Net Promoter Score (NPS) is an important metric that measures how recommended a company is by its customers. NPS evaluates customers' likelihood of recommending a product or service to others on a scale of 0-10 and divides these scores into three main categories: Promoter, Passive, and Detractor. This project aims to measure customer satisfaction and loyalty by calculating NPS scores.
Dataset Description
The dataset we created for this project includes customer demographics, NPS scores, and customer feedback. The data set is contaminated with incomplete, erroneous and outlier data, just like real-world data.
CustomerID: Unique ID of the customer
Gender: Customer's gender (Male, Female, Other)
Age: Customer's age
Region: The region where the customer lives (North, South, East, West)
SubscriptionType: Subscription type (Basic, Standard, Premium)
NPSScore: NPS score (0-10)
Feedback: Customer feedback (Original text)
Project Steps
Data Loading and First Look: We will load the dataset and get an overview. We will examine the structure of the data and detect missing or incorrect data.
Cleaning of Missing and Erroneous Data: We will fill in the missing values and correct the incorrect data. In this step, we will perform the necessary cleaning operations to ensure the accuracy and integrity of the data.
NPS Calculation: We will calculate the overall NPS score using customers' NPS scores.
Demographic and Behavioral Analysis: We will analyze NPS scores with demographic and behavioral data to determine their impact on customer satisfaction.
Visualization of Results: We will present the analysis results by visualizing them.
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