Customer churn occurs when customers or subscribers stop doing business with a company or service. Employee churn is when a member of a business process leaves that process. Sometimes churn is excessive and influences major business decisions. The traditional solution for such occurrences is to predict high-propensity churners.
Identifying early who is at risk to leave and knowing what leading indicators are the best way to re-engage these users or employees effectively can save time and money for companies. Data science and machine learning allows companies to predict churn and can show what are the leading predictors of churn.
This event is designed to educate professionals with the fundamentals of data science and machine learning that concludes with a hands-on lab using Watson Data Platform to demonstrate the capabilities of SPSS Modeler and Watson Machine Learning to predict churn.
The three major sections will cover:
Organizing, sorting, and standardizing data format for further analysis
Thorough understanding machine learning models like supervised and unsupervised learning
Basic knowledge sharing of types of algorithms like regression, classification, and time series
Note:Attendees will need to bring their own laptops and will need an IBM Cloud account. If they do not currently have an IBM Cloud account, they will be able to register for a free IBM Cloud Lite account ahead of time at https://ibm.com/cloud or we can assist during the event.
Darrel is a Technical Evangelist with a background in business intelligence, data integration and data science. His experience also includes server administration, networking, programming, and engineering. He believes in unlocking the power of data and by utilizing systems and services, anything is possible. He strives to make individuals he interacts with the best they can be by educating, motivating, and teaming up to push the boundaries of what is possible with IBM. You may find him on Twitter, LinkedIn, or at various meetups.