Categorizing preferences
Categorizing preferences of customers or users as they forge relationships (content-based recommendations)
Recommendation systems are an important component for digitized companies, particularly in the e-commerce sector. We implement data science to provide companies with the foundation for more turnover and improved customer retention.
Why Basefarm?
In developing customized recommender solutions for our clients based on state-of-the-art machine learning processes, we enable them to gain a competitive advantage. We believe there is no such thing as a ready-made solution. We work together with our clients to find the right solution for them, whether that is B2C or B2B. We do not limit ourselves to only the traditional website use cases; we also develop recommenders for internal processes, expert support or process optimization.
Our Big Data Team possesses extensive sector-spanning experience in the field of data science and machine learning. With a mix of mathematicians, psychologists, physicists and computer scientists, the team is able to examine the various facets of a data challenge from a wide range of angles. We stay on the cutting edge of research and do not shy away from technical innovations.
From idea to cable
The initial phase is followed by specific development steps and cycles that pave the way to actual applications and solutions – the Data Solution itself.
This constructive phase is characterized by practicerelevance and usability. Using conventional consultancy approaches and methods, the result might be, for example, confirmation that a data case is feasible and an indication of how it could work.
However, the whole process grinds to a halt after the prototyping or proof of concept stage.
In contrast, Data Thinking goes much further. Rather than setting prototyping as the objective, it focuses on product maturity from the outset; use and application are considered as the next step in the process chain throughout.
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Customer Expectations
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Communities & Startups
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Trends & Disruptions
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Social & Technological Change
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Business Challenges
Big data Blog
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