Software for data analysis and accurate forecasts.

"The quality of our forecasts is growing all the time and the sales volumes we are predicting are becoming more and more accurate."
Michael Sinn, Head of Purchasing Support at Otto

Your turnover will increase and your costs will go down.

Services | Advantages

Use data analysis to understand
and plan for the future 
reliably

The goal of performing a data analysis is to identify patterns within data and uncover any interconnections that exist within unstructured data resulting from a variety of different sources. That is exactly what our NeuroBayes predictive analytics software achieves when used to perform data analyses, providing companies with well-founded insights for the future. 


Using our NeuroBayes predictive analytics software to perform your data analysis helps you to understand the future and align your business strategy accordingly.

The NeuroBayes data analysis is based on algorithms and neuronal networks. These can be used in countless industries and specialist areas. Compared to conventional methods, the quality of forecasts produced using this form of data analysis is significantly higher. 

 

Using predictive analytics software for your data analysis yields superb results. You can create forecasts for any question imaginable that needs a predicted answer.


Possible uses or outcomes of a data analysis are:

  •  Sales forecasts
  •  Risk forecasts
  • Product recommendations
  • Order-size optimization
  • Procurement proposals
  • Preventing contract terminations
  • Social media activity
     

“Big data and predictive analytics open up a wealth of new possibilities for analyzing data. Our software enables companies to answer precisely those questions that they are faced with on a daily basis.”

Professor Dr. Michael Feindt, NeuroBayes founder 

Bringing everything in line: Analyzing data using NeuroBayes

  • Using NeuroBayes to analyze data improves forecast quality by at least 20 percent
  • Data analysis using data from many different internal and external sources: operational systems, sensors in physical world (mobile radio units, vehicles, cameras…), external databases (weather, economic indicators...)
  • Data analysis based on algorithms developed at the cutting-edge of global research
  • Data analysis based on a self-learning system that requires low maintenance effort
  • Data analysis in real-time
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