What is Market Basket Analysis?

Data MiningDatabaseData Structure

Market basket analysis does not represent an individual method and it represents a set of business issues related to understanding point-of-sale transaction data. Market basket data is transaction data that represents three fundamentally different entities such as users, orders (also called purchases or baskets or, in academic papers, item sets) and Items.

The order is the component data structure for market basket data. An order describe a single purchase event by a user. This can correlate to a user ordering several products on a website or to users buying a basket of groceries or to a user buying a several items from a catalog.

This contains the total amount of the purchase, the total amount, higher shipping costs, payment type, and whatever several data is efficient about the transaction. Consistently the transaction is provided a definite identifier. Sometimes the specific identifier needed to be cobbled from other data.

Single items in the order are described independently as line items. This data includes the value paid for the item, the number of items, whether tax should be charged, and possibly the cost (which can be used for calculating margin).

The item table also frequently has a connection to a product reference table, which provides more descriptive information about each product. This descriptive data must contain the product hierarchy and other data that can demonstrate valuable for analysis.

The user table is an optional table and should be accessible when a user can be identified, for example, on a website that required registration or when the user required an affinity card during the transaction. Although the users table can have interesting concepts, the dynamic element is the ID itself, because this can link transactions over time.

Tracking users over time makes it accessible to determine, for instance, which grocery shoppers “bake from scratch” something of intense interest to the creators of flour and pre-packaged cake mix. Such users can be identified from the frequency of their buys of flour, baking powder, and equal ingredients, the proportion of such buys to the users total spending, and the deficiency of interest in pre-packaged combines and ready-to-eat desserts.

These support wide insight into the business. In these cases, there are some repeat customers, hence the proportion of orders per users is near to 1; this supports a business opportunity to improve the number of sales per users. The multiple products per order can be near to 1, supporting an opportunity for cross-selling during the procedure of making an order.

Updated on 15-Feb-2022 06:54:39