In this task, you add a data source view that will be used to access the call center data. The same data will be used to build both the initial neural network model for exploration, and the logistic regression model that you will use to make recommendations.
You will also use the Data Source View Designer to add a column for the day of the week. That is because, although the source data tracks call center data by dates, your experience tells you that there are recurring patterns both in terms of call volume and service quality, depending on whether the day is a weekend or a weekday.
To add a data source view
In Solution Explorer, right-click Data Source Views, and select New Data Source View.
The Data Source View Wizard opens.
On the Welcome to the Data Source View Wizard page, click Next.
On the Select a Data Source page, under Relational data sources, select the Adventure Works DW Multidimensional 2012 data source. If you do not have this data source, see Basic Data Mining Tutorial. Click Next.
On the Select Tables and Views page, select the following table and then click the right arrow to add it to the data source view:
On the Completing the Wizard page, by default the data source view is named Adventure Works DW Multidimensional 2012 . Change the name to CallCenter, and then click Finish.
Data Source View Designer opens to display the CallCenter data source view.
Right-click inside the Data Source View pane, and select Add/Remove Tables. Select the table, DimDate and click OK.
A relationship should be automatically added between the DateKey columns in each table. You will use this relationship to get the column, EnglishDayNameOfWeek, from the DimDate table and use it in your model.
In the Data Source View designer, right-click the table, FactCallCenter, and select New Named Calculation.
In the Create Named Calculation dialog box, type the following values:
Get day of week from DimDate table
(SELECT EnglishDayNameOfWeek AS DayOfWeek FROM DimDate where FactCallCenter.DateKey = DimDate.DateKey)
To verify that the expression creates the data you need, right-click the table FactCallCenter, and then select Explore Data.
Take a minute to review the data that is available, so that you can understand how it is used in data mining:
An arbitrary key created when the data was imported to the data warehouse.
This column identifies unique records and should be used as the case key for the data mining model.
The date of the call center operation, expressed as an integer. Integer date keys are often used in data warehouses, but you might want to obtain the date in date/time format if you were going to group by date values.
Note that dates are not unique because the vendor provides a separate report for each shift in each day of operation.
Indicates whether the day was a weekday, a weekend, or a holiday.
It is possible that there is a difference in quality of customer service on weekends vs. weekdays so you will use this column as an input.
Indicates the shift for which calls are recorded. This call center divides the working day into four shifts: AM, PM1, PM2, and Midnight.
It is possible that the shift influences the quality of customer service so you will use this as an input.
Indicates the number of Level 1 operators on duty.
Call center employees start at Level 1 so these employees are less experienced.
Indicates the number of Level 2 operators on duty.
An employee must log a certain number of service hours to qualify as a Level 2 operator.
The total number of operators present during the shift.
Number of calls received during the shift.
The number of calls that were handled entirely by automated call processing (Interactive Voice Response, or IVR).
The number of orders that resulted from calls.
The number of issues requiring follow-up that were generated by calls.
The average time required to respond to an incoming call.
A metric that indicates the general quality of service, measured as the abandon rate for the entire shift. The higher the abandon rate, the more likely it is that customers are dissatisfied and that potential orders are being lost.
Note that the data includes four different columns that are based on a single date column: WageType, DayOfWeek, Shift, and DateKey. Ordinarily in data mining it is not a good idea to use multiple columns that are derived from the same data, as the values correlate with each other too strongly and can obscure other patterns.
However, we will not use DateKey in the model because it contains too many unique values. There is no direct relationship between Shift and DayOfWeek, and WageType and DayOfWeek are only partly related. If you were worried about collinearity, you could create the structure using all of the available columns, and then ignore different columns in each model and test the effect.