Analysis of Call Center Arrival Data Using Singular Value
Decomposition

Haipeng Shen, Jianhua Z. Huang

abstract:

We consider the general problem of analyzing and
modelling call center arrival data. A method is described for
analyzing such data using singular value decomposition (SVD). We
illustrate that the outcome from the SVD can be used for data
visualization, detection of anomalies (outliers), and extraction
of significant features from noisy data. The SVD can also be
employed as a data reduction tool. Its application usually results
in a parsimonious representation of the original data without
losing much information. We describe how one can use the reduced
data for some further, more formal statistical analysis. For
example, a short-term forecasting model for call volumes is
developed, which is multiplicative with a time series component
that depends on day of the week. We report empirical results from
applying the proposed method to some real data collected at a call
center of a large-scale US financial organization. Some issues
about forecasting call volumes are also discussed.}

Key words and phrases: 

Anomaly detection, call center, data reduction, exploratory data analysis,
feature extraction, forecasting call volume.