Market Basket Analysis

Know your customers better and deliver what they want

Market Basket Analysis

Why retailers need to carry out market basket analysis

The science of identifying customer behavior, buying patterns and finding the relationship between products and content delivery by the retailer inside the store or on their online shop is known as market basket analysis.

It helps in identifying target markets, getting, retaining and growing customers through creating, delivering and communicating a superior customer experience. Technically, it’s a combination of association rule mining techniques to identify frequent patterns, affinities, correlations or casual structure among different sets of items in the transactional database.

Our data scientists help you in identifying the right point of sale to maximize your profits. They will find out the products/items associations that have a good buying history to sell them together. They will create a customer profile based on their buying patterns to help you reach the right target market. Ultimately, this helps in predicting sales on the right time at the right place for the right customer.

Implementing market basket analysis with AI and machine learning

Companies want to analyze different aspects of customer behavior inside the store. With different sets of data to analyze customer behavior of retail stores, businesses can classify data for defining the right product association, trip types, point of sale and marketing.

After analyzing consumer behavior inside the store, the next step is to apply AI techniques on different data sets. By applying ML algorithms powered by our data intelligence and analytics services, our data scientists use different mathematical formulas to identify association between products/items, which is helpful in creating appropriate segments.

How market basket analytics services can be used to boost retail businesses

  • Retailers can develop combo offers based on products often bought together.
  • Organize and place associated products/categories nearby inside the store.
  • Optimize the layout of the catalog of an eCommerce site.
  • Control inventory based on product demands and what products sell better together.
  • Implement customer segmentation and create customer profiling based on their buying pattern.
  • Classify different shopping trips for creating the best shopping experience.
  • Finding the best product association.
  • Creating more appealing product sets and identifying sales seasons and items.

Talk to us about your data complexities and let data intelligence address them