Optimising Staff Resourcing Levels at a Retail Bank
Customers interact with organisations via multiple channels. But whilst cost efficiency provides a clear motivation for businesses to increase customer self-service, many banking clients still prefer to call and talk to an agent.
BJSS’ client, a top-3 retail banking group, dedicated 80 full-time staff to manually forecast the likely demand on its network of call centres. This would inform the number of customer service staff needed at any half-hour interval to answer customer calls.
A manual, subjective and heavily error-prone process
The existing manual process relied heavily on subject matter expertise, and deep personal experience rather than systemised knowledge. Therefore, the most immediate need to impact performance was to improve efficiency by automating this forecasting process.
BJSS built the client’s demand forecasting tool to drive optimal staff resourcing levels
Accurate forecasting is critical to successfully managing a call centre. To make sure that there are enough agents to attend to clients, organisations deploy methods to precisely predict the call volumes they are likely to face. However, predicting the “future” is challenging. Inaccurate forecasts can result in under or over-staffing, and this can lead to a poor customer experience as well as operational inefficiencies.
The BJSS solution uses Python and Prophet and includes a front-end rebuild and data pipeline automation
We delivered time series analytics that include seasonality, covariate handling for events, and structural breaks.
This highly flexible, scalable solution has allowed the client to automatically produce 18-month forecasts in 30-minute intervals. It has reduced forecasting errors by 15%, improving resourcing decisions and levels of customer satisfaction.
However, this is just the starting point. On a larger scale, the client has engaged BJSS to deliver a modernisation initiative that covers many of its customer and non-customer facing capabilities.