Machine learning is hot nowadays and is helping companies uncover insights out of there data. Market basket analysis is a marketing statistical technique that decomposes a shopping basket into individual product levels and calculates the contribution of individual products to the total sale. This article will teach you the basics and help you understand how to use this analytical tool in your projects.
What is a Market Basket Analysis?
Have you ever wondered how Amazon knows what products to recommend to you? The answer lies in market basket analysis.
Market basket analysis is a statistical technique that allows retailers to identify relationships between items. In other words, it tells them what items are commonly bought together. This information can be used to make all sorts of business decisions, from product placement to targeted marketing.
There are two main types of market basket analysis: associative and rule-based. Associative analysis looks for patterns in customer behavior, while rule-based analysis relies on pre-determined rules (i.e. if A then B).
Both types of market basket analysis can be used to demystify the consumer's buying behavior: identifying what customers want before they know they want it. By understanding the relationships between items, retailers can make better decisions about which products to stock and how to promote them.
So next time you're wondering how Amazon knows what you want, remember: it's not magic, it's market basket analysis!
How do you do a Market Basket Analysis?
In order to do a market basket analysis, you need to have access to data that captures the items that people are buying. This data can come from a point-of-sale system, an ecommerce platform, or even surveys. Once you have this data, you need to analyze it to look for patterns in what people are buying together.
There are a few different ways to do this analysis, but one of the most popular is called Apriori. This algorithm looks for items that are frequently bought together and tries to identify which items are most important for driving sales of other items.
Once you've run the analysis, you'll have a list of items that people tend to buy together. You can use this information to make changes to your marketing or merchandising strategy. For example, if you find that people who buy item A also tend to buy item B, you can promote these items together or make sure they're displayed prominently in your store.
Market basket analysis can be a powerful tool for understanding how your customers shop and what they're looking for. By using this technique, you can make changes that will increase sales and improve the customer experience.
Why should you use a Market Basket Analysis?
There are many reasons to use market basket analysis. First, it can help you better understand your customers and what they are looking for. Second, it can help you identify trends and patterns in customer behavior. Third, it can help you optimize your marketing efforts by targeting specific products or services to specific customer groups. Finally, it can help you improve your overall customer service by identifying opportunities to cross-sell or upsell products and services.
Determine lift, support, and confidence ratio
When it comes to understanding machine learning and market basket analysis, one of the key things to understand is the lift, support, and confidence ratio. This simple calculation can help you determine how likely it is that a given product will be purchased along with another product.
Generally speaking, a higher lift indicates a stronger relationship between two products, while a higher support ratio indicates a more popular product. Confidence indicates whether the second product was effectively bought together with the first product.
In order to calculate the lift, support, and confidence ratio, you will need to take the following steps:
Firstly, you will need to identify the number of items in the market basket.
Secondly, you will need to calculate the support for the market basket. This is calculated by taking the total number of transactions in which the item appears and dividing it by the total number of transactions.
Thirdly, you will need to calculate the confidence for the market basket. This is calculated by taking the total number of transactions in which the item appears and dividing it by the total number of transactions in which at least one item in the market basket appears.
Finally, you will need to calculate the lift for the market basket. This is calculated by taking confidence and dividing it by support.
There are also several tools, like Knime, where you can automate this flow and reach results in no time!
Limits of the market basket analysis
There are a few limitations to market basket analysis that should be considered before using this technique to solve customer problems. First, market basket analysis requires a large amount of data in order to be effective. This can be difficult to obtain if you don't have access to customer purchase history or other types of data that can be used to determine what items are typically purchased together.
Second, market basket analysis can be time-consuming to set up and interpret, especially if you're not familiar with the statistical methods involved. Finally, keep in mind that this technique can only provide insight into past customer behavior - it can't predict future trends or reveal hidden relationships that you may not be aware of. Despite these limitations, market basket analysis is still a powerful tool that can help you understand your customers better and make more informed decisions about how to serve them.
Conclusion
If you're having trouble understanding what your customers want, or if you're simply curious about what kinds of things they tend to buy together, market basket analysis could be a helpful tool. By understanding how different items are related, you can make better decisions about what to stock in your store or which products to promote together. And although it may seem like a complex technique, once you get the hang of it, it's actually quite simple.