VQ-AI learns your users' behaviour and recommends personalized offers to them, or vice versa.
If you’re not innovative in e-commerce or content, you may lag behind. Recommendation engine sets your company apart by taking your e-commerce efforts to the next level. Recommendation engines can take on many diverse forms. A few you may be familiar with:
Amazon's product recommendation
Products are recommended to user's according to user's interests and behaviours.
Linkedin's Connection Tool
“People You May Know” tool that recommends connections based on existing contacts.
Google's Search Engine
Results page (SERP) which recommends pages to view based on keywords and other usage data
Online Media Outlets
Constantly recommending new and relevant articles are a way to keep visitors on a website.
More User Engagement
Better User Retention
✔ Recommended products for users
You can use VQ-AI to display recommended items for your users. Displaying recommended items enhances your users' experience and provides you with a more user engagement and customer retention.
✔ Recommended users for products
If you have a new item or a group of items or a campaign, you can use VQ-AI to target users for those items. This leads you to present interesting content to some of your users while not bothering the rest of them.
✔ Find similar behaviours
Being able to find which users are similar to each other could be useful for your users significantly when they are able to find other like-minded users. Personal connections made in your platform would help you increase the user loyalty.
✔ Find similar products
You may promote similar items on item pages for your users. Since they are already visiting an item, they could be more likely to see other items that similar users also visit. You can also use the feature to predict tags or categories for your items.
✔ Analytics Dashboard
Nearly real-time visualization of the tracking data. You get nearly real-time insights into the tracking data that is collected on your platform.
VQ-AI’s application architecture wraps high-performance machine learning and prediction into an innovative, fault-tolerant modern technology stack. The application works in a public cloud using containers for secure and private front-end, computation and internal storage. The enterprise edition is extensible to virtual private clouds and parallelized compute infrastructures.
We offer you a competitive and robust technology that learns your customers' behaviour and recommends personalized products to them, or vice versa.
Integrate and track
User Actions are sent to the VQ-AI using the REST API. The user actions are stored in the database.
The provided Analyzers periodically analyze all recorded data for identifying patterns to generate recommendations.
Display Recommended Content
These Recommendations can be accessed through calls to the easyrec Web Service API and presented to a user.