OnVoard Product Recommender

Develop by Shopify

$14.9

/ Month

14 Days Free Trial

EU hosted. Set & Forget Product Recommendations with Upsell.

Revenue tracking

Analytics

Filtering

Style editor

Meta labels

About OnVoard Product Recommender

Launched Mar 08, 2021

Built by Shopify

Address

7391 Pacific Circle, Mississauga, ON, L5T 2A4, CA

Website Support Portal Privacy policy Tutorial FAQ Document

Related Categories

Claim Offer

Synctrack Order Tracking Page

(57 Reviews)

Claim Offer

Sponsored

All-in-1 Order Tracker with a Branded Order Tracking Page

Description

EU Hosted. Next Generation Product Recommendations with Upsell.

Why choose OnVoard Product Recommender? Our app don't just display product recommendations. We make product recommendations more compelling by adding social proofs like "25% of customers bought this together" to convince customers WHY they should add product to cart. This is our app differentiation VS other product recommendations app. For GDPR, data are hosted in EU.

Style Editor: Customize product recommendations based on your store design
Meta Labels: Use social proofs to enhance product recommendations
Filtering: Use filters to exclude products based on rating, quantity, tags etc.
Revenue Tracking: Identify assisted revenue generated recommendations
Analytics: View analytics to identify top-performing recommendations

Pros

EU hosted – Ensures compliance with GDPR for businesses operating within the EU
Compelling product recommendations – Adds social proofs like '25% of customers bought this together' to persuade customers
Customization – The Style Editor allows customization of product recommendations to match store design
Advanced filtering – Allows exclusion of products based on rating, quantity, tags, etc
Revenue tracking – Ability to identify revenue generated by product recommendations
Analytics – Provides insights into the top-performing recommendations

Cons

Limited to EU hosting – May not be ideal for businesses outside the EU
Possible complexity – The advanced features may require a learning curve for new users
Dependence on social proof – The effectiveness of recommendations might be highly reliant on the availability of customer data