Unsupervised learning can really improve how recommendation systems work for online shopping. It helps businesses understand their customers better by organizing them into different groups based on their buying habits.
Here are two important techniques:
Behavioral Clustering: This means finding customers who often buy similar items.
Market Basket Analysis: This looks at which items people tend to buy together.
By using these methods, businesses can create marketing strategies that feel more personal. This makes shopping better for customers and helps boost sales.
For example, when a store suggests products that go well together based on what a customer usually buys, it makes the recommendations more relevant. This can lead to more sales, as customers are more likely to buy things that are suggested to them.
Unsupervised learning can really improve how recommendation systems work for online shopping. It helps businesses understand their customers better by organizing them into different groups based on their buying habits.
Here are two important techniques:
Behavioral Clustering: This means finding customers who often buy similar items.
Market Basket Analysis: This looks at which items people tend to buy together.
By using these methods, businesses can create marketing strategies that feel more personal. This makes shopping better for customers and helps boost sales.
For example, when a store suggests products that go well together based on what a customer usually buys, it makes the recommendations more relevant. This can lead to more sales, as customers are more likely to buy things that are suggested to them.