Call centres and service centres are an essential resource of today’s businesses, from emergency response to financial services. With more than 70% of all customer and business interactions handled through call centres, they often represent the primary channel to provide information and assistance to customers.
Across industries and government, more than 3.5 million were employed in US call centres − more than 2.6% of the US workforce in 2006, making it an important and continuously growing sector of society. With human resources as its major cost driver, a major challenge to call centres is that of forecasting call centre demand of inbound calls, call durations and call types in order to schedule agents efficiently across different services within a week and during the day (operational planning), or to determine the quantity and timing of hiring and training (tactical planning), in order to balance service quality, costs and efficiency.
Although advances in information and communication technology (ICT) have helped identify the true historic call volumes, which are often masked by redials and reconnects, the challenge of forecasting inbound calls has increased with the increasingly competitive business environments. As a result, forecasting call centre demand in half-hours or even 15-minute intervals is often particularly challenging, as it is driven by multiple overlapping intraday, weekly and yearly seasonalities, combined with multiple exogenous drivers such as advertising, bills & letters sent out, and different bank holidays with lead and lag effects, and different responses by call types and across socio-demographics.
The Lancaster Centre for Forecasting has significant experience in meeting the challenges of forecasting call centre data which current commerical software do not address. We deliver tailored solutions employing the latest statistical methods, from triple Exponential Smoothing to ARIMAX and Regresison models to state-of-the-art nonlinear methods of Artificial Intelligence such as Neural Networks or Support Vector Regression.
We help companies in dealing with complex seasonal patterns inherent in most call centre demand data. These include the presence of both a within-day seasonal cycle visible from the demand profile of one day to the next, and a within-week seasonal cycle which appears when one compares demand on corresponding days of the same week.
Advanced nonlinear methods for call centre forecasting
We provide expertise in time series and causal prediction (to include key drives e.g. catalog mailings, unplanned publicity, back orders and holidays) of call centre volume using artifical neural networks, support vector regression and other methods of artificial intelligence. This includes a fully-working software simulator dedicated to time series prediction with artifical neural networks.
Automatic event and outlier detection
Advanced automatic event and outlier detection to forecast and explain the impact of special events including special promotions and ad campaigns. Use of time series clustering, time series classification and data mining for identifying and exploring special features including days of interest, call types and class of promotions.
Improving expert judgement
The complex and dynamic nature of call centre management means that judgment plays a key role in the forecasting process and affects the accuracy of any forecast and the resulting staffing plan. We have amassed several years of experience in enhancing expert judgment, and have developed robust processes for combining expert judgement with baseline forecasts for both operational and tactical planning needs of call centres.
Combining forecasts is now the standard approach to improving forecast accuracy and robustness. We provide simple unweighted (mean, median) to complex optimal weighted combination methods, as well as more recently developed active model combination methods including bagging and boosting for improving your forecasting accuracy.
Econometric Models Building for British Gas Call Centre @ British Gas
Call Centre Demand Forecasting @ Barclaycard
One PhD student, Xi Chen integrates call centre foecasting and queuing decision models for overall planning. His research has achieved substantial improvements in planning accuracy from combining daily call centre forecasting with optimal queuing models for staffing decisions.
Through the Masters programme at Lancaster University, we facilitate the development of young researchers in the area of call centre forecasting conducting a number of Masters student projects in call centre forecasting and management. Examples of these initiatives include:
- Modelling Royal Mail IT Support Call Centres, Royal Mail
- Development of a Call Centre Resource Management Tool, Call Centre Co.
- Call Centre Simulation, NTL
- Adding to AT&T Call Centre Consultants' Toolkits, AT&T
- Index Extra Call Centre Simulation, Littlewoods
- Call Centre Resource Scheduling for Sales Team, Premierline
- Effect of a BT Price Change on the Volume of Incoming International Calls, BT
- Analysing Reason For Calls, BT
- Analysing call drivers of inbound calls using sent bills & letters, British Gas
- Automatic forecasting of inbound calls using Weather Influences, British Gas
The Forecasting Centre regularly engages in general as well as custom-made training courses to various call centre teams. Past courses included attendees from British Gas, Barclaycard, Capita Dixons, Scottish Power, etc.
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