Constantly evolving algorithms
Qubit’s machine learning algorithms are trained on actual visitor data and are constantly being re-trained and updated to deliver value.
Qubit Aura uses machine learning to create a completely individualized experience for every user, learning and growing with every customer interaction.
Category preference prediction
Qubit Aura goes beyond product recommendations with category preference prediction showing users the categories of products they are most likely to prefer.
Aura inspires visitor confidence with clear evidence selection information. Products are given clear context so visitors understand why they are seeing them.
Recommendations across the customer journey
Qubit’s product recommendation machine learning models are based on core business strategies, trained on vast data cohorts, and then layered with segmentation rules throughout the customer journey.
Wisdom of the crowd recommendations
Use Qubit’s product recommendations to show trends and product popularity and showcase the very best of your brand.
Recommendation algorithms for business goals
Several Qubit product recommendation algorithms are designed to reach business goals, like conversion or upsell—going beyond what’s viewed together to bring out interrelationships between different SKUs.
Broad data collection
The Qubit platform is designed to uncover insight from across hundreds of different data points. Thanks to the low-latency in our data pipeline, we can run our machine learning algorithms on that data for faster iterations that deliver consistent improvements.
Structured data for speedy comprehension
Qubit’s data layer collects structured data allowing it to operate across all datasets: every Qubit customer can take advantage of machine learning powered by billions of user journeys’ worth of data.
Our ML engine automatically finds and exposes valuable revenue opportunities for your attention - automating segment discovery and helping you focus your web personalization efforts.