For many organisations, the Net Promoter Score (NPS) is a critical measure of customer experience and satisfaction. NPS is a measurement of advocacy, asking customers how likely they would be, on a scale from zero to ten, to recommend that company to others.
NPS works for many companies for good reason – it’s a single number that is easy to collect and easy to understand. Those involved in the day to day management of NPS often realise that while distilling customer satisfaction to a single metric makes tracking and sharing simple, this simplicity comes at a cost: it’s not always simple to know what drives your NPS. So while you might have a score, you are left with no idea of what your customers think.
Some attempt to overcome this with a follow up open-ended question asking why the customer scored that way. An open-ended question allows customers to provide feedback on their terms, using their own words. This means it can reveal problems and issues that you may not have anticipated.
However analysing and understanding open-ended text data can be time consuming and difficult. Often it’s just too hard and the answers are ignored – and all that insight is lost!
This is where text mining comes in. Text mining automates the sorting and classification of text data, allowing you to quickly identify themes, phrases and words that occur throughout the responses.
It’s important to remember that text mining does not provide all the answers and is not a black box – it requires human input to ensure the classifications make sense and to interpret the outputs. This is still the most important aspect of any analysis. What it does do is make the process much faster: it takes care of most of the manual, repetitive tasks quickly, freeing the human expert to focus on learning and uncovering insight.
The result is that large unwieldy volumes of diverse text responses become a valuable, usable data source that can add a new level of understanding to your company’s NPS score.
While having one number to measure customer satisfaction is appealingly simple, it is rarely enough to provide the insight needed to take action. To extract real meaning we need to look beyond the numbers to truly understand what customers think.
David Templeman is Research Director at Edentify