Market basket analysis is a technique used by retailers to identify the products that are commonly purchased together. By understanding the relationships between different products, retailers can optimize their product placement, pricing, and promotions to increase sales and improve customer satisfaction.

To perform market basket analysis, retailers typically use machine learning algorithms to analyze historical transactional data. The solution takes into account the products that were purchased in each transaction, as well as other relevant information such as the time of purchase, the location of the store, and the demographic information of the customer. By analyzing this data, the solution can identify patterns and relationships between different products, such as which products are commonly purchased together and which products are complementary or substitutes.

Implementing this solution can help retailers to increase sales by optimizing product placement and promotions. For example, by identifying products that are commonly purchased together, retailers can place these items in close proximity to each other to encourage additional sales. Additionally, by identifying products that are complementary or substitutes, retailers can adjust pricing and promotions to encourage customers to purchase a specific set of items. This solution can also be used for the customer segmentation, which can help the retailers to adjust their products and services to the different customers' needs and increase the customer satisfaction.