Developing Revenue-Increasing Recommendations: Best Practices for ML in Ecommerce

B.

Frequently bought together' recommendations aim to up-sell and cross-sell customers by providing product.

To develop an ML recommendation model for shoppers on an ecommerce website that increases revenue while following best practices, there are several things to consider. The use of Recommendations AI can assist in building, testing, and deploying such a system. The following are the answers to the question:

A. Use the Other Products You May Like recommendation type to increase the click-through rate. This recommendation type recommends products that are frequently viewed together by other customers. Although this can increase the click-through rate, it may not necessarily increase revenue since it does not take into account the customer's purchasing history or preferences.

B. Use the Frequently Bought Together recommendation type to increase the shopping cart size for each order. This recommendation type recommends products that are frequently purchased together with the product being viewed. This can increase the shopping cart size for each order, which in turn can increase revenue. This is a common practice in ecommerce and is likely to follow best practices.

C. Import your user events and then your product catalog to make sure you have the highest quality event stream. To develop an accurate recommendation model, it is essential to have high-quality data. User events such as clicks, purchases, and searches can provide insights into a user's behavior and preferences. The product catalog should also be imported to provide information about each product, including its attributes, such as brand, color, size, and category. Having high-quality data is a best practice for developing an accurate recommendation model.

D. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model. Using placeholder values for the product catalog is not a recommended practice since it does not provide accurate information about each product. Placeholder values can result in inaccurate recommendations, which can lead to a negative user experience and ultimately a decrease in revenue. It is better to wait until accurate data is available before testing the viability of the model.

In conclusion, the best practice for developing an ML recommendation model that increases revenue is to use the Frequently Bought Together recommendation type, import high-quality user events and product catalog data, and avoid using placeholder values.