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. In this project, we will examine the effects of customer satisfaction on sales and other business performance metrics by analyzing the relationship of NPS scores with business performance.
Dataset Description
The dataset we created for this project includes customer demographics, NPS scores, customer feedback, and business performance metrics. The data set, like real-world data, is contaminated with incomplete, erroneous and outlier 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)
TotalPurchases: Total number of purchases by the customer
TotalRevenue: Customer's total spend
CustomerLifetimeValue (CLV): Customer lifetime value
Project Steps
Data Loading and First Look: We will load the dataset and take a general look. We will examine the structure of the data and identify 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 Segmentation: We will segment customers as Promoter, Passive and Detractor according to their NPS scores.
Business Performance Analysis: We will analyze the business performance metrics of each segment.
Visualization of Results: We will present the analysis results by visualizing them.
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