Using Sentiment Analysis to Improve CX

Using Sentiment Analysis to Improve CX

Your customers are always talking to you, but how well are you listening to how they are feeling? The quantitative feedback you receive from customers in terms of NPS, CSAT or CES metrics is incredibly valuable in terms of understanding how your business is performing. Examining customer feedback in terms of verbatim comments – whether in a survey, on social media, on review sites, or from speech to text transcriptions – can be a time consuming and thankless task, and oftentimes the negative feedback takes priority. This is where sentiment analysis can come in to make a massive difference in how you interpret and understand customer feedback. 

What is Sentiment Analysis?

Sentiment analysis refers to the use of natural language processing (NLP), text analysis, and computational linguistics to systematically identify, extract, quantify, and study affective states and subjective information.

Comments left by customers can be categorised by sentiment as positive, negative or neutral. Analysing customer comments together allows a business to gain a greater understanding of how customers feel about their experiences with your brand. It is of particular use when you have a large amount of text or comments to understand, giving you a comprehensive view of sentiment towards your brand at any given time. 

Your ability to have customised, accurate data reflected through sentiment analysis is enabled by advanced tools as part of an NLP system. This includes lemmatisation which will bring together different forms of a word so they can be analysed as one like plurals or different tenses (i.e., run, runs, ran, running); disambiguation which identifies the different meanings of the same words (identifying bass as a fish, instrument or sound); part-of-speech tagging which can tell if a word is a verb, noun, or adjective; and named entity recognition (NER) through which you can determine words that can be categorised into groups (i.e., people, places, items). The addition of these features enables you to perform a robust analysis of your customer feedback data set with ease so you can focus on results. 

Sentiment analysis tools will also help you to categorize feedback and comments based on sentiment (positive, negative or neutral) and categorise surrounding particular terms or phrases. Any sophisticated tool should allow you to search “employees,” for example, to get a pulse on how your customers are feeling about your staff or alternatively for specific products, services, policies or locations. 

Improving CX with Sentiment Analysis

Introducing sentiment analysis to your voice of the customer and social media monitoring processes can offer you a greater realm of opportunity to quickly identify and improve areas of concern for the customer. By analysing comments in this way you can learn your customers’ honest, unfiltered opinions to identify gaps in your service, products and offerings. 

You also should be careful not to silo all of these data-rich points of analysis. Feedback via chat, IVR, email, social media and review sites should all be examined holistically. To perform the most robust analysis and to have the greatest understanding of customer sentiment towards your brand, you need to look at each point of communication or feedback in conjunction with all of the others. This consolidation of feedback allows you to grasp a wider perspective on what is being said across channels which otherwise might be analysed by separate teams at separate intervals.

When done well, you can use sentiment analysis to identify your biggest fans and your greatest weaknesses. Where you’re really winning and your areas of improvement. It can be used to aid in customer outreach, social media and marketing strategising, product development, staffing determinations and countless other use cases.