Service Requests have many attributes in addition to category. These attributes include joins with other tables that describe the affected product, its version, the number of customers impacted and the likes of this place. When the service requests are filtered by one or more of these attributes, they tend to form requests that display scatter plots suitable for linear regression. It is very helpful to use these linear regressions to predict the next data point especially given that the data points are progressing along the timeline.
The prediction parameter need not restrict itself to relief time. It can be used for any parameter that affects the next service request. These include cost, duration, knowledge base, or any suitable attribute which can give an indication to the customer up front. The opening of a service request is an indication of a concern for the customer and any information provided to the customer that alleviates such concerns with the help of setting expectations or providing self-service hints will be tremendously appreciated. For example, if the customer knows an estimate for the relief time of the incident based on the linear regression of the past service requests of this nature, then the customer can wait to poll the resolver. If the duration for a specific step of the relief is indicated, then the customer may even willingly wait longer.
The number of incidents that may appear in certain filtered categories might be very small. A linear regression on fewer data set is prone to error. But the regression can be reevaluated when more data points accrue in this narrow category. This calls for an application to use the linear regression in an automatic manner. Such auto-regressors may even be run on every new datapoint.
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