Asking the customers for direct feedback is one of the best ways for companies to get actionable information. This data can be leveraged easily using Machine Learning and Text Analytics. Therefore, companies must focus on open-ended comments rather than just closed rating questions, to better understand customer issues and challenges.
Once, a continuous flow of comments has been established, the process is started.
Primarily, it comprises of two steps-
Therefore, we can raise trigger/alarms based merely on the tonality of the comments.
By leveraging Text Analytics, companies can analyse comments and as a first step pick out recurring topics (issues) that most customers are talking about (This is represented by the size of the bubble). Now, what Numr Research next is we utilise Regression Analysis to help companies prioritize the issues that have the biggest impact.
Notably, this Regression Analysis model isolates how much impact a topic will have on the overall Net Promoter Score®. For instance, it predicts whether a customer talking about trip cancellations is more likely to give a low NPS, as opposed to the one talking about fares and vice versa. As emphasised in the chart, the customer having issues with trip cancellations is a lot more likely to be negative in their outlook as compared to say car quality. And, this negativity is clearly reflected in the NPS.
Most importantly, this tells companies the topic that they should prioritise and handle first.
In a word, both.
NPS is a clean, manageable metric that an entire organisation (from the mangers to the frontline) can utilise.
However, relying only on open-ended comments is extremely effective in understanding the issues that need fixing. In conclusion, Text Analytics enables companies to get a firmer handle on everything that is going on with the customers.