Contributed by: Alfiya Shaikh
I work at a firm as an associate program manager. My role here involves multiple aspects of operations, research, and project management. I lead a vertical where I have complete ownership of the service provided by the company, right from planning to executing to analyzing feedback. NPS (Net promoter score) is a very important parameter for any and every product/service in the market. While checking the responses of the NPS data, I realized that there were too many subjective questions and responses in the data; building a word cloud for the same and analyzing the text was important.
There was a dire need to check for the customer sentiment, areas of improvement, which aspects of the customer journey need more enhancement and, what were the concerns raised by the customers, what were the overall NPS scores and their graphical representations. I used the Text Mining tools such as NLTK, word cloud, and lambda functions to get a clear picture of the various parameters/features which were collected in the NPS data survey.
While doing the analysis, I removed the stop words, did the stemming of the data, removed the punctuations, and checked for the most frequently occurring words. This helped me to dig deeper and gave me multiple insights on the following points such as customers’ overall sentiment, concerns they faced while managing their journey, what factors added to their delight element, and what factors contributed to their negative or passive sentiments.
A meeting was held post my analysis, and we are in the process of implementing the feedback from the customers to enhance the overall experience for them. I had also graphically represented certain integer datatype features to get an in-depth understanding of the distribution of the feedback received from the customers.
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