Articles on: Recommendation rules

Learn About Different Product Recommendation Types

Plans: All Plans Platforms: Shopify

Overview



AfterShip Personalization allows you to choose from 11 product recommendation types to improve your sales figures. Check this guide to learn about all of them.

AlgorithmDescriptionStore Data UsedTime frame for data accumulationUser Case
Manual selectionRecommends specific products that merchants want to upsell or cross-sell through manual selection.None
Best sellersRecommends products that have sold the most based on all historical orders in the store.Store orders data30 days
New arrivalsRecommends products that are new on the shelf.Products meta information
Frequently bought togetherRecommends products that are most likely purchased together based on store order history statistics. This works well with bundle upselling.Store orders data180 daysPotato chips and soft drinks
ComplementsComplements offer a subtle way to tie a whole look or function together with the main products, enhancing the customer shopping experience.Store user orders, products meta information180 daysPhone case for a phone; replacement bags for a vacuum
Similar productsRecommends products that are semantically or visually similar, without using any customer data.Products meta informationPink t-shirt and pink long sleeve t-shirt; sandals and slippers
SubstitutesSubstitute products are alternate items that can be used in place of a desired item. They have the same features as the main product but may differ in price, availability, or quality.Products meta informationSubstitute wine with grape juice; substitute a low-priced handbag with a more expensive one
Same product upsellRecommends the most expensive or cheapest products in the current cart or order, usually combined with a discount.User cart and orders data1 day
Free shipping upsellRecommends products that could help reach the free shipping threshold when purchased.User order and product price data30 days
TrendingRecommends products that are currently trending in the store based on user views or orders within a few days.Store clicked data30 days
NextLLMReal-time recommendations for the next best product series using a self-training multifaceted predictive model based on product tagging data and shopping behaviors from all AfterShip’s stores.Products meta information, store orders data, and user clicked data30 days

Updated on: 16/10/2024

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