Lesson 5: Building Neural Network and Logistic Regression Models (Intermediate Data Mining Tutorial)


Applies To: SQL Server 2016 Preview

The Operations department of Adventure Works is engaged in a project to improve customer satisfaction with their call center. They hired a vendor to manage the call center and to report metrics on call center effectiveness, and have asked you to analyze some preliminary data provided by the vendor. They want to know if there are any interesting findings. In particular, they would like to know if the data suggests any staffing problems with staffing or ways to improve customer satisfaction.

The data set is small and covers only a 30-day period in the operation of the call center. The data tracks the number of new and experienced operators in each shift, the number of incoming calls, the number of orders as well as issues that must be resolved, and the average time a customer waits for someone to respond to a call. The data also includes a service quality metric based on abandon rate, which is an indicator of customer frustration.

Because you do not have any prior expectations about what the data will show, you decide to use a neural network model to explore possible correlations. Neural network models are often used for exploration because they can analyze complex relationships between many inputs and outputs.

In this lesson, you will use the neural network algorithm to build a model that you and the Operations team can use to understand the trends in the data. As part of this lesson, you will try to answer the following questions:

  • What factors affect customer satisfaction?

  • What can the call center do to improve service quality?

Based on the results, you will then build a logistic regression model that you can use for predictions. The predictions will be used by the Operations team as an aid in planning call center operation.

This lesson contains the following topics:

Adding a Data Source View for Call Center Data (Intermediate Data Mining Tutorial)

Lesson 1: Creating the Intermediate Data Mining Solution (Intermediate Data Mining Tutorial)

Lesson 2: Building a Forecasting Scenario (Intermediate Data Mining Tutorial)

Lesson 3: Building a Market Basket Scenario (Intermediate Data Mining Tutorial)

Lesson 4: Building a Sequence Clustering Scenario (Intermediate Data Mining Tutorial)

Lesson 5: Neural Network and Logistic Regression Scenario (Intermediate Data Mining Tutorial)

Basic Data Mining Tutorial
Intermediate Data Mining Tutorial (Analysis Services - Data Mining)