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Contact center productivity improvement based on predictive analytics

EasyChair Preprint no. 2097

6 pagesDate: December 5, 2019


The overall performance of a contact center is often measured through its ASA (Average Speed of Answer), abandonment rate and Service Level (SL). This study focuses on the predicting ASA as a function of call volumes, AHT, occupancy of agents, number of productive Full Time Equivalents (FTE) and Off Phone Activities % (OPA) and analyzing the impact of each of these parameters on ASA through sensitivity analysis. An ensembled model for ASA prediction was created using multi-variate time series prediction algorithms like ARIMAX and neural network predictor based on 3 years of data from a contact center in US

Keyphrases: Average Speed of Answer, capacity planning, Contact Center, time series analysis

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Rishabh Tyagi and Sandeep Bhattacharya},
  title = {Contact center productivity improvement based on predictive analytics},
  howpublished = {EasyChair Preprint no. 2097},

  year = {EasyChair, 2019}}
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