Relek Build‑the‑Look

Develop by Shopify

Free plan available

Build the Look with Relek - Complete Style Recommendations

User profiles

Matching clothing items

Wishlist creation

One-click purchasing

Inventory alignment

Customisable layout

About Relek Build‑the‑Look

Launched Sep 19, 2023

Built by Shopify

Address

SOUTH SAN FRANCISCO, CA, US

Website Support Portal Privacy policy Tutorial FAQ Document

Related Categories

Unlock Deals 20% off

BON Loyalty Program & Rewards

(1,682 Reviews)

Unlock Deals 20% off

Sponsored

Loyalty rewards program with points, POS, referrals &...

Description

Modernise your store with our ‘Build the Look’ integration. Instantly drive Conversion and AOV+!

Relek is a fashion ‘Build-the-Look’ application to help you provide a more engaging experience for your customers. Simply match your inventory and we will do the rest! Your customers will be able to curate a complete look, via our recommended matching items, without the need for expensive infrastructure. Customers can ‘like and dislike’ items, build their profiles and receive targeted recommendations to ‘complete the look’. Seamless opportunity to modernise and drive efficiency!

Build the Look – Matching clothing items based on Style; Color; Inventory, etc.
User Profiles - Allow for 'Like/Dislike' and Wishlist Creation
Buy the Look – Multi-Item Build + ‘One Click’ purchasing funnel for users
Merchant Driven – Match garment categories to align with inventory
Integrated - Easy overlay on store and ability to customise page layout

Pros

Provides a more engaging experience for customers by allowing them to build complete looks
Offers targeted recommendations based on customer preferences and profiles
Does not require expensive infrastructure to implement
Enhances efficiency and modernizes the shopping experience
Facilitates multi-item purchasing with a ‘One Click’ buying funnel
Easy integration with existing store inventory and customizable page layout

Cons

Limited to existing inventory, may not cater to all fashion preferences if stock is limited
User interaction heavily relies on the accuracy of matching algorithms, which may not be perfect
Requires customers to actively engage with the platform to build profiles and interact with recommendations