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WooCommerce Product Recommendations

WooCommerce Product Recommendations 3.0.6

Developer
WooCommerce

Upsells and cross-sells with a pinch of intelligence​

Ever noticed how the most successful stores leverage the power of product recommendations? When done right, upsells and cross-sells deliver a more engaging experience to your visitors — the kind of experience that not only helps them find products they like, but also keeps them coming back.

Fortunately, you don’t need a team of machine learning specialists to get started. Product Recommendations is a state-of-the-art product recommendations platform for WooCommerce that brings together human intelligence and machine learning, with one goal: To help you grow your sales. Here’s how:
  1. Offer more intelligent upsells and cross-sells — or create your own, rule-based recommendation engines.
  2. Improve the shopping experience across your entire store, with relevant recommendations placed at strategic locations.
  3. Measure their impact and optimize your strategies with in-depth analytics.

Let technology do the heavy lifting for you​

Build intelligent engines that generate automated, accurate Frequently Bought Together recommendations, with near-zero waiting or training. Our lightweight search algorithm analyzes your orders to discover meaningful product relationships, and quickly adapts itself to new trends and seasonal patterns.

Deliver captivating, Amazon-style recommendations, without giving up your store's data or revenue to anyone
Deliver captivating, Amazon-style product recommendations, without giving away your store’s data (or revenue) to third-parties.

Add recommendations across your entire catalog, effortlessly​

Ever tried to manually add upsells and cross-sells to every product or category page of your store? With Product Recommendations, you can do both in minutes. Create upsells and cross-sells in bulk, instead of entering products one by one. Add category, attribute, tag, or price filters to narrow down products, and use amplifiers to boost specific results based on popularity, rating, creation date, conversion rate, or more advanced criteria.

Looking for a way to recommend products from the currently viewed category, or brand? Want to limit results to products that cost more than the currently viewed product? No problem! Product Recommendations lets you deploy context-aware recommendations, faster.

Create upsells, cross sells and related products in bulk, by category or tag.
Use smart filters, amplifiers and visibility conditions to create recommendation recipes for multiple products and product category pages at once.

Promote the right product, to the right customer, at the right time​

Increase your store’s average order value by recommending recently viewed products and products from recently viewed categories on the checkout page, or even after customers complete their orders. Make relevant, well-timed offers by displaying them conditionally, based on your customers’ cart/order contents, browsing history, date, or location.

Recommend products from recently viewed categories.
Offer personalized upsells before or after checkout. Recommend products from categories that customers viewed, and display your recommendations conditionally, based on order or cart contents.

Recommend products anywhere​

Deploy recommendations at more than 20 locations on your store’s:
  • Main shop page.
  • Product category or tag pages.
  • Individual product pages.
  • Cart and checkout pages.
  • Thank You (order-received) and order-pay pages.
3.0.6Version
125 Downloads
802 Views
0.00 star(s) 0 ratings
Last update

More resources from raz0r

Latest updates

  1. Woo Product Recommendations

    2024-02-07 - version 3.0.6 * Fix - Ensure that the engine type filter doesn't appear on other...
  2. WooCommerce Product Recommendations v3.0.2

    2023-10-17 - version 3.0.2 * Fix - Removed the deprecation warning for the...
  3. WooCommerce Product Recommendations v3.0.1

    2023-09-27 - version 3.0.1 * Fix - Resolved a fatal error that was triggered when viewing...

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