NPS Calculation Method

The Net Promoter Score (NPS) is based on a single question: "How likely are you to recommend our company/product to a friend or colleague on a scale of 0–10?" Customers are categorized into three groups based on their responses: detractors (score 0–6), passives (score 7–8), and promoters (score 9–10). NPS is calculated by subtracting the percentage of detractors from the percentage of promoters.

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NPS in Business Management

Numerous studies have shown that NPS is strongly linked to company growth and profitability (e.g., Keiningham et al. 2007). Reichheld (2003), known for developing NPS, has demonstrated that NPS predicts company growth better than traditional satisfaction metrics. Additionally, NPS is considered a reliable measure for improving customer experience and building a brand (e.g., Morgan & Rego, 2006).

From a business management perspective, it is crucial to recognize that customer loyalty significantly impacts a company's ability to create value (Buoye & Reichheld, 2008). Therefore, it is justified to make NPS a central guideline for customer relationship management.

Counterpoint: Challenges of NPS

Despite its popularity as a business development tool, NPS has faced criticism. Some researchers have questioned its reliability and validity as a measure of customer experience. For instance, Rietveld and van Rossum (2018) point out that the simplicity of NPS can lead to an overly simplified understanding of customer satisfaction.

Furthermore, it has been observed that NPS results can vary across different industries and cultures (Mittal & Kamakura, 2001). Another challenge is that NPS alone does not indicate which business development actions should be taken. There is a solution to this, which we will return to at the end of this post.

Effective Utilization of NPS

While NPS has been criticized for its simplicity, it can be effectively used as part of a broader customer experience measurement strategy. For example, Verhoef et al. (2017) suggest that NPS should be combined with other metrics, such as customer satisfaction index and customer loyalty measures, to gain a more comprehensive view of customer experience. Additionally, it is important to analyze NPS results more deeply and identify key factors that influence customers' willingness to recommend.

Figure: NPS root cause analysis delves into text feedback and themes essential NPS drivers.

Borges and Aksoy (2017) have studied the development of NPS through the identification of outliers. In our company’s development work, we have deepened the idea of identifying root causes behind NPS using generative AI. This approach identifies outliers as part of the analysis but places particular emphasis on identifying and thematizing the factors that strongly impact NPS.

Although NPS has been criticized, it is an effective tool for measuring customer experience and business development. By combining it with other metrics and carefully analyzing the results, companies can gain valuable insights into customer needs and expectations, helping to improve products and services and strengthen customer relationships.

Additionally, by deepening customer listening, companies can unleash the creative capacity of customers for the benefit of the company (Kristensson et al., 2002; Matthing et al., 2004), supporting product and service development.

At Zeffi, we have also embraced this approach using AI - AI-conducted root cause interviews combined with thematic summarizing analysis have proven to be a strong way to advance the utilization of NPS. If you are interested in an AI-based approach to the root causes of NPS, feel free to contact the author. More information is also available on our separate web page for AI-based NPS root cause analysis: Esko.AI.

Sources

Borges, A., & Aksoy, L. (2017). Improving the robustness of the Net Promoter Score by detecting outliers. Journal of Service Management, 28(5), 998-1022.

Buoye, A., & Reichheld, F. F. (2008). The economics of loyalty—Unlocking the value of frontline engagement and the Net Promoter Score. Journal of Service Research, 11(4), 357-373.

Keiningham, T., Aksoy, L., Buoye, A., & Cooil, B. (2007). Customer metrics and their impact on financial performance. Marketing Science, 26(3), 305-315.

Kristensson, P., Magnusson, P. R., & Matthing, J. (2002). Users as a hidden resource for creativity: Findings from an experimental study on user involvement. Creativity and Innovation Management, 11(1), 55-61.

Matthing, J., Sandén, B., & Edvardsson, B. (2004). New service development: Learning from and with customers. International Journal of Service Industry Management, 15(5), 479-498.

Mittal, V., & Kamakura, W. A. (2001). Satisfaction, Repurchase Intent, and Repurchase Behavior: Investigating the Moderating Effect of Customer Characteristics. Journal of Marketing Research, 38(1), 131-142.

Morgan, N. A., & Rego, L. L. (2006). The Value of Different Customer Satisfaction and Loyalty Metrics in Predicting Customer Retention, Recommendation, and Share-of-Wallet. Managing Service Quality: Managing Service Quality: An International Journal, 16(5), 395-412.

Reichheld, F. F. (2003). The One Number You Need to Grow. Harvard Business Review.

Rietveld, P., & van Rossum, W. (2018). The Net Promoter Score: A Critical Examination of the Method and Its Limitations. In The Customer Success Economy: Why Companies Struggle to Adopt Customer-Centric Practices and What to Do About It.

Verhoef, P. C., Franses, P. H., & Hoekstra, J. C. (2017). The Effectiveness of Different Customer Feedback Metrics for Loyalty: An Exploratory Study. Journal of Retailing and Consumer Services, 37, 1-11.

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