Understanding market basket analysis and product recommendation
What is market basket analysis
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 identify which products tend to be purchased together with the solution. Simply speaking, it allows retailers to identify relationships between the items that people buy.
Using machine learning, retailers can then recommend an additional item to the customer based on the item in their basket.
AI for market basket analysis
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? The relational data is difficult to correlate without the extra muscle that is artificial intelligence. In addition, market trends and behaviors are constantly shifting, regardless of the industry, especially in retail.
A valuable feature of product recommendation that wouldn’t be possible without AI is the ability to identify trends and relationships across various dependent variables such as location, demographic, and time. Just recall how a supermarket in Chinatown sells different goods than one in Suburbia!
Benefits of market basket analysis
- Optimize store layout
- Offer special deals and discount
- Create product combos to encourage sales for relational products
- Manage price and sales inventory
- Improve customer satisfaction and brand loyalty
Example of market basket analysis
For a real-time example, imagine this scenario. You go to the movie theater and swing by the concession stand for 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 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, from the example above, we would suggest an app be created and added to the cashier’s device. That app would recognize the scanned products 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, check out this posted customer story.
- About Retail & CPG industry
- Role of AI in revolutionizing e-commerce
- How to build a personalized recommender using data science
- Retail at the edge
This blog was originally published 3/19/2020 and has since been updated.