Vue.ai’s Product recommendation engine tracks website shoppers' behaviour based on their user behaviour. We tracked their historical and live data and based on that we provide product recommendations to them. Every shopper should delight in getting an individualized shopper experience. personalized recommendations that convert 120% better.
Drive growth with personalized product recommendations. Make every customer feel special with 1:1 curated & personalized recommendations that convert 120% better.A personalized experience at every touchpoint. Delight every shopper with truly individualized shopper experiences and grow your revenue. Vue.ai’sProduct recommendation engine can provide personalized recommendations for every shopper across their journey on an eCommerce site.
Most users struggle to see the full value of a product within the first 14 days (if ever).
That's why we built UserGuiding, a no-code product adoption platform that helps increase activation & retention and reduce churn using many in-app walkthroughs and widgets as well as standalone Knowledge Base and Product Updates pages. Provide your users all the self-serve help they need throughout their journey, and also gather valuable insights and feedback from them with our in-app surveys to give you direction and improve your product development.
The best part? You can do it all without breaking the bank and with zero technical expertise, thanks to our drag-and-drop interface.
Try UserGuiding today to give your product adoption a huge, instant boost.
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Based on our record, product recommendation engine should be more popular than UserGuiding. It has been mentiond 4 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Use Case I: Getting the Ensemble That Compliments the Product The shopper can view the curated looks and ensembles on the Product Detail Page (PDP) of each item. If an item is not available, the Vue.ai engine proposes a similar item: Use Case II: Recommend Complimentary Items The product recommendation engine also suggests complementary items to shoppers, based on their price preferences: Use Case III:... - Source: dev.to / over 2 years ago
The journey of understanding customer intent begins with the first click. For instance, a black cocktail dress could, in some sense, convey that the buyer is looking for an item for a specific occasion, with a specific length, colour, pattern or style. Integrating Visually Similar product recommendations (product recommendation engine)on the product page instantly opens up all possible black dresses a shopper can... Source: over 2 years ago
Product recommendation engine — Here’s a scenario for you: You’ve found the perfect “match” on Tinder, and want to make an impression on your first date. You want to start off by looking for a shirt and find one that you really like. So you click on it to look at its product detail page. Source: over 2 years ago
When shoppers use styling recommendations (product recommendation engine), they aren’t just buying clothes. They are buying a fashion story. Instead of buying just a red dress, your shopper is now going to be buying the dress, a clutch, a blazer, accessories, and heels that will make her feel ready from head to toe for the party she was shopping for. The entire outfit is also curated in a way that not only does... Source: over 2 years ago
I do some work with https://userguiding.com/ and I find them to be a good compromise between features and pricing. It's one of the more affordable user onboarding platforms out there but comes in packed with functionalities, and it looks nice, too. Source: over 2 years ago
Use user guides to onboard customers flawlessly (https://userguiding.com/). Source: over 3 years ago
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