples we see every day. One of the best-known use cases today is online retail and marketplaces, which use algorithms to understand consumer preferences and offer the most suitable products based on needs or interests.
Some examples of this analysis are Brazil's most popular e-commerce platforms, such as Mercado Livre, Magazine Luiza, and Amazon, which include sections on their websites showing potential buyers the most purchased items and also the items frequently bought together with what has been added to the shopping cart.
Another clear example is using this analysis in marketing to deliver more personalized ads, showing products the consumer is more likely to prefer.
A well-known example of effective MBA use involved major U.S. retailer Target, which was involved in a controversy after predicting a teenager's pregnancy before her own father knew. By analyzing purchase patterns among pregnant customers, the retailer was able to identify common products in their baskets and build a statistical system capable of even predicting the likely baby due date.
But this does not happen only in digital stores. We also frequently see the outcomes of this analysis in physical retail. For example: if a bookstore notices that consumers buy a specific book together with pens or a specific bookmark, it will place these items near the books to improve the customer experience, increasing average ticket size and each store's productivity.
Market Basket Analysis is a strong example of how a well-defined data strategy and data culture can help drive sales and improve customer relationships.
Large retail companies already use this strategy to guide decision-making, driven by the need to deeply understand their customers in order to offer what they are looking for quickly and conveniently.
Want to learn more about how to become data-driven? Join Erathos and start your data journey now!