How to sell more using personalized product recommendations

Hey there 🙋‍♂️, it's Thomas.

In today's issue, I will talk about how you can increase your sales by using data for personalized product recommendations.

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Recently, I had the idea to use data science to help e-commerce stores increase their sales by predicting customer preferences and sending them personalized recommendations.

I approached someone I knew who owned an e-commerce store and asked if I could run an experiment using their sales data. They agreed and provided me with the data, which included past sales information but no data on customer preferences.

To infer product preferences based on past buying behavior, I decided to build a small recommendation model that suggests products to customers based on their past purchases. However, since I did not have any data on customer preferences, I had to come up with a creative solution. I remembered that market basket analysis is a technique that can identify relationships between items that are frequently purchased together, and thought that this could be a useful approach for my recommendation model. By identifying these relationships, I could suggest products to customers that are frequently bought together by others, in the hope that they might also be interested in these other products.

Introduction to Market Basket Analysis

Market basket analysis is a statistical technique used to identify the relationships between different items that are frequently purchased together. It is a popular tool used by retailers to understand their customers' purchasing habits and to make decisions about product placement, pricing, and promotions.

What is Market Basket Analysis?

Market basket analysis involves analyzing customer transaction data to identify the items that are frequently purchased together. For example, if a customer frequently purchases bread and milk together, it is likely that these items are related and may be appealing to other customers as well. By identifying these relationships, retailers can make informed decisions about how to group and display products in their stores, as well as which products to offer discounts or promotions on.

The most famous case is probably Amazon. They use this technique to show related products in their: “Customers Who Bought This Item Also Bought” section.

Why Should You Do Market Basket Analysis?

The ultimate goal of market basket analysis is to increase revenue for businesses. Here are a few ways in which it can achieve this:

1. Increase sales: By understanding which products are frequently purchased together, retailers can group and display related products in a way that is more appealing to customers. This can lead to an increase in sales.

2. Improve customer loyalty: By offering customers the products they want and need, retailers can increase customer loyalty and retention.

3. Identify new product opportunities: Market basket analysis can help retailers identify potential new product opportunities by revealing relationships between items that were previously unknown.

How Do You Do Market Basket Analysis?

There are several steps involved in performing market basket analysis:

1. Collect and organize data: The first step is to collect transaction data from your customers. This data should include information about the products purchased, the date and time of the purchase, and wether they were bought in the same batch.

2. Clean and preprocess the data: Next, you will need to clean and preprocess the data to ensure that it is ready for analysis. This may involve correcting errors, filling in missing values, or aggregating data into relevant categories.

3. Identify frequently purchased items: Once you have cleaned and preprocessed the data, you can use a variety of techniques, such as association rules or clustering algorithms, to identify the items that are frequently purchased together.

4. Analyze the results: By now you have identified the frequently purchased items, and can use this to analyze the results to understand the relationships between different products.

5. Recommend products: The final step is to recommend new products to customers in some way. One common method is to include a "Customers also bought..." section on your website, similar to what Amazon does. However, implementing this feature typically requires some web development skills. In my case, I’m going to send these recommendations through email, which is easier for now. Would be interesting to see how much extra revenue we can create with this.

Conclusion

I’m still working on the analysis, but thought it would be fun to bring you along in my approach. Some preliminary results show that I probably have too little data to do personalized recommendations for everyone, so I’m going to explore other techniques for people that I don’t have a recommendation for.

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See you next week!

Thomas

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