Recommendation Profiles

Bestseller Recommendations

Best Sellers and Trending Products, or simply "Top Lists," is a recommendation type that relies on comprehensive trend data and popularity metrics. It presents items that are currently deemed "hot and popular" in the store, reflecting the prevailing trends and customer preferences.
Purpose: Push main trending products

Bundled Recommendations

Dynamic Bundles serve as a method to curate sub-collections of items within Sift Lab that complement each other seamlessly. Common applications include presenting "Frequently Bought Together" or "Get the Look" inspirational content, which is then showcased through Onsite Product Recommendations.
Purpose: Display "shop the look", a pre-defined set of products that should be bought together

Cherry-Pick Recommendations

Manual merchandising involves consistently showcasing specific products based on their product IDs and the predetermined order of those IDs. It's essential to ensure that the selected products are currently in stock, as the Cherry-Pick method for manual merchandising aligns with the availability status of the items.
Hand-pick products you would like to showcase

Check-Out Recommendations

Shopping cart recommendation displays products based on all products in the shopping cart, use filters and rules to showcase the most relevant products. The recommendation can be used to display free shipping products, cheaper than the ones in the basket, cross-sell etc to improve AOV etc. Siftlab and Qliro offer this setup for common customers to enhance the customer experience.
Purpose: Increase AOV, reach free shipping, low threshold products to purchase.

Color & Size Affinity Recommendations

If the customer has a preferred size, color, etc, Sift Lab can alter the recommendations shown to follow the chosen preference of the customer. Ensure to show i.e Size M for customers who are a size M and show Red items for customers who prefer the color red.
Purpose: Align recommendations with the preferred choice of the customer
What is needed: Return the picked color/size to Sift Lab in the API call

Custom Recommendations

Use filters, rules, trends, and boosting of products to create your preferred recommendation logic, to achieve your precise targets.
Purpose: Achieve a custom-built/bespoke recommendation setup

Cross-sell Recommendations

Product recommendations associated with the product the customer is browsing, known as "Customers Who Bought This Also Bought." It incorporates cross-selling and up-selling functionalities with rules and filters. These rules allow for precise control over the type of products displayed in the recommendations, offering the ability to tailor whether the recommendations showcase alternatives, supplementary items, or other specified product categories.
Purpose: Recommend products from other categories than what you're currently browsing

Loyalty Points Recommendations

Recommend available products based on the loyalty points the customer has accumulated.
Purpose: Improve customer loyalty and retention

Order Related Recommendations

Free Shipping Recommendations

The Free Shipping Recommendation is based on the total cart value. It allows for tailored suggestions based on the total value of items in the shopping cart, providing an incentive for customers to qualify for free shipping.
Purpose: Improve AOV and make the customer reach free shipping limit
Requirement: Return basket value and shipping limit in the API call.

Inspiration Recommendations

Recommend products in categories that the customer has not purchased from before / selected product
Purpose: Inspire purchases in the most relevant categories that have not yet been purchased from / the product is not in

Landing Page Recommendations

Recommendation displays products based on the source of traffic, read from the UTM parameters. Define products that should be displayed based on the source of the customers to enhance their experience coming to the site.
Purpose: Tailor the recommendations based on the external source of the customer
Requirement: Return UTM-tag in the API call

On Sale Recommendations

Personalized Recommendations

The customer will be displayed with the products which whom is most likley to purchase. This is a default set-up in Sift Lab and optimized for revenue and transaciton.
Purpose: Go with a high-performing out of the box setup

Random Products Recommendations

This functionality displays a completely random set of products. It can be used for testing or the live environment.

Replenish Recommendations

You can exclude products to be shown during a set period of time to eliminate customers receiving unwanted products. Further, use the recommendation to decide what time a product can be re-introduced to a customer.
Purpose: Occasionally bought products can be optimized, i.e Toothpaste, Socks, Shampoo etc

Similar products Recommendations

Sustainable Recommendations

Recommend products based on their level of sustainability. Use existing sustainability metrics to promote such products, such as material, shipping alternatives, returns etc. Enlighten your customers of your sustainable product line.
Purpose: Promote your sustainable products to build awareness and loyalty

Thank you page Recommendations

Use the thank you page to introduce additional products tailored to the customer preferences. Use this to showcase supplementary products and/or limited offers to boost the sales.
Purpose: Improve the AOV of the order and show limited offers

Standard Recommendations

Win-back Recommendations

Recommend the most likely products a customer will purchase based on historical purchasing behavior
Purpose: Present the products that a churned customer is most likely to buy

Thanks for the last order Recommendations

On Sale Recommendations