Increase sales with recommender systems

Understand what your customers want, make relevant product recommendations in real-time and increase customer satisfaction and revenue.

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Recommender Systems on Lentiq EdgeLake

Why recommender systems are important

Recommender systems deliver the best results when they help customers make decisions quickly by feeding them buying options that are relevant, interesting and useful. Implementing a recommender system can help businesses improve their performance metrics significantly:

Up to 37% revenue increase
Up to 23.7% business growth
Up to 75% product usage increase

Recommender systems with Lentiq

With Lentiq, each company, store, regional office can define custom ML models, can use best practices developed organization-wide, tweak models and improve them in real-time and deliver real, scalable, reliable recommendation results for both businesses and their customers

Recommender systems can improve your business by:

  • personalize their customers' buying experience
  • implementing cross-selling strategies based on product recommendation
  • increase revenue by making relevant recommendation and promoting high-margin products
  • optimize stock inventory identify trends

Simplify implementation with Lentiq

Our platform provides as a service infrastructure, resources, application, data and metadata, notebooks management and helps data teams be independent and focus on data analysis and insight extraction. EdgeLake empowers users to:

  • abstract the computing and storage layer and run in multiple clouds simultaneously
  • deploy use case specific application stacks
  • easily develop and scale machine learning models
  • productify machine learning models through the "reusable code block" technology
  • share data across multiple data pools
  • localize machine learning models and improve prediction results
  • build an internal knowledge repository out of models deployed in multiple regions

How to start?

Collaborative filtering
Recommend based on similar users or products
Content filtering
Recommend based on item profiles
Rules-based filtering
Recommend using rules that match a user's profile


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