Market basket analysis, also referred to as affinity analysis, uses machine learning to increase sales by analyzing customer purchasing patterns. You can use market basket analysis for analyzing large data sets, such as purchase history, to reveal products and product groups, that customers are likely to purchase together.
Market basket analysis is a very valuable marketing analytics tool. Identifying relationships between items that people buy provides tremendous opportunity to know your customers better which, in turn, helps you improve customer experience. Here’s a look at some of the top areas where market basket analysis can have a huge impact.
The goal of cross-selling is to sell an additional product or service to an existing customer. Market basket analysis assesses whether the purchase of the product increases the likelihood of the purchase of other products, so that marketers can bundle products or develop other cross-selling strategies.
#2. Recommendation Engines
A recommendation engine suggests products based on the interest of the customer. It frequently uses market basket analysis of data such as items in the shopping basket/cart, browsing history and past purchases to recommend related products that the customer is likely to be interested in.
#3. Product Placement
Identifying products that are often purchased together through market basket analysis helps optimize the placement of products in stores, in catalogs and on a web site. Placing those associated items close by increases encourages the buyer to purchase multiple products.
#4. Affinity Promotion
Affinity promotions are designed based on the inclusion of associated products. Examples include creating more attractive discount plans, incentives and loyalty programs that increase spending and improve customer experience.
#5. Customer Behavior
Associating purchases with demographics and socio-economic data, such as age, gender, ethnicity, and income, may produce very useful results for understanding customer behavior. This helps to develop marketing strategies that break customers into segments and designs marketing activities that target the segments most likely to respond to your efforts.
#6. Inventory Management
Understanding items that are frequently bought together can help you manage your inventory better. For example, you can avoid stockouts when you run a promotion by predicting how the sale of products related to the promotional item may increase.
#7. Store and Website Traffic
Selling low-margin products can drive the sale of high-margin products. Understanding their associations can help you develop loss-leader strategies with a low-priced product that increases store and website traffic, and increases overall revenue and profits.
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Who doesn’t want to make more money? The Actian Data Platform makes it easy to connect, manage and analyze data so that you can delve into detailed associations and product relationships using marketing analytics such as market basked analysis. The results can help you increase sales, improve customer experience and cut costs.