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Fact Versus Fiction - Gathering Real Insight from Callers

Posted by: Todd Bray @ 7/04/2010 2:38:39 PM

There’s no denying that when a customer calls your organisation, it’s one of the most important (and in some cases the only) interactions you will have with them.  These calls provide a valuable opportunity to impress your customers; they also give you the opportunity to gain vital information about your customers and their needs – all you need to do is listen.

In my work in Australia and the UK, I’m always coming across new opportunities for organisations to gain greater comprehension of their customer behaviours. A prime opportunity to gather this insight comes at the start of a call when the customer communicates why they are calling. Traditional touch tone IVR menus only allow customers to tell you which department they ‘think’ may be able to assist with their enquiry. Pigeon holing customer demands can often give a distorted and potentially misleading view, of what the customer actually wants. Whereas natural language speech recognition gives customers the freedom to say what’s really on their minds.

Asking the customer to explain the reason for their call (simply and in their own words) provides a much more accurate and meaningful picture. For example, if the customer says a short sentence like “I have a question about a recent transaction on my account”, this tells you what they really want and provides a far greater understanding, with less customer effort, than you could ever hope to gain from touch tone menus.

Understanding and recording the ‘real’ reasons why customers are calling offers insight into the effectiveness of the processes that your organisation provides.  It doesn’t rely on a call centre agent inconsistently selecting the closest wrap code for a call. The reason for the call is collected in the customers’ words every time and as a result, it helps you understand the drivers for customer demand and how the interaction between different components of your organisation impacts the customer experience. For example, if many customers are calling to check on the status of applications it may indicate that back office efficiencies, or communication of expectations, need to be addressed.

Of course the information that customers give you on the call can be intelligently used in real time too, to help make intelligent informed decisions on the best agent to route the call to.

It is well known that retention conversations are more effective if the caller is connected directly to a retentions agent without any transfers. By listening for retention triggers the natural language IVR can route customers that are at risk of leaving the organisation, to a skilled agent for a retentions conversation. This approach will improve the customer experience and increase retention conversion rates.

By knowing who your customers are through automated identification and verification (ID&V),  and what they wanted the last time they called, different treatments can be applied to subsequent calls to drive business outcomes. For example, it may be appropriate to let a customer self serve for a valuation enquiry, but if they call again soon after with the same demand, it may be appropriate to connect the customer to a skilled agent to have a conversation to understand, and act on, the underlying reasons for the call.

It’s important to note that each customer should be treated individually and not all enquiries are suitable for speech automation or self service. For example a death enquiry in the Life and Pensions industry should immediately be routed to an agent, with the appropriate soft skills, without any further automated interactions.

Natural language speech recognition can do more than identify, verify and direct customers to the right team. Whilst it’s been proven to improve efficiency, security and the customer experience, organisations often forget that it provides another secret weapon to help you make the most of those few minutes you have your customers attention.

It's just a matter of taking the time to listen.

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