Retail banks attempt to grow business by cross-selling to the customers visiting branches. However, customers generally want to be served first, and retail banks lose market share in absence of smart customer segmentation and speed of service. As such, customer service is a key driver to retain and grow a customer base.
One solution to achieve faster service is that a bank can invest more in their branches. This will lead to higher operational costs (for example, larger real estate, more tellers, etc.). Such a problem becomes exacerbated if a retail bank is serving a large number of customers in a branch (typical, for example, in developing countries such as India, China, Brazil, Thailand, etc.). Legal regulations create additional demands on the cost of serving customers (for example, in Brazil there are legal regulations to limit the time a customer spends waiting in line for branch tellers). Failure to comply can result in monetary penalties being imposed on the bank.
Existing queue management approaches help bank schedule services, typically using a FIFO scheduling policy. However, existing approaches do not capture and/or take customer profiles or service characteristics into account, nor do existing approaches optimize on available resources. Additionally, existing approaches do not support a feature to generate management information systems (MIS) reports, nor do they offer automatic priority based dynamic scheduling. Generally, in existing approaches, a branch supervisor needs to identify customer and service priorities based on manual observations. Hence, a smart queue management needs a smart supervisor to constantly observe the queues and make adjustments to the allocation of customers to service counters.
Existing approaches do not provide a differentiated service to customers based on customer profiles and/or service characteristics. Further, existing scheduling systems cannot simultaneously take into account customer satisfaction (average wait time) and bank/service profitability (customer segmentation). Existing approaches additionally do not consider dynamic availability of resources, and do not include a concept of customer profile and prioritized service or real time capability (online).
Some existing approaches forecasts arrival pattern of customers based on historical data and uses that to schedule tellers. However, such approaches do not perform real time scheduling using actual arrival of customers, do not handle dynamic resource availability, and do not provide differentiated services.
Other existing approaches include, for example, allocating resources to jobs, where each job has a time-dependent cost function and each resource has a time at which it becomes available. Such approaches do not take into account job categorization and providing preferential treatment to critical and/or remunerative jobs, nor do such approaches take into account customer profiles and providing preferential treatment to important customers.