How do you use algorithms to analyze market baskets?

1 What is market basket analysis?

Market basket analysis is based on the idea that if a customer buys a certain set of items, they are more likely to buy another set of items. For example, if a customer buys bread and butter, they are more likely to buy jam than if they buy bread and cheese. These sets of items are called itemsets, and the strength of their association is measured by metrics such as support, confidence, and lift. Support is the percentage of transactions that contain an itemset, confidence is the probability of buying an item given that another item is bought, and lift is the ratio of the observed confidence to the expected confidence.

2 How do you find itemsets?

To find itemsets, you need to scan the data and count the frequency of each item and each combination of items. This can be done by using a data structure called a hash tree, which stores the itemsets and their counts in a hierarchical way. A hash tree can reduce the memory and computation required to find itemsets, as it only stores the itemsets that meet a minimum support threshold and eliminates the need to search the entire data. However, building a hash tree can be time-consuming and complex, so some algorithms use simpler data structures such as arrays or lists.

3 What are some common algorithms?

Market basket analysis can be performed using various algorithms, such as Apriori, Eclat, and FP-growth. Apriori uses a bottom-up approach, finding frequent single-item itemsets and extending them to larger itemsets. While this reduces the number of candidates to be tested, it can be inefficient for large or sparse data sets. Eclat uses a top-down approach, finding all frequent itemsets and dividing them into smaller subsets. This reduces the number of candidates to be tested, but requires a lot of memory for large or dense data sets. FP-growth employs a divide-and-conquer approach, compressing the data into a frequent pattern tree and extracting the frequent itemsets. This requires only two scans of the data, making it faster and more scalable than Apriori and Eclat.

4 How do you choose an algorithm?

The choice of an algorithm depends on several factors, such as the size, density, and distribution of the data, the minimum support threshold, the number and length of the itemsets, and the available memory and computation resources. Generally, FP-growth is considered to be the most efficient and robust algorithm, as it can handle large and sparse data sets, and does not generate any candidates. However, it also requires more memory to store the frequent pattern tree, and may not perform well for very low support thresholds or very long itemsets. Apriori and Eclat are simpler and easier to implement, but they may suffer from performance issues for large or sparse data sets, or high support thresholds or short itemsets. Therefore, it is advisable to test different algorithms and compare their results and performance before choosing one.

5 How do you use the results?

The results of market basket analysis can be used for various purposes, such as product placement, pricing, promotions and recommendations. For product placement, you can arrange the items in your store or website according to their association, so customers can easily find and buy related items. Pricing can be adjusted to maximize revenue and profit. Promotions can be designed and targeted based on customer preferences and behavior. Recommendations can be made based on previous or current purchases. For example, discounts or coupons can be offered for items that are frequently bought together, or personalized emails or notifications for items that are likely to interest customers.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?