Market Basket Analysis

Discover hidden customer purchasing patterns and improve your cross-selling and upselling efforts with real-time analysis of customer purchasing habits

This turnkey enterprise AI solution helps retailers optimize their sales by identifying the products that are commonly purchased together, and enables data-driven decisions about product placement, pricing, and promotions to improve customer retention and lifetime value. Schedule a call with us to speak with our in-house experts to explore how this solution can be applied to your business.

Key Metrics

Cost of inventory reduced by


Inventory turnover increased by


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.


  • Collect and analyze historical transactional data from retail sales, including product and customer information.
  • Utilize advanced data mining and machine learning techniques to identify patterns and relationships between products.
  • Continuously update the analysis with real-time transactional data to identify new trends and patterns, and to incorporate external data sources such as weather, time, and socio-economic data.
  • Monitor and evaluate the impact of these decisions on sales, customer satisfaction, and product placement, and perform A/B testing to measure the effectiveness of different strategies
  • Continuously update the analysis and fine-tune the algorithms to improve the accuracy of the insights and stay ahead of changing consumer trends.
  • Use the insights gained from the analysis to inform decisions related to product placement, pricing, promotions, product recommendations, and personalization

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* Values are approximates arrived at based on earlier experience and/or existing literature. Contact us to find out how you can measure the ROI on this solution for your business