Retailers maximize the value of their customer data through data mining. Market Basket Analysis in Data Mining identifies items that customers have purchased or are likely to purchase together. Market basket analysis in data mining aids retailers in better comprehending and ultimately serving their customers by forecasting customers’ purchasing patterns.
Market Basket Analysis (Association Rule Mining) With Excel
6 ways to use market basket analysis
A market basket analysis’s data can be used in a variety of ways, such as:
1. Enhance customer satisfaction
Market basket analyses could be used by your business to enhance the shopping experience for customers and boost customer satisfaction. Retail analysts may use this data to assist their company in efficiently organizing its store, which may make it simpler for customers to find the goods they intend to purchase.
Additionally, by tailoring coupons and promotions to the wants and needs of your target audience using the information from a market basket analysis, you could possibly boost customer satisfaction. For instance, if you perform a market basket analysis for a grocery store and discover that most customers purchase a particular brand of paper towels, you might advise the retailer to hold a sale for that item. This brief price change might improve client satisfaction and boost sales.
2. Increase cross-selling
Retail analysts can use market basket analyses’ customer insights to increase sales through retail tactics like cross-selling. Displaying products next to one another in a store to entice customers to buy them together is known as cross-selling.
For instance, an analyst might advise a clothing retailer to display a matching hat, belt, and purse on the same mannequin to encourage customers to buy the items at the same time based on historical purchasing patterns.
3. Improve advertisements
A market basket analysis can give your business’ marketing team the data they need to develop more precise marketing and advertising campaigns. They can group products in advertisements based on consumer purchasing patterns, which could draw in more customers and boost sales. For instance, if customers of an apparel company frequently buy rain boots and umbrellas at the same time, the marketing department might develop an advertisement that features both products.
4. Adjust store layouts
In order to better design its store layout, your business may use market basket analyses, which could persuade customers to buy more products. A business may alter the layout of its store by moving groups of related products, rearranging shelves, or setting up transient product displays.
For instance, a grocery store might relocate its frozen food section from the front to the back of the establishment. This might tempt customers to browse other aisles before deciding on frozen goods, which, according to data mining, are typically the last thing they choose when they visit a store. This new layout could have a significant impact on how customers shop and possibly increase sales.
5. Create online recommendations
You could produce automatic, logically-based online recommendations for customers if you perform market basket analyses for an online retailer. Effective recommendations can persuade customers to buy related products in addition to the ones they originally planned to. For instance, if a customer adds a tent, a flashlight, and a lantern to their online shopping cart, a sleeping bag might be suggested to them.
6. Identify other relationships
Market basket analyses can be used to help you identify complex relationships outside of retail and unlocking consumer purchasing patterns. For instance, an analyst could assist a pharmacy in identifying the connections between prescription ingredients and medical diagnoses. In contrast, a data analyst could assist a bank in spotting fraud through analysis of a customer’s typical credit card usage.
What is a market basket analysis?
Data miners use a market basket analysis to find connections between items in a data set in order to spot trends and patterns. A market basket analysis typically reveals which products customers frequently purchase together, aiding analysts in predicting what potential new customers may buy.
Association rules in market basket analysis
In market basket analysis, a single item’s “support” is measured by how frequently customers purchase it in comparison to other goods. The majority of transactions in a data set are typically reviewed by analysts to help them spot trends and verify the validity of support values for products. You can choose which items to set at a higher price in order to increase profits by being aware of the support value of the goods your business sells. You can calculate an items support by using this equation:
Support = total items in a set of transactions / number of transactions involving item X
This equation defines “support” as the frequency with which customers purchase “item X.” For instance, a data analyst who works for a grocery store may employ the equation shown below:
55 apples out of 100 total items in a set of transactions constitute support, which equals 0 55.
How often customers buy two products in a set is measured by the term “confidence” in market basket analysis. When a customer purchases the first item, a higher confidence value indicates that they are more likely to purchase the second item as well. You can design more efficient store layouts and develop targeted marketing campaigns by knowing the historical trends and the confidence value for the products your company sells. This could lead to an increase in sales. You can use the following equation to determine an item set’s confidence:
Confidence is equal to the product of the number of transactions involving item X and item Y divided by the number of transactions involving item X.
The term “confidence” in this equation denotes how strongly “item X” and “item Y” are associated. For instance, a market basket analyst for a bookstore may employ the formula below:
Confidence (magazines and books) = (50 magazines + 50 books) / (50 books) = 0. 5.
The actual confidence value versus the anticipated confidence value for an item set is referred to as the “lift” in the MBA. Data analysts may use lift to ascertain whether selling one item has an impact on sales of another item.
A lift of more than 1 may strongly indicate a trend in which customers frequently buy the first and second items as a set. A lift value of less than 1 on the other hand indicates that customers rarely buy two items together. You can calculate an items lift using this equation:
Lift equals (X and Y’s confidence values) / (Y’s support),
The likelihood that a customer will buy “item Y” when they also buy “item X” is referred to in this equation as “lift.” For instance, an analyst employed by the aforementioned bookshop might apply the equation shown below:
Lift (magazines) = 0. 100 total items in a set of transactions divided by 50 magazines is equal to 5.
What are three metrics used in market basket analysis?
The market basket tools in Alteryx Designer display the three most widely used metrics for this analysis: support, confidence, and lift. On the internet, you might also come across discussions of leverage and conviction.
Which algorithm is used in market basket analysis?
Market basket analysis frequently employs the well-known Association Rule algorithm known as Apriori Algorithm.