Examples of using Data mining methods in English and their translations into German
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Various data mining methods are developed.
Possible issues in your company that may be suitable for Data Mining methods.
Data mining methods, predictive analytics and artificial intelligence are a new standard.
We then started to develop first solutions for retailers based on data mining methods.
Businesses today use data mining methods to detect sales trends and to predict outcomes.
Lucas Pedretti: Predictive Sales Analytics uses data mining methods to make predictions.
With Data Mining methods, we can find solutions to tasks where traditional methods meet their limits.
Here we discuss the importance and present some ideas and data mining methods to analyse leads.
Various analyse modules and data mining methods are basis of the mockups for the project ICT.
Data mining methods for causal modelling can be viewed as"counterfactual" analysis: they attempt to explain what would be the difference between opposing sales actions, were to happen and were not to happen.
We also analyze your data with advanced data mining methods for gaining new information.
Bosch applies data mining methods in order to identify gaps next to the curb unequivocally as parking spaces.
In two days, we will present you the most important data mining methods for Predictive Sales Analytics.
More complex data mining methods can be employed to identify statistical patterns or similarities between places or datasets.
This is obtained through the analysis of monitoring data with data mining methods and their aggregation.
Predictive analytics uses data mining methods, machine learning, and artificial intelligence to make predictions concerning customers, sales leads, and products.
Predictive analytics, a crucial challenge for mid-sized companies,works with a collection of data mining methods used to describe and predict the likelihood of future outcomes.
This enhancement envolves the use of text and data mining methods to automatically add semantic annotations to text resources and make them searchable.
Media and publishing house: Implementation and application of Data Mining methods in forecasting customer behaviour; concentration on customer churn.
In two days, we will present you the most important data mining methods for Predictive Sales Analytics and how these can be applied for Sales Coaching.
Predictive sales analytics uses a collection of data mining methods to describe and predict the likelihood of future outcomes.
The participant stands to discover how the application of data mining methods can assist in bringing into light new knowledge and insights from the existing data. .
Data Mining Method: Regression or"value estimation.
Data Mining Method: Classification or class probability estimation.
A regression data mining method attempts to predict or estimate a numerical value for some given variable, in this example the total revenue per customer.
Predicting customer churn is the second most profitable data mining method in B2B.
This data mining method combined with machine learning offers exceptional good results for predictive sales analytics.
In this example, the data mining method will create two classes,"will buy" and"will not buy.
Using this data mining method across your CRM or ERP data, you can now determine which attributes are more informative.
This data mining method provides valuable information in predicting which sales opportunity a sales manager should push or where she should prioritize.