Market basket analysis is a Data Science concept that aims to understand customers’ buying habits to uncover associations and relationships between products. It focuses on analyzing the contents of customers’ shopping carts to identify frequently purchased items together. If you want to understand the concept of Market Basket and how Data Science professionals use this technique, this article is for you. This article will give you a detailed introduction to Market Basket Analysis in Data Science.
What is Market Basket Analysis & How Does it Help?
Market basket analysis is a Data Science concept that aims to understand customers’ buying habits to uncover associations and relationships between products. It focuses on analyzing the contents of customers’ shopping carts to identify frequently purchased items together.
By identifying associations between products, companies can gain valuable insights into customer behaviour and make informed decisions about product placement, cross-selling, and promotional strategies.
Let’s understand more about Market Basket Analysis in Data Science and how it helps businesses by taking an example of a real-time business problem.
Take the example of a supermarket chain that wants to optimize its product placement strategy. The supermarket can find out which items are frequently purchased together by applying Market Basket Analysis to customer transaction data.
For example, the analysis may reveal that customers who buy diapers are also likely to buy baby wipes. Armed with this knowledge, the supermarket can strategically place these related items nearby to increase the likelihood of cross-selling and generating additional sales.
Process of Market Basket Analysis You Can Follow
It doesn’t matter what language or tool you use for analyzing market baskets. Below is the process you can follow:
- Gather transactional data that includes information about customer purchases, such as item IDs or descriptions, transaction IDs, and timestamps.
- Clean and preprocess the data to remove duplicates, handle missing values, and transform it into a suitable format for analysis.
- Apply association rule mining algorithms, such as Apriori or FP-Growth, to discover frequent itemsets and association rules. These algorithms help identify which items are commonly purchased together and the strength of their relationships.
- Calculate the support, confidence, and lift values for the identified itemsets and association rules. These measures provide insights into the frequency, strength, and significance of the associations.
- Interpret the results and visualize the findings using statistical graphs, such as support, confidence, and lift ratio graphs. It helps understand the relationships between products and identify actionable insights for business decision-making.
Data Visualization Techniques for Market Basket Analysis
Here are some of the valuable data visualization techniques that you can use as a Data Science professional for analyzing market baskets:
- Support Analysis: It measures the frequency or occurrence of item combinations in the shopping baskets. It helps identify popular item sets and assess their significance in the analysis.
- Confidence Analysis: It illustrates the likelihood of purchasing one item given the presence of another item in the basket. It helps determine the strength of the association between products and facilitates cross-selling opportunities.
- Lift Ratio Analysis: It measures the strength of association between items by comparing the observed frequency of co-occurrence with the expected frequency under independence. It allows businesses to identify which combinations of items are more likely to be bought together.
So, Market basket analysis is a Data Science concept that aims to understand buying habits of customers to uncover associations and relationships between products. It focuses on analyzing the contents of customers’ shopping carts to identify frequently purchased items together. I hope you liked this article on the concept of Market Basket Analysis in Data Science. Feel free to ask valuable questions in the comments section below.