Comparel benefits the entire clothing industry from the manufacturer to the customer. Below are some of the key benefits of Comparel.

Comparel allows customers to visually compare personal clothing with a merchant’s clothing and accurately measure any differences. Customers can see sizing relative to clothing of a known fit rather than relying on conventional sizing methods (S, M, L) and their variability.

Merchants can immediately see the reduction in return shipping costs, packaging waste and restocking and processing expenses.

Comparel allows customers to submit images of clothing with a known fit to personal stylists. Stylists can overlay the images with clothing selected for the customer and accurately measure differences to ensure proper fit before sending. Additionally, the stylist can extract dimensional data from customer imagery and eliminate any manual measurements required by the customer. Stylists immediately see a reduction in returns and increased customer satisfaction.

Comparel programmatically acquires measurements from an image of a customer's clothing - eliminating manual measurements and associated errors. Imagine a customer seeking a new suit. The customer may want the chest to fit like his favorite yellow t-shirt and the thigh width of his favorite khaki shorts. His clothing images are uploaded as a reference and a suit is received that matches or exceeds the fit of a professionally-measured suit.

The measurements are more exact and the customer will not need to travel to a tailor or perform manual measurements. Subsequently, returns and alterations are greatly reduced.

Comparel charts can be sent to the customer or printed at home. The customer only needs to photograph clothing items once. As customers purchase clothing photographed with a Comparel chart, those images can be used as references for future purchases.

Comparel can be implemented very quickly to an existing site. The implementation costs and material (charts) costs are extremely low.

Customers upload images of clothing they already own with a fit they like and want to recreate in their purchase. With machine learning, these images can be classified by clothing type and predetermined aspects, such as the depth of a V-Neck, can be automatically measured. You end up with the world’s richest data collection of exactly what fit customers seek. Clothing manufacturers could purchase this data to ensure they are meeting the sizing demands of the market - reducing surplus and waste. Merchants can also use the data to determine purchase quantities based on fit demands of their customers.

Machine learning allows for more advanced search functions. Users will be able to search by dimensions which are important to them regardless of the clothing size indicated.

Currently, a customer can search for a long-sleeve T-shirt in a particular size. With the application of Comparel, a customer can search for a long-sleeve T-shirt that has a chest width of 23” - 24.5” and a length of 31” - 32”. Or he can refine the results by any other parameters that are important to him.

As an added bonus, the search results can be standardized in appearance because they are all converted to the same scale and view.

Inconsistent sizing within a manufacturer’s product line or between manufacturers is a common problem. With Comparel, dimensional differences between items can be proven. Excessive shrinking or manufacturing defects can be submitted by customers and compared to original images. The manufacturer can use this data to increase their quality control procedures.

Comparel is not only good for your bottom line, it is also good for the environment. Reduced returns equate to less transit and packaging waste.

Comparel can be used to visually compare or measure objects that are flat or semi-flat in the same manner as clothing. Just a few examples include bedding, curtains, towels, mobile phone cases, plates and flatware. You can compare the cutting blade edge on a new mower blade or measure the handle length of a new wrench. The possibilities are nearly endless.

A color chart that can be integrated into a Comparel chart or added to a scene with Comparel is addressed in the patent. The color chart allows a customer to know the exact color of an item they see online regardless of their display settings. Here is an example:

Without the color chart:

Mary is shopping online for a teal shirt to match her new boots. She orders a shirt that appears teal only to receive it and find out it is navy blue. The discrepancy could have come from the camera settings or Mary’s display settings. Mary returns the shirt.

With the color chart:

Mary is shopping online for a teal shirt to match her new boots. The shirt she is interested in was photographed with Comparel and an integrated color chart. Mary sees the shirt most closely matches color #42 on the chart in the scene. Mary refers to the physical color chart she has at home and looks to see that color #42 appears navy blue. Mary doesn’t order the shirt.