Instacart is a widely used grocery delivery service. Customers can order groceries from various retailers through its mobile app or website. Instacart market basket analysis has been able to leverage data to provide a better shopping experience for its customers. One such application of data analytics is market basket analysis. Using a market basket analysis method, businesses understand the relationship between frequently purchased products.
This data can be used to optimize product placement and pricing strategies, leading to increased sales and customer satisfaction. We will perform market basket analysis on Instacart’s transaction data to identify the most frequently co-occurring items in customers’ orders. We will also use association rule mining to generate insights into customer behavior and make recommendations for product placement and pricing strategies.
How The Data Was Prepared For Analysis
Data collection and preprocessing are critical steps in the market basket analysis process. The data will be described in this section. Sources and how the data was prepared for analysis.
The Instacart dataset used for market basket analysis was obtained from Instacart’s public research database. The dataset contains over 3 million orders made by over 200,000 users on the Instacart platform. Each order contains a unique order ID, user ID, and the time and date of the order. It also includes information about the products in the order, such as the product ID, product name, aisle, and department.
Before the data could be used for market basket analysis, it needed to be preprocessed to ensure that it was in a suitable format for analysis. This involved several steps, including:
Removing Irrelevant Information
Some of the information in the dataset, such as user and product names, were unnecessary for market basket analysis and were removed to simplify the dataset.
Aggregating Data
The dataset was organized by order, but we needed to aggregate the data at the product level for market basket analysis. This involved grouping the orders by product ID and counting the times each product was purchased.
Encoding Data
To conduct market basket analysis, we needed to convert the transactional data into a suitable format for association rule mining. This involved encoding the data into a binary matrix, where each row represented a unique order, each column represented a unique product, and the value in each cell indicated whether or not the product was included in the order.
Filtering Data
To remove noise and improve the quality of the analysis, we filtered out infrequent and irrelevant products from the dataset. This involved setting a minimum support threshold, below which products were considered infrequent and removed from the analysis.
Data collection and preprocessing are crucial steps in market basket analysis that ensure the data is in a suitable format for analysis and can generate meaningful insights.
Exploratory Data Analysis Visualising The Data
Exploratory data analysis (EDA) is an important step in market basket analysis that involves visualizing the data to identify patterns and trends. Here, we’ll show you how the Instacart dataset was explored and visualized to gain insights into customer purchasing behavior.
To begin the EDA process, we first analyzed the distribution of products across departments and aisles to understand which products were popular and which were less frequently purchased. This analysis revealed that the most popular departments were produce, dairy, and snacks, while the least popular departments were bulk and specialty.
We used clustering techniques, such as k-means clustering, to similar group products together based on their co-occurrence patterns. This analysis revealed that products in the same category, such as dairy products or snack foods, tended to cluster together.
Using Association Rule Mining
Market basket analysis is a powerful data mining technique to identify frequently co-occurring items in customer transactions. This technique uses association rule mining to analyze transactional data and identify patterns in customer purchasing behavior.
Association rule mining was used to identify products that were frequently purchased together. This analysis involved calculating support, confidence, and lift values for each product combination in the dataset.
Support measures the frequency of a particular product combination in the dataset, while confidence measures the likelihood that a customer who purchased one item also purchased another item in the same transaction. Lift measures the strength of the association between two items and compares the observed frequency of their co-occurrence to what would be expected by chance.
Providing Insights On Customer Behaviour
The final stage of a market basket analysis project involves interpreting the results and providing insights and recommendations based on the findings. In this section, we will discuss the insights and recommendations that can be drawn from the Instacart dataset.
Another insight from the analysis is that certain products strongly associate with others. For example, customers who purchase milk are highly likely to purchase bread, while those who purchase eggs are highly likely to purchase cheese. This data can be used to optimize product placement strategies, such as placing milk and bread next to each other on store shelves or offering a discount on cheese when purchased with eggs.
The analysis revealed that some products are more likely to be purchased together at specific times or weeks. For example, customers who purchase beer are likelier to purchase snacks on weekends, while customers who purchase fresh produce are likelier to purchase these items on weekdays. This information can be used to optimize pricing strategies, such as offering discounts on beer and snacks on weekends or fresh produce on weekdays.
The Analysis And Potential Areas For Future Research
As with any data analysis, there are limitations to market basket analysis that should be considered. The limits will be discussed in this section. The Instacart market basket analysis and potential areas for future research.
Another limitation is that the analysis only considers past purchasing behavior and does not consider other factors that may influence customer behavior, such as personal preferences, product quality, or pricing. Future research could explore ways to incorporate these factors into the analysis to provide a more comprehensive understanding of customer behavior.
The Instacart dataset only includes data from a single retailer and may not represent broader consumer behavior. Future research could expand the analysis to include data from multiple retailers or industries to provide a more comprehensive understanding of market basket analysis.
Frequently Asked The Question
How do you analyze a market basket?
Market basket analysis is a technique that involves analyzing customer transaction data to identify patterns and relationships between products. To conduct market basket analysis, you must first collect and preprocess transaction data, such as customer orders, and then use association rule mining algorithms to identify frequently co-occurring items.
What is a real-life example of market basket analysis?
A real-life example of market basket analysis is a retailer analyzing customer transaction data to identify the most frequently co-occurring items. For example, a grocery store might use market basket analysis to identify customers who purchase bread and are likelier to purchase butter and eggs.
What is the dataset description of Instacart?
The Instacart dataset consists of over 3 million orders made by more than 200,000 users on the Instacart platform. The dataset includes transactional data such as the order ID, product ID, user ID, and the time and date of the order. It also includes information about the products, such as product name, aisle, and department.
What is the market share of Instacart?
As of 2021, Instacart has a market share of around 30% in the US online grocery delivery market, making it the largest player in the industry. The business has expanded significantly during the past few years due to the increased demand for online grocery shopping during the COVID-19 pandemic.
Who is Instacart’s target market?
Instacart’s target market is primarily busy urban and suburban households who value the convenience of having groceries delivered to their doorstep. The company’s platform appeals to many customers, including working professionals, busy parents, and elderly individuals who may have difficulty leaving their homes to shop for groceries.
Conclusion
An effective method, market basket analysis, can help businesses like Instacart optimize their product placement and pricing strategies. By analyzing transaction data, we identified the most frequently co-occurring items in customers’ orders and generated insights into customer behavior.
These insights can be used to optimize Instacart’s product placement and pricing strategies, leading to increased sales and customer satisfaction. As businesses rely on data-driven decision-making, market basket analysis will continue to be essential for identifying patterns and generating insights that can drive business growth.