Understanding market basket analysis and product recommendation

Understanding market basket analysis and product recommendation

Market basket analysis – or product recommendation – is where you take inventory and point-of-sale data to predict which combinations of products will sell the best. With a clean sales history, businesses can then identify which products tend to be purchased together with the solution. Using machine learning, we are then able to recommend an additional item to recommend to the customer based on the item in his/her basket.

This may seem like an obvious and simple business strategy. After all, why wouldn’t I be able to offer customers an additional product based on historical purchase trends? But that relational data is difficult to correlate without the extra muscle that is artificial intelligence. Adding to that, market trends and behaviors are constantly shifting, regardless of industry but especially so in retail. An especially valuable feature of product recommendation that wouldn’t be possible without AI is the ability to identify trends and relationships across various dependent various such as location, demographic, and time. Just recall how a supermarket located in Chinatown sells different goods than one located in suburbia!

For a real-time example, imagine this scenario: you go to the movie theater and swing by the concession stand for some snacks. You’re at the register with a bottle of water and small popcorn in hand. If the theater employed a market basket analysis solution, it would know that the product with the highest probability of purchase to alongside the water-popcorn combo is a box of Red Hots. And if you, the customer, had walked up to the register with all three in hand or a soda instead of water, the concession stand would have known to recommend a different next product entirely.

To fully leverage your market basket analysis solution, which is mostly utilized within the retail industry, one typically needs an app to react in real-time. So, taking again from the example above, we would suggest an app be created and added onto the cashier’s device. That app would recognize the products scanned and immediately prompt the employee to verbally suggest the appropriate product for purchase with a pop-up notification.

Market basket analysis and product recommendation has been a common practice implemented by businesses in the retail industry for years. But with digital transformation spanning its reach across all verticals, many businesses are recognizing the opportunity to operate with unprecedented efficiency and intelligence. For those without foresight, they too, are forced to join the wave of digital transformation as their competitors begin to offer lower price points. To read how Neal Analytics implemented a product recommendation solution for Majid Al Futtaim, a retail conglomerate, take a look at our posted customer story.