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.
Tools are created to serve our own purposes and technology needs to add value to our lives without creating friction.People should not adapt to technology. Technology needs to adapt to people. We don't need to teach people how to interact with software but train software to interact with people. Software adoption relies on people learning how to navigate through a user interface. But this causes resistance and hinders productivity. We close the knowledge gap between humans and machines by allowing anybody to operate any software instantly. For Software providers that need to sell their product the ability to guide users in real time translates into higher engagement, activation, conversion, and retention. Companies that implement on-screen interactive guidance in the applications their staff needs to work with, solve all the logistic problems connected to staff training and see an increase in productivity that derives from a workforce which is fully operative in any software application from the get-go.
Based on our record, product recommendation engine seems to be more popular. 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
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