Brand Managers have for long been flooded with myriad of customer survey metrics. Surveys typically are a list of questions that often do not tie to each other cohesively. Net promoter Score provided an industry standard, quantifiable and easy to institutionalize metric to measure customer satisfaction.
- NPS coupled with type of customer can give us better understanding of the customer sentiments of different customer segments
- NPS framework can be used to segment customers based on satisfaction and loyalty.
- NPS score can be used to measure Company’s growth as it is highly correlated. As per Bain and Co., in most industries NPS accounts for 20% to 60% of a company’s organic growth.
- By time series analysis of NPS, customer churn can be predicted.
- Follow up questions to the promoters, passives and detractors can give us idea about the different reasons driving them. Understanding the reason behind lesser ratings can give us insights to convert detractors and passives into promoters and keeping the promoters happy.
- Comparing against the benchmark of the market gives us the areas that need attention.
Yet, given the high popularity and application of NPS, the framework is packed with significant drawbacks.
The NPS by itself might seem more intuitive than an average score because it is expressed as a percentage, but what makes good, average or poor scores varies a lot by industry (think cable companies versus luxury hotel chains). For example, the average NPS for consumer software products is 21% compared with about a 6% for cable providers.
Secondly, NPS measures the successful outcomes of loyalty and advocacy but does not explain what drives the score.
Thirdly, while NPS is a great measurement tool, it provides little to no insights into the next steps. A Brand manager is often clueless as to what drives a particular MPS score and what can be done to alter it over time.
Analytics on NPS
In addition to the NPS question, each respondent rates the brand/ company across a number of service-delivery attributes. This helps to derive that for each 1-point improvement in these attributes, NPS will improve by xx points.
Find out what’s driving NPS using Multiple Regression and beta coefficient.
- Correlation between the variables can identify the relationships.
- Then use stepwise regression to find out the different beta value for different variables to identify how much a variable affects NPS.
- Identifying the best fit and then selecting the variables that can affect NPS most. And hence improving those aspects
The analysis explain xx% of the variance in NPS i.e. if for each unit improvement in the determined elements, NPS score is improved by 0.xx points. This is helpful to determine at a corporate level those attributes that are most important for all customers.
Online Promoter Score
With the availability of various platforms across Internet for customers to share, connect and interact with the brands and individuals, there is a plethora of information getting piled up on a lot of services-review and social media sites. Customers are freely speaking about their brand experiences. These textual data can be mined and sentiment analysis along with various other NLP techniques performed to extract online promoter score for various brands.
Employee Promoter Score
Employee Promoter Score is a method to bring transparency in the company where employees have to recommend their workplace on a scale of 0 to 10. Loyal and engaged employees are enthusiastic about their work, affect other employees positively, provide better customer experiences, create energy within the organisation and give good feedback and creative ideas. Companies can discover which departments represent liabilities and which offer potential best practices. They can see which team leaders are doing the best job and which ones need more coaching. Ultimately, top management can also understand which elements of employee sentiment and engagement affect customer loyalty the most, so that they can identify ways to improve.
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Bhasker is a Data Science evangelist and practitioner with proven record of thought leadership and incubating analytics practices for various organizations. With over 16 years of experience in the area of Business Analytics, he is well recognized as an expert within the industry. Earlier, Bhasker worked as Vice President at Goldman Sachs. He is B.Tech from Indian Institute of Technology, Varanasi and MBA from Indian Institute of Management, Lucknow.