The Net Promoter Score (NPS) is a relationship survey that allows businesses understand whether customers would recommend them to others. However, the validity of survey results can get compromised as a result of many biases introduced into the data from sampling and administration procedures.
The legitimacy of the survey primarily depends on the nature of sample, a subset of the target population, taken for calculating the score. Meaning, if the sample is limited and biased, then outcome of the survey surely will not be accurate and trustworthy.
Biases are common in sampling and surveys, and while it’s impracticable to remove them entirely, there are ways to reduce their impact by increasing the data quality. In this blog post, you’ll know how selection bias can be a problem and how you can reduce its interference in business.
Here’s a common situation:
A business surveys its clients and sets a strong Net Promoter Score. After careful evaluation, the company comes to know that the high score is a result of responses fed only by those customers who maintain a strong relationship with the company.
A situation like this provides firms with all of the "biased" data that can amplify the overall score without considering any of the data from customers that aren’t connected directly with the business. The end result is an artificially high score– one that can give the company an overly optimistic view of customer health.
In many other cases, there can be a selection bias that systemically includes or excludes certain segments of customers on the basis of demographics or other attributes in order to influence an NPS score.
It is a known fact that metric systems such as Net Promoter Score is designed for accuracy. The higher the response rate of the survey, the higher is the accuracy. In fact, many NPS metric experts recommend a minimum response rate of 40% for business-to-consumer businesses and 60% for business-to-business enterprises.
Response rate lesser than this is likely to be impacted by selection bias and could present an overly positive picture of a business’s ability to meet customer needs and retain them. This means that if organizations want to make sure that their data isn’t affected by any sort of bias, they need to aim for a higher-than-average response rate. However, this isn't enough.
For ensuring this score’s accuracy, companies need to trim selection bias to a minimum. This will not only improve the integrity of the score but also measure the customer’s intent and behavior precisely.
The authenticity of Net Promoter Score can be improved by reducing selection bias. And, to reduce this bias, business users can take several steps:
Companies must reach out directly to clients and customers to get honest opinions about their product/service. They must connect with their customers to comprehend the reasons for feedback and function operationally to resolve service gaps, ultimately closing the loop and substantiating the authenticity of survey results.
The sample must include a large number of customers and clients. This will allow companies balance out the biased results from friends and acquaintances with the greater amount of results from non-friendly customers.
Rather than focusing on static NPS data which includes the score at any point in time, companies must target the progress their business makes by measuring improvements in NPS. These are much less likely to be affected by biased results.
By taking all these steps, companies can remove the selection bias to ensure the integrity of their NPS survey. Go through our comprehensive whitepaper on NPS to dig up more details.