python net promoter score
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Net Promoter Score (NPS) is a widely used metric to measure customer satisfaction and loyalty. It is based on the simple question: "How likely is it that you would recommend our product/service to a friend or colleague?" Respondents typically answer on a scale from 0 to 10. Based on their responses, customers are categorized into Promoters (score 9-10), Passives (score 7-8), and Detractors (score 0-6).
In this tutorial, we will implement a simple Python script to calculate and analyze the Net Promoter Score using a sample dataset.
Make sure you have Python installed on your system. You can download it from python.org.
Let's create a sample dataset to represent customer responses. You can replace this with your actual dataset.
Now, let's write a Python function to calculate the Net Promoter Score.
Now, let's call the function with our sample dataset and analyze the results.
This tutorial provides a simple example of how to calculate and analyze the Net Promoter Score using Python. You can adapt the code to work with your actual dataset and integrate it into your customer feedback analysis workflow.
Remember that NPS is just one metric, and it's essential to consider other factors and feedback sources to gain a comprehensive understanding of customer satisfaction.
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