Product Recommender

Powering ecommerce marketing for brands you love

Meta Labels

The number one differentiation is that our product recommendations comes 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. 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

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 product recommendation logics for all product recommendations use cases. Each personalized recommendations can be easily set up in a plug-and-play manner.

List of Product Recommendation Types

Below are available product recommendation types you can use

Frequently Bought Together

Use frequently bought together to upsell commonly bought together products. Implemented with association rules.

Also Bought

Also known as "Customers Who Bought This Also Bought". Use this to cross-sell other products bought by customers. This algorithm looks at purchase history of customers who've purchased a given product. Then recommend other products based on their purchase history. Results will be similar to "Frequently Bought Together" if most of your customers are one-time purchasers.

Best Selling

Recommend best selling products by revenue. Use lookback days to get best selling products within a time period. Or filter by tags to get best selling products from a category.

Handpicked Items

Manually select products for recommendations. You can use this for various use cases like "Featured Items", "Recommended for Christmas", "Editors Favorites", "Staff Picks Under $20".

Similar Items

Recommend similar products based on textual attributes like title, description, keywords. The pros of this algorithm is that it does not require any past purchase data (aka cold start problem). Use this as fallback for newly added products that doesn't have sufficient order history to generate recommendations with other logics. Implemented with tf–idf.

Selling Fast

Recommend products that are selling fast based on the number of units sold. Unlike trending, selling fast allows you to display the number of product units sold during period. Use trending if you don't wish to display this stat publicly.

Selling Out Soon

Recommend products that are low in stock. Display selling out soon to induce FOMO and get users to make quick purchase.

New Arrivals

Recommend newly arrived products. Place new arrivals in your home page to drive that initial boost for new products.

Discounted Items

Recommend discounted items. Use discounted items to capture user attention and increase the time they spent at your store.

Top Rated

Recommend top rated products. Add filter to exclude products with low number of reviews to make recommendations more compelling. The algorithm used is bayesian average, similar to the one used by imdb.com where a 4.8 rated product with 100 reviews would be ranked higher than a 5.0 rated product with 1 review.

Recently Sold

Recommend recently sold products. Use recently sold products as an alternative for trending products.

Recently Reviewed

Recommend recently reviewed products. Use recently reviewed products as an alternative for trending products.

Other Key Features

Style Editor

Customize product recommendations widget according to your store design.

Widget Placement Tool

Our built-in placement tool allows you to install recommendations widget with zero 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.