Product Recommendations as a Revenue Engine
Majority of ecommerce visitors go to an online store without knowing what they want to buy beforehand. As a merchant, you can add product recommendations to suggest, guide, and convert these visitors into customers.
For users that are already buying, product recommendations can help boost average order value (AOV) with cross-sell recommendations like "Frequently Bought Together" and "Customers Who Bought This Also Bought".
Why choose OnVoard Product Recommender
1) Meta Labels
The number one differentiation is that our product recommendations come with an option to display meta labels. Meta labels like "25% of customers bought this together" serves as social proofs to convince customers WHY they should add the product to cart. Meta labels can be customized according to each recommendation logic and make product recommendations more compelling.
Below are some examples:
- Frequently Bought Together: 25% of customers bought this together
- Also Bought: 7.6X more likely to buy
- Selling Out Soon: 3 units left
- New Arrivals: Added 5 hours ago
- Recently Sold: Last Sold 3 hours ago
2) 14 Types of Product Recommendations
Gain access to our rich library of product recommendations. With a single subscription, you gain instant access to over 14 recommendation logics for all product recommendations use cases. Each recommendation logic can be easily set up in a plug-and-play manner.
Below are types of product recommendations that we provide.
- Frequently Bought Together
- Also Bought
- Similar Items
- Recently Viewed
- Handpicked Items
- Best Selling
- Trending
- Selling Fast
- Selling Out Soon
- New Arrivals
- Discounted Items
- Top Rated
- Recently Sold
- Recently Reviewed
Other Key Features
- Style Editor: Customize product recommendations based on your store design.
- Auto Placement: Automatically install product recommendations widget to your site without code modifications.
- Filtering: Use filters to exclude products based on rating, num reviews, inventory quantity, tags, etc.
- Revenue Tracking: Identify assisted revenue generated from product recommendations.
- Analytics: View analytics for each recommender to identify top-performing product recommendations.
