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During promotion of new products, identifying potential loyal customers is advantageous to offer them discounts on products. As these customers will return after this initial incented price, it becomes very essential to know their shopping pattern before they make any purchase on a new product.
Discounts come at the expense to companies as these deals equate to lower revenue. Therefore it is important that these costs translate to loyal customers who will become repeat product buyers within and outside of product offer periods.
Various ML algorithms have performed well by implementation on basket data. Some basic statistical qualities that identify interesting patterns are 'support' and 'confidence'. Support measures how frequently a pattern occurs in the data, whereas confidence measures the predictive power of a pattern. This makes it possible to examine patterns based on their importance.
Furthermore, optimising store layout by putting items that are often bought together in near proximity to each other have been valuable to drive business in smarter ways.