April 29, 2022
How to better prepare for major world events that effect contact centres
Contact centres have been a key part of companies’ customer service and sales since the 1960s. They have been a test bed for new telecommunication networks and software, including call routing, customer relationship management software (CRM) and cloud computing. Could “speech analytics” be the next big tech trend for contact centres?
According to research by Gartner, a research and advisory company, nine in ten (89%) of customer service and support leaders still rely on a “post-transaction” review of a call (typically an agent’s written record of a call). However, there is increasing interest in speech analytics – software that transcribes voice calls to text and analyses the calls for customer sentiment and how well the agent handles the call.
Customer service and support leaders said that understanding the customer’s experience through data is one of the most urgent priorities in 2021, the research found.
Speech and text analytics are not the most common tools for collecting data − used by 45% and 25% of organisations, respectively, the Gartner research found. However, eight in 10 (79%) of customer service leaders said that using speech analytics technology to review calls would, within five years, be the most valuable way to collect customer data.
We agree. Currently, however, much speech analytics technology is just not good enough. Much of it has been over-hyped, is too expensive and does not provide customers with a quick enough return on expenditure. And we aren’t just saying this because we think Listen is better; we’ve experienced it first-hand.
Most platforms are designed by analysts for analysts. You need data scientists to get full value from it.
Our conversation analytics platform – the cheapest on the market − can tell you what your customer says and feels. It analyses calls in two main ways. The first way is by analysing key words and phrases during calls (for example, when a customer asks to “speak to a manager” or “supervisor”, or “I’d like a refund”). The second way is to understand the sentiment, or emotion, in a call.
Our machine learning technology and algorithms in our speech analytics platform can also measure the quality of customer service during calls by analysing empathy and the skill of the agent in negotiation. It does far more than tracking metrics such as average call handling time, number of abandoned calls, and percentage of calls whose queries were dealt with on the first time (important as those things are).
If a customer has recently been in a car crash or their ceiling has collapsed, a good agent will naturally show empathy and tact when the customer calls the contact centre.
For calls like this, our speech analytics software will scan the call for phrases such as “I can only imagine” or “That must be distressing for you”. Companies can use this data for an overview of how their contact centres are handling tricky calls.
Empathy is important for customers and should be measured. It’s a good measure of customer experience because there is a link between advisers hitting these customer metrics and increasing customer lifetime value.
To fulfil its potential, speech analytics software must also measure beyond what is being said—it needs to understand what is being implied; for example whether a customer is feeling vulnerable. Perhaps the customer is hard of hearing but doesn’t want to admit to needing more support. Or perhaps they are concerned about their finances, which is one reason why they have missed two consecutive payments for their credit card balance.
Good speech analytics software can also help your business understand why customers are calling your contact centre, and whether some of the calls could be dealt with more efficiently in other ways.
Our machine learning technology analyses contact centre conversations and puts them into categories − order query, request for refund, insurance claim etc. If lots of calls are about your store opening times that is a “low-value” transaction that could be better dealt with online. The aim should always be for a contact centre to get fewer calls from customers.
Our customers are using speech analytics to improve customer service and increase profits. We helped one customer − a top-five supermarket − work out the most common reasons for customers calling its contact centre to complain. By changing some product prices, we helped to improve customer sentiment by 10 percentage points. And by analysing other customer data we identified a £1 million opportunity for additional revenue through cross-selling.
Conversation analytics technology is being used in more contact centres.
The technology can help businesses improve their customer service, understand their customers and improve the productivity of their contact centre workers. As economies return to normal after the pandemic, conversation analytics could become the new normal for customer service.
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