For decades customers have endured long queues at service centers. We queue for mortgage applications. We wait hours for medical attention. And, depending on where we live, we even queue to pay our parking tickets.
Sure, things have changed over the years. Queue management and appointment scheduling systems are reducing wait times. Some services are available online. Yet still, many of us find ourselves in long-winding queues, wasting time and feeling frustrated.
Unfortunately, most businesses only become aware of customer frustration when a person gives negative feedback or storms out of a branch–fuming about poor service. By then it's too late. You've lost a customer, and quite possibly someone's lost their job.
A case in point, I was queuing at my bank a few weeks ago when the lady behind me became so frustrated that she declared at the top of her voice: "That's it, I'm closing my account!".
And she did, demanding the manager transfer 100,000 USD of her hard-earned money to another bank. Needless to say, the next time I visited the branch there was a new manager.
Until now, the common approach to fixing customer satisfaction has been to hire more clerks, open more branches and implement self-service devices. But retail banking is becoming more competitive. And emerging competitors, already lean, are using technology to win business. Opening more branches is no longer a viable strategy. In countries like the United Kingdom, where the High Street is dying, banks have no choice but to close branches.
However, it's not all doom and gloom. Some innovative institutions are turning to Machine Learning (ML)–a technology based on mathematical models to help predict, optimize and 'train' data.
Leveraging ML, banks and their branches can accurately estimate how many people they expect at any given time. Allowing managers to proactively match and allocate resources based on real-time forecasts in demand.
As more data is collected, software applications begin to accurately identify patterns and trends. Predicting, for instance, a spike in home loan applications and ensuring suitably skilled mortgage processing agents are on hand–thereby improving the efficiency of your branch operations.
Machine Learning is already helping organizations like banks and retailers lower costs, improve customer service and streamline operations. To learn more about how ML can help your organization, get in touch.