Understanding and Applying Net Promoter Scores (NPS) using Data Distiller for Customer Satisfaction Analysis

What is NPS?

At its core, the Net Promoter Score (NPS) is calculated by asking customers a single, simple question: "On a scale of 0 to 10, how likely are you to recommend our product or service to a friend or colleague?" Based on their responses, customers are categorized into three groups:

• Promoters (scores 9–10): Loyal enthusiasts who will keep buying and actively recommend your brand to others.
• Passives (scores 7–8): Satisfied but unenthusiastic customers who are vulnerable to competitive offers.
• Detractors (scores 0–6): Unhappy customers who can damage your brand through negative word-of-mouth.

The NPS score itself is calculated using this simple formula:

NPS=%Promoters?%Detractors

The percentage of Promoters (%Promoters) refers to the proportion of customers classified as Promoters out of the total number of respondents, which includes Promoters, Passives, and Detractors. The same applies to the %Detractors.

This provides a number between -100 and +100 that reflects overall customer sentiment.

Traditional NPS: A Simple Yet Powerful Metric

The traditional NPS formula offers a binary view of customer sentiment by focusing only on Promoters and Detractors, while Passives are excluded from the score. This simplicity is its strength — it gives companies a quick, easy-to-understand view of how customers feel about their brand.

However, it's important to remember that while Passives don’t directly affect the NPS score, they still contribute to the overall sample size, which impacts the accuracy and reliability of the score. Companies should pay close attention to Passives, as they may require only a small nudge to become Promoters or, on the flip side, could easily turn into Detractors if not engaged effectively.

Industry Benchmarks: Where Does Your NPS Stand?

One of the key strengths of NPS is that it allows companies to benchmark themselves against industry standards. Here's a rough guideline of what NPS scores typically look like across industries:

• Above 50: This is generally considered excellent, reflecting high customer loyalty and satisfaction.
• Between 0 and 50: This range suggests moderate customer satisfaction, with room for improvement.
• Below 0: A negative NPS indicates more Detractors than Promoters, meaning there are significant issues with customer experience or satisfaction.

Each industry has its own NPS standards, and businesses should benchmark themselves within their specific sector. For example, industries like technology and luxury brands often see higher NPS scores, while sectors like telecommunications or utilities tend to have lower scores due to the nature of their services.

Weighted NPS: A More Nuanced View of Customer Loyalty

While traditional NPS provides valuable insights, it doesn’t account for the fact that not all customer feedback holds the same weight. In some cases, companies might want to give more importance to specific customer groups or segments. Enter weighted NPS.

In weighted NPS, each group of customers—Promoters, Passives, and Detractors—can be assigned different weights depending on their perceived value to the business. For example:

• Promoters may be given a higher weight if they are particularly valuable to the business in terms of revenue or referrals.
• Passives may be assigned a modest weight, as they are still likely to continue using the product or service, though they aren’t as enthusiastic as Promoters.
• Detractors could be weighted more heavily if the business wants to aggressively focus on reducing negative customer sentiment.

This approach allows for more tailored insights and enables companies to prioritize the areas where they want to focus their efforts.

Correlations: Unlocking Deeper Insights from NPS

The real value of NPS often comes from looking beyond the score itself and examining how it correlates with other customer attributes. For example, by analyzing correlations between customer behaviors (e.g., purchase frequency, customer support interactions) and their NPS score, businesses can uncover patterns that may help improve overall customer satisfaction.

In the tutorial below, we showcase weak correlations existed between NPS and several customer attributes like purchase frequency and average order value. This suggests that, with these current set of attributes, using a traditional linear regression or classification model may result in a model with low predictive power for estimating NPS.

This insight encourages us to explore new approaches:

1. Feature Engineering: Creating new features by combining or transforming existing ones (e.g., combining total spent and average order value to create a new "customer value" feature).
2. Non-Linear Models: Considering more advanced machine learning models such as decision trees, random forests, or gradient boosting machines (GBM), which are better at capturing complex relationships in the data.
3. Additional Attributes: Introducing new data points, such as customer satisfaction scores or social media interactions, that might provide stronger insights into customer loyalty.

Moving Beyond Basic NPS

NPS remains a critical tool for businesses to gauge customer satisfaction and loyalty, but it’s important to dig deeper. Whether through exploring weighted NPS to better reflect customer impact or conducting correlation analyses to reveal underlying trends, companies can unlock greater insights into what drives customer loyalty.

By leveraging these methods and employing more sophisticated modeling techniques, businesses can turn NPS from a simple score into a powerful driver of actionable customer insights.

Try the Tutorial

Link is here .