Market Basket Analysis: That’s How You Serve Your Customers Efficiently in the 21st Century!

market-basket-analysis
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Omkar Khapare

Machine learning applications can be seen all around the world.

Whether they are re-marketing or re-targeting techniques, recommendation engines constantly tell you what to buy.

Those who leverage machine learning’s functionalities are better able to market their products and services.

Today, you will read about Market Basket Analysis (MBA)!

Amazon and other eCommerce sites apply it to maximize their sales.

You can do it as well!

Keep reading to learn how.

Read About Sentiment Analysis: How Sentiment Analysis Can Help You With Customer Insight?

Market Basket Analysis

                                        

Ever wondered how Amazon suggests exactly the same or related items you’ve been searching before and after you make the purchase?

This works specifically to improve sales and is called Market Basket Analysis. It’s also known as Association Rules.

In the business world, market basket analysis helps serve consumers by studying their purchasing behavior.

Applications of MBA in the Real World

1. Associated Purchases

You buy a certain product online, but guess what, eCommerce sites don’t want to stop you there.

They show you associated products, creating a perfect link of continuous product chain for you to buy.

For example, if you buy a shirt online, they might offer you a bundle of choices for pants which is a fair suggestion.

2. Friendly Products

Items that are associated can be put beside each other.

Like bread and butter, blade and shaving cream, chocolate and cream, and so on.

It forces a higher probability that a customer will buy both the products wherein initially, they only intended to buy a single product.

3. Detection of Fraud

Credit card usage data can be used to predict the purchase behavior of a person, and possibly used to detect fraud based on any out-of-order purchase at any given time.

4. Customer Behavior

Associating a purchase with demographic and socio-economic data results in useful insights for marketing purposes.

Socio-economic data such as age, gender, preference, and likes or dislikes, and more are considered.

Using such insights, one can predict if a customer will buy their product or not.

Conclusion

Stay tuned for more on Machine Learning and its applications in our next few blogs.

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