Fit Preference Segmentation

💡Prerequisite knowledge

We recommend that you review the Good Practice Segmentations guide to understand how the segmentation can be used with Discovery Real-Time Segments.

Segmentation Description

This segmentation categorizes customers based on their clothing preferences, specifically how the clothes fit their body shape. It recognizes that size alone is not sufficient for finding the right fit, and that body shape plays a significant role in how clothes look and feel.

By understanding which type of cut or fit customers prefer, you can better cater to their individual needs and provide a more personalized shopping experience.

Segment Examples

  • Slim
  • Regular
  • Loose fit

Does this Segmentation fit my brand

This segmentation can be a good fit for brands that do not show figure/body type as a product category. It adds a layer of personalization for customers who prefer a particular fit.

Segmentation Evaluation

- Well-known universal fit concepts within fashion brands.
- Easy to add to custom tracking.
- May require custom tracking.
- May not be accurate for customers who buy clothes for more than one person.

Segments Criteria - How are Segments Defined

The event tracking details are summarized below.

  • It is required to track the event attribute on the view_item events. When tracking just the view_item events, the segmentation is defined as an aggregate of the most common view_item fit.

  • In addition to view_item events, you can track the attribute on cart_update and purchase_item events as well. This is not required in the product/variant catalogs.

Custom Tracking Requirements

This segmentation requires tracking of the custom event attribute “Item_fit.” Send this attribute as a string with the descriptive names of the clothing fits like baggy, slim, and so on.


We recommend that each segment has a size of a minimum of 100,000 daily visitors. This ensures that segments are large enough for Bloomreach Discovery to learn and for the AB tests to get statistically significant results.