NPS CRIS-AR Data Dictionary: A Comprehensive Overview
Understanding the intricacies of the NPS CRIS-AR data dictionary is crucial for anyone involved in customer experience management. This guide will delve into the various aspects of the dictionary, providing you with a detailed insight into its structure, components, and applications.
Data Dictionary Structure
The NPS CRIS-AR data dictionary is organized into several sections, each serving a specific purpose. Let’s explore these sections in detail.
| Section | Description |
|---|---|
| General Information | Contains details about the NPS CRIS-AR framework, including its purpose, scope, and methodology. |
| Variables | Lists all the variables used in the NPS CRIS-AR framework, along with their definitions and measurement units. |
| Scoring System | Describes the scoring system used to calculate the NPS score, including the different categories and their corresponding values. |
| Analysis Techniques | Outlines the various analysis techniques used to interpret the NPS CRIS-AR data, such as segmentation, correlation, and predictive modeling. |
Now that we have a general understanding of the structure, let’s dive deeper into each section.
General Information
The NPS CRIS-AR framework is designed to measure customer loyalty and satisfaction. It is based on the Net Promoter Score (NPS) methodology, which has been widely adopted by organizations around the world. The framework aims to provide a comprehensive view of customer experience by considering various factors, such as product quality, service delivery, and brand perception.
Variables
The NPS CRIS-AR data dictionary includes a wide range of variables that are used to assess customer experience. These variables can be categorized into four main groups:
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Product Quality: This group includes variables related to the features, performance, and reliability of the product.
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Service Delivery: This group includes variables related to the speed, efficiency, and responsiveness of the service provided.
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Brand Perception: This group includes variables related to the brand image, reputation, and trustworthiness.
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Customer Satisfaction: This group includes variables related to the overall satisfaction level of the customer.
Each variable is defined and measured using specific criteria, ensuring consistency and reliability in the data collection process.
Scoring System
The NPS CRIS-AR framework uses a scoring system to calculate the NPS score. The score is determined by asking customers to rate their likelihood of recommending the product or service to others on a scale of 0 to 10. Based on their response, customers are categorized into three groups:
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Promoters (9-10): Customers who are highly satisfied and likely to recommend the product or service.
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Passives (7-8): Customers who are satisfied but not likely to recommend the product or service.
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Detractors (0-6): Customers who are dissatisfied and likely to discourage others from using the product or service.
The NPS score is calculated by subtracting the percentage of Detractors from the percentage of Promoters. A higher NPS score indicates a better customer experience and higher customer loyalty.
Analysis Techniques
The NPS CRIS-AR data dictionary provides guidance on various analysis techniques that can be used to interpret the data. Some of the commonly used techniques include:
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Segmentation: This involves dividing the customer base into different segments based on demographics, psychographics, or behavior patterns. This helps in understanding the specific needs and preferences of different customer groups.
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Correlation: This involves analyzing the relationship between different variables to identify patterns and trends. For example, a strong correlation between product quality and customer satisfaction can indicate that improving product quality will lead to higher customer satisfaction.
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Predictive Modeling: This involves using statistical techniques to predict future customer behavior based
