Implementation of Machine Learning in Business
Guide to Recommendation Systems
Recommender systems as the most valuable application of machine learning

What’s inside:

How to measure performance and business value of recommendation system

A closer look how do recommendation systems works


Four-Stage Recommender System example

Develop your recommendation system with QuickStart ML Blueprints

Steven Jobs said: "People don't know what they want until you show it to them."
Discover the power of Recommendation Systems with this White Paper
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What industries are most likely to use
recommendation systems?
  • E-commerce
    Flagship example when it comes to achieving profits from the use of recommendation systems. Suggesting relevant products to end-users at multiple touchpoints sets online stores apart from their competitors and brings more sales
  • Banking
    Banks can try to better meet customers' expectations by offering personalized services, reduce the complexity of their choices, increase customer loyalty and ensure customer retention, and finally increase the frequency and also the overall value of the products they sell
  • Telecom
    Companies possess huge amounts of information. Allowing the customer to more easily discern the services offered and access more personalized offers can significantly reduce the cost of marketing campaigns, as well as ensure a steadily growing customer base
  • Streaming services
    This is an area that relies almost entirely on recommendations
“
80% of what users watch on Netflix is influenced by their movie recommendations, and that the recommendation system saves the company around $1 billion each year
Directly increase sales
What recommendation system can bring to your business?
Dive into and find out why recommendation systems give you a better understanding of our business.
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Download White Paper: Guide to Recommendation systems
Expand customer base
Reduce churn
Increase customer engagement
Reduce manual work
“
35% of Amazon's revenue is provided by its own recommendation system
QuickStart ML is a set of complete blueprints for solving typical machine learning problems. It leverages best-in-class open source technologies and materializes best practices for structuring and developing machine learning solutions.
How QuickStart ML Blueprints can help data scientists and engineers with building recommendation models?
Best Practices + Technologies = Working ML Blueprints
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Who will benefit from this White Paper?

Whether you are a business owner looking to implement a recommendation system, a data scientist exploring the latest advancements in the field, or simply curious about how recommendation systems work, this white paper is a comprehensive guide to understanding the complex and fascinating world of recommendation systems.
Why do you need this White Paper?

In the White Paper you can find what business value recommendation systems bring to the worldwide companies such as Netflix, Amazon, Spotify or Airbnb. What industries and how they use recommendation systems, why we need them and what exactly recommendation systems are?
Do any of the above points describe your situation?
Grab White Paper and get it DONE.
The administrator of your personal data is GetInData Poland Sp. z o.o. with its registered seat in Warsaw (02-508), 39/20 Pulawska St.
Your data is processed for the purpose of provision of electronic services in accordance with the Terms & Conditions.
For more information on personal data processing and your rights please see Privacy Policy.
By submitting this form, you agree to our Terms & Conditions and Privacy Policy.
GetInData | Part of Xebia is a leading polish expert company delivering cutting-edge Big Data, Cloud, Analytics, and ML/AI solutions. The company was founded in 2014 by data engineers and today brings together 120 big data specialists. We work with international clients from many industries, e.g. media, e-commerce, retail, fintech, banking, and telco. Our clients are both fast-growing scaleups and large corporations that are leaders in their industries. We maintain laser focus on data technologies, cultivate very strong engineering culture and support extensive knowledge sharing both within a company and outside through meetups, conferences and contributions to open-source. We are a go-to partner for companies that need tailored and highly scalable data processing and analytics platforms that give competitive advantage and unlock full business potential of their data.





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