Speech and Text Analytics: why top performing businesses need it



Speech and Text Analytics: why top performing businesses need it


Many UK businesses now focus on customer experience as their main point of competitive differentiation, and profitability.

In fact, according to a round-up of CX stats from Forbes, brands with superior customer experience bring in 5.7 times more revenue than competitors that lag in customer experience.

Now more than ever, as physical contact with shops, banks and other businesses decreases, telephone, webchat and digital customer experience is now a focal point for performance improvement and competitive edge.

But how do you achieve that edge? One way is through driving positive emotion in the customer journey – which we have talked about before here – and the other is about understanding what is happening between your agents and your customers all of the time. Which leads us to ask, how do you gather that information? The answer is speech and text analytics.

What is speech analytics?

Speech (or voice) analytics is used to analyse recorded customer calls, mining them for information on the customer experience. Using selected criteria, it can evaluate all the telephone conversations between agents and customers, extracting high-quality and relevant information that can drive further analysis and inform decisions about how best to handle a given situation.

Speech analytics listens out for words, phrases, vocal patterns and emotions, and can detect displeasure, uncertainty, disappointment, happiness or even sarcasm, as well as keywords, silence, music, crosstalk, speech rate, and many other useful metrics in every conversation. Based on these parameters, conversations can be categorised, compliance levels checked, KPIs tracked and trends visualised.

Check out a previous blog on speech analytics here.

Why use speech analytics?

There are a host of benefits of using speech analytics, including:

  1. A more complete picture of how the contact centre is performing and how well customer interactions are being managed;
  2. Reducing complaints and legal cases by using emotion filters to help agents identify, act and handle bad situations before they escalate.
  3. Using data to retain and train contact centre agents, a common challenge for many businesses.

The evolution of speech analytics

With 60% of customers preferring to use the telephone to communicate with businesses, speech analytics is an invaluable tool to gauge and improve customer sentiment. Most businesses have this tool at their disposal, but with telephony continuing to make up such a large portion of customer communications, real-time speech analytics is a more advanced evolution and one that adds even more benefits.

A multinational telecommunications company used speech analytics to successfully increase sales efficiency. They implemented, at the end of each incoming call, an automatic sales offer. Without voice analytics, this reached 27% of callers, with a sales conversion rate of 5.5%. With voice analytics, which is able to identify which callers display uncertain or positive emotions, the top 20% of receptive callers were identified, resulting in a much improved sales conversion rate of 6.3%. With an average of 1.5 million incoming calls each year we can calculate that this means nearly 3,240 utilised sales opportunities. With an annual profit of €20 per call, this adds up to around €64,800 extra revenue for the company.

What is text analytics?

Now, let’s take a look at text analytics; what it is and where and why it’s used.

Text analytics analyses any type of unstructured data across email, chat, social media, reviews and surveys. Traditional analytics has always struggled to handle unstructured data, finding it difficult to recognise the true meaning of the language or terms used.

Modern text analytics can detect things like irony, which is often used in comments and reviews, and correctly categorise this as negative, rather than positive as it traditionally would.

Key topics and keywords are identified by the number of times they’re mentioned and by polarity (positive or negative) to bring attention to focus areas.

Using text analytics, you can automatically analyse any quantity of text with human-level accuracy to:

  • Identify the main topics and patterns
  • Quantify main opinions
  • Analyse targeted questions.

Example use case for text analytics

Text analytics is of particular use in customer feedback, especially surveys. Customer surveys have been an important tool for businesses for many years, but recently there has been a focus on large-scale analysis on calls as well as surveys. However, it is text analytics that is the missing piece of the puzzle to provide you with a 360-degree view of your business. It does this by scanning all comments to tell you what was good, bad, help you understand key topics and problem areas. The result is a much quicker view of feedback.

Text and speech analytics combined

Text analytics integrates seamlessly with speech analytics, giving you a panoramic view of your business, with text capabilities adding another level of emotion. Imagine the power of converting everything your customer says into text, then adding to that everything they ever have written in emails and social media!.

By combining speech with text analytics, you get a much more complete understanding of the voice of the customer. And that is what will help you to refine and improve customer communications, whilst driving up sales and call centre productivity.  

We recommend using both of these tools to effectively and comprehensively improve your company’s bottom line. Throw real-time speech analytics into the mix and you have a truly modern and all-encompassing customer experience analysis.


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