The role of text analytics in a voice of the customer program

Chris Breslin says text analytics can explore beneath the surface of customer experience to create a richer image for your voice of the customer program.

It seems everyone has something to say these days and social media has provided a worldwide stage for them to say it on. But the din from all those people expressing themselves at once can make it difficult for them to be actually heard.

Tweets, Facebook posts, survey responses and call centre records are just some of the free-form content that organisations have to grapple with to determine what their customers are trying to tell them. To extract insight from this deepening trove of free-form content and add value to your voice of the customer program (VoC) you have to dive below the surface to find what lies beneath.

Manually sifting through all that unsolicited data and irregular information, however, is both time consuming and resource intensive – which is where text analytics comes on board. Integrating text analytics with your VoC program can help you react swiftly and meaningfully to enhance the customer experience and boost your bottom line.

Does it work?

Understandably, the idea of being able to take tens of thousands of your customers’ comments to get a genuine insight into what they are thinking may sound too good to be true. Certainly, many argue that technology can never pick up the nuances of languages as well as a real person.

In fact, people can have such vastly differing interpretations of the same statement that the right software can produce results almost exactly the same as a human analyst.

Furthermore, in a test which compared free-form product reviews on Amazon with the number of stars the reviewer provided for the product, some solutions achieved an 89% accuracy rating.

So it works. But how does that benefit you?

1. Making the complex simple

Basically, it takes the complexity out of understanding what customers and the wider market are saying about you.

Text analytics solutions are trained against the right types of language for your business to then categorise comments by sentiment. The sophistication of these solutions enables them to also understand complex comments which talk about several factors at once.

For example, ‘My hotel room was fine but we were very disappointed with the restaurant, it was slow and the food wasn’t as good as we’d expected. The concierge was wonderful, though, and recommended plenty of great restaurants nearby.’

A cutting-edge solution will be able to take this comment, split it into multiple parts and allocate a sentiment score to each part. The hotel’s restaurant will receive a negative score, while the concierge service will receive a positive one.

Multiply this by several thousand and add it to your VoC programme, and you can develop an incredibly rich picture of every facet of your company.

2. Getting sentimental

The lifeblood of any VoC programme is feedback.

Surveys in particular bring in a huge amount of data, and much of it is neatly packaged into ranking scores, tick boxes and sliding scales. Insight like this can take you a long way down the road of streamlining processes, responding to dissatisfied customers and understanding some of the key drivers behind customer behaviour.

But once your VoC programme has matured and some of the quick wins are behind you, adding text analytics can offer a new lease of life. For example, you can gain insight into what is being said across different categories of your business, such as stores, call centre, and your website.

Rather than having just a simple picture that shows how customers have rated their experiences, and whether they intend to use your company again, you can get to the critical ‘why?’ Most importantly, you can access the ‘why?’ at an aggregate level through sentiment analysis.

3. Taking action

Text analytics empowers you to take action that will have a significant impact.

For example, if you can identify from your business metrics that you’ve had a drop in your satisfaction or recommend scores, you can harness text analytics to understand why that’s happened. An important point here is that you are not necessarily dependent on survey responses telling you why customers have become less satisfied.

If a significant group of customers have started providing lower satisfaction scores, but failed to provide verbatim comments as to why they’ve done that, you can bring in free-form data from other sources.

When you add comments from contact centre records, CRM, social media and other unsolicited channels, and combine it with your survey data, the detail you uncover can provide clear causes. And when you have clear reasons for shifts in your metrics, you can take clear action to rectify the situation. VoC programmes are incredibly powerful, and can generate huge insight.

The addition of text analytics and sentiment analysis provides the next steps in ensuring that not only are you able to hear the customer, but you understand what they are saying.

 

Chris Breslin is country manager, Confirmit Australia

  • Memzie Mehmet

    Sentiment analysis has some major limitations, which include not being able to analyse multi-foci text, sarcasm and irony in texts, lacks contextual understanding and a few more. I working on something that will have the best of large scale sentiment analysis with a human touch