Returns are an inevitable aspect of the e-commerce landscape, but they don't have to be a headache for online retailers. With the right tools and strategies in place, businesses can streamline the returns process and minimize the associated costs. One such tool that has been gaining traction in the industry is the size recommender feature offered by Kiwi Sizing.

Product returns pose several challenges for online retailers. Not only do they result in lost revenue and increased operational costs, but they also impact customer satisfaction and loyalty. One of the primary reasons for returns is sizing issues. When customers receive items that don't fit as expected, they're more likely to initiate a return, leading to additional expenses for the retailer.

Kiwi sizing, a leading provider of e-commerce solutions, offers a comprehensive size recommender feature designed to address the sizing challenge head-on. By leveraging advanced algorithms and machine learning technology, Kiwi's size recommender helps customers find the perfect fit for their body type and preferences.

The size recommender works by analyzing various factors, including body measurements, previous purchase history, and customer feedback. Using this data, the algorithm generates personalized size recommendations for each individual shopper, ensuring a higher likelihood of satisfaction with their purchases.

From the chart that you created. Just click on Start setting up recommender, then you will see 3 options for the recommender to use.

1. Advanced Apparel Recommender: The user enters basic information (i.e. age, weight, height) and we estimate the user's body size which is then used to make apparel size recommendations.

2. Basic Table Apparel Recommender: Users input different body measurements, and the app makes the best size recommendation based on that.

3. Custom Recommender: A programmable recommender where you can design the entire recommendation logic. Examples of products that are suitable for custom recommenders include bras.