Research an example of where a real business or organization has used generalized linear modeling

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Research an example of where a real business or organization has used generalized linear modeling to predict a specific outcome. This may be on any topic or in any field or discipline that is interesting to you. In your initial response, provide for the class a summary of each of the five steps of risk management planning, as they relate to your chosen example. Ensure that you clearly delineate sections for Identification, Understanding, Data Preparation, Data Modeling and Application. Your summaries for how the organization in the example you have chosen must be substantive and meaningful.

Describe how the organization identified the risk(s) they have addressed through GLM;
Discuss what the organization did to understand the risk(s);
Outline, to the extent possible, how the organization gathered and prepared their data;
Explain how the organization used GLM to build a model on their data; and then;
Review how the organization applied their model to respond to the risk(s)

 

Sample Solution

Example: Using Generalized Linear Modeling to Predict Customer Churn

Risk Identification:

Customer churn is the loss of customers over time. It is a major risk for businesses, as it can lead to lost revenue and profits.

Risk Understanding:

There are many factors that can contribute to customer churn, including:

  • Product dissatisfaction
  • Price dissatisfaction
  • Poor customer service
  • Competition
  • Changes in customer needs or preferences

Data Preparation:

To use GLM to predict customer churn, businesses need to gather data on their customers and their churn behavior. This data can include:

  • Customer demographics (e.g., age, gender, income, location)
  • Customer usage data (e.g., frequency of use, products or services used)
  • Customer support interactions (e.g., number of support tickets opened, customer satisfaction surveys)
  • Customer churn data (e.g., whether or not the customer has churned)

Once the data has been gathered, it needs to be cleaned and prepared for analysis. This may involve removing outliers, filling in missing values, and transforming the data into a format that is compatible with GLM software.

Data Modeling:

To build a GLM model to predict customer churn, businesses need to select the appropriate dependent and independent variables. The dependent variable is the variable that the business is trying to predict (i.e., customer churn). The independent variables are the variables that are thought to influence the dependent variable.

In the case of customer churn, some common independent variables include:

  • Customer demographics
  • Customer usage data
  • Customer support interactions
  • Past churn behavior

Once the independent and dependent variables have been selected, the business needs to choose a GLM model that is appropriate for the type of data they have. There are many different types of GLM models, including logistic regression, Poisson regression, and negative binomial regression.

The business then needs to fit the GLM model to their data. This involves using the software to estimate the parameters of the model. Once the model has been fit, the business can evaluate its performance on a holdout dataset.

Model Application:

Once the GLM model has been evaluated and found to be satisfactory, it can be applied to new data to predict customer churn. For example, the business could use the model to identify customers who are at high risk of churning and target them with interventions to prevent them from churning.

Example:

One example of a business that has used GLM to predict customer churn is Netflix. Netflix uses GLM to identify customers who are likely to cancel their subscriptions. Netflix then targets these customers with personalized offers, such as recommendations for new movies and TV shows, to encourage them to stay subscribed.

Conclusion:

GLM is a powerful tool that can be used to predict customer churn. By understanding the risks of customer churn and using GLM to build and apply predictive models, businesses can reduce churn and improve their bottom line.

 

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