The Ultimate Guide to Net Promoter Score | Chattermill
What You Need to Know about the Net Promoter System
In 2003, Frederick F. Reichheld introduced the concept of NPS in the seminal article ‘The One Number You Need to Grow‘. Following two years of research, measuring the link between survey responses of individual customers of a company and those individuals’ actual referral and purchase behaviour. Reichheld and his team found one question was correlated directly with differences in growth rates amongst competitors in multiple industries, “how likely is it that you would recommend [company X] to a friend or colleague?”.
It was a tipping point in the industry. A path to profitable growth lied in a company’s ability to get its loyal customers, in effect, to become their marketing department. Companies that enjoyed the highest rate of growth had the highest net promoter percentages among its competitors.
Reichheld found that a simple single question was able to show a correlation between NPS and long-term growth. It has now become a key performance indicator (KPI) for many industry leaders to understand customer loyalty. When combined with an open-ended question it provides and an actionable metric for enhancing your customer experience.
Indeed, other factors besides customer loyalty contribute to a companies growth, economic or industry expansion, innovation and so on. While it would be a bold claim to say it guarantees growth, in general, it can’t be achieved without it.
It’s now widely accepted that a simple real-time feedback loop is essential for businesses to measure loyalty and to use the Net Promoter Score (NPS) to measure customer loyalty has undoubtedly become industry best practise.
In our ultimate guide to NPS we cover the following key topics:
What is Net Promoter Score?
Net Promoter Score (NPS) is a seemingly simple yet effective way for companies to track and measure promoters and detractors to produce a precise measure of their performance through its customer’s eyes.
NPS is built on the basis that every company can divide their customers into three distinct buckets - promoters, passives and detractors and customers are categorised based on their response to an NPS survey question - “How likely is it that you would recommend us to a friend?” on a scale of 0-10.
Once you’ve identified your Promoters, Passives and Detractors with your NPS survey, close the loop with personalised follow-up communications to each group.
The follow-up communication is critical. NPS is not about a number but about learning and improving. Understanding the drivers behind the score provides vital insights into why people are loving or hating your product. The second follow up question is what gives NPS its one-two punch, and allows you close the feedback loop.
The goal is to discover patterns inside the data. For promoters, do more of what works. For detractors, do less of what’s causing customers to leave your product. For passive, ‘ignore’ them for now. If you work on decreasing detractors and increasing promoters, you’ll address the passives over time. The primary goal is to identify pain points in your system that are creating repeatable results.
The cool thing about growing your NPS is that if you continue to improve it, you will have more and more people going around saying your product is excellent. However, be warned, growing your NPS is hard and you won’t see results overnight. It requires a long-term effort to build out internal processes to understand how your customers feel about their experience at scale, providing you with actionable insights to create experiences that will have your customer coming back to you time and time again.
Calculating Your NPS
Your NPS is calculated by subtracting your percentage of detractors from your percentage of promoters. The best possible NPS is +100, and the worst is -100. Your passives are excluded from the calculation since their loyalty is relatively neutral.
Responses can be defined into three distinct clusters that represent different attitudes, sentiment and economic value.
Promoters (scored 9-10): Promoters are your biggest fans. They actively advocate your product on your behalf, bringing in the majority of referrals, and are far more likely than any group to remain customers. Their Customer Lifetime Value (CLV) is far greater than any others.
Passives (scored 7-8): Passives are satisfied for the time being but can defect at any time. Their referral rate is as much as 50% lower than promoters, and those referrals are of far less quality. Their CLV is also usually less than half that of promoters.
Detractors (scored 0-6): Detractors are unhappy customers and account for more than 80% of negative word-of-mouth opinion. They have the highest rates of churn and defection and harm your company’s reputation, putting off new customers.
The Net Promoter Score is a simple and straightforward metric that can be shared throughout the company with every function and team. You can also track by product, store, team, geography and more to focus on the goal of improving customer experience.
If you have more detractors than promoters the score will be negative and likewise positive for more promoters than detractors. Lower Net Promoter Scores can be indicators of bad customer experiences leading to potential losses of revenue, while higher Net Promoter Scores suggest a stronger performing business.
Net Promoter Score: Types of Surveys
A relational survey can be sent at any time, and it’s an excellent way to get a finger on the pulse of the relationship between your customers and your business. Relational surveys should be sent out often — a quarterly schedule is ideal.
However, while regular surveying keeps you up-to-date on customer feedback and loyalty, surveying your customers on such a consistent schedule risks “survey fatigue,” which can lower your overall response rate. It’s important to make sure your customers know you value their survey responses — some options are sending thank you emails or offering perks or discounts for customers who complete relational NPS surveys. It’s also important to only survey as often as you have time to analyse the feedback, respond to your customers and create an action plan based on their surveys (we’ll cover this in more detail in a later chapter).
A transactional survey is sent after an event, such as soon after a new customer purchases your product or service, or after the resolution of a support ticket. The timing is a little easier with transactional surveys than with relational surveys — just make sure to send the survey before too much time has passed after the event that triggered it. Some good guidelines are:
While transactional surveys aren’t as likely to cause “survey fatigue”, they also give a narrower picture of your NPS, since they relate to specific events and not your business as a whole.
Irrespective of the type of survey you choose, if you can pass any additional data you have for your customers into your survey emails, such as the product they ordered, location and length of time as a customer, you can then begin to piece together the opinions of different segments of your customers to understand their similarities and differences too.
Timing and Survey Frequency
When thinking about how frequently to carry out NPS surveys, there are several key considerations.
The first is the size of your customer base. The smaller your customer base, the larger the sample you need to survey each time or even wait longer for more responses to achieve a high response rate, which constrains how frequently you can send out NPS surveys.
The second consideration to make depends on the user’s lifecycle stage with your product. If the customer has just started to use your product, then they need a chance to internalise the changes of a product or brand before they form a substantive opinion. Timing is critical, and setting up NPS surveys like a behavioural drip email series that triggers based on a user lifecycle means each user can respond to the NPS survey at comparable points in their experience. For instance, you can see data on how opinions on difference segments new users vs longtime promoters change after specific product updates.
Surveying the right number of customers
To get a clear picture of your NPS, you need a good sample of responses that represents your entire customer population. This can be tricky to get, but the bottom line is that the more responses you can get, the better your data will be.
It takes some complex math to get the exact number of responses will be representative of your business’ customer base, but a good rule of thumb is to assume that only 15 percent of the customers you send surveys to will actually respond, which means you need to send out a minimum of 1,700 surveys to get 250 responses, what’s generally considered a good sample for calculating NPS.
Net Promoter Score: Analysis
If your goal is to build exceptional experiences for your customers, it’s essential you get critical stakeholders on the product, marketing, business ops, customer service onboard right from the beginning. Without this, the NPS survey analysis may get overlooked during the product development lifecycle. It’s also essential the information be easily accessible on dashboards across multiple teams to ensure company efforts are aligned on improving the rights areas.
Secondly, while your NPS score is useful for maintaining a constant finger on the pulse of how your customers feel about your business or service, analysing the open-ended questions included on your survey gives you the most actionable part of the NPS survey.
To get to the ‘why’ of your score if you receive thousands of verbatims monthly then automatically categorising the open-ended verbatim from promoters and detractors into specific themes and tagging comment sentiment using text analytics enables you to get the most value from your customer feedback. However, if you’re an early stage start-up that only receives a handful of verbatims a month you can hold off investing in advanced text analytics tools. You can just as easily roll up your sleeves and read and categorise verbatims on your own.
A theme is simply feedback around a recurring topic; for example, shipping times, support tickets or prices. You may already have an idea of some themes just from reading through the feedback you’ve already received. Or, as you begin to tag your feedback, you’ll likely start to see themes naturally emerging. One potential difficulty with tracking themes is how many different ways there are to give feedback around one theme;
for example, if your theme is “shipping times,” you may see feedback like “fast shipping,” “lightning quick” and “arrived on time.” There are so many different ways for people to refer to the same theme, which can make theme tagging labour intensive.
Another way to organize analytics is by the sentiment of the customer. Sentiment is more closely related to NPS because, broadly, it means assigning a metric to a piece of feedback that details how positive or negative that feedback is.
Approaches to text analytics
There are a number of ways to analyse your customers feedback.
Rule-based analytics rely on sets of rules to determine which keywords found in customer feedback are positive and which are negative, and then determine a cumulative score for the feedback based on how many positive and negative words were found. Since rule-based analytics can function only within the boundaries of their existing rules, they work well when the scope of what they’re analyzing is narrow.
Excel Macro Approaches
It’s possible to use Excel macros to collect rule-based analytics. Keywords can have values attached that denote how positive or negative they are, and then Excel will assign an average value to each piece of feedback depending on the number of positive and negative keywords found. This method is limited because keywords must be manually entered and assigned value.
Natural Language Processing, which is an application of artificial and machine learning to unstructured data is quickly becoming a need to have for customer experience teams. Supplementing structured feedback from NPS with text analytics is the most advanced way to measure customer feedback.
Many customers respond to the NPS follow-up question, “Care to tell us why you’ve given us this score?” Promoters, for example, want organisations to continue to do well, but they often ask for specific changes. Acting upon unstructured data has been painful in the past and has involved teams crawling through feedback on a 1:1 basis. It’s inconceivable for a large company to ask every customer for feedback and to read each one. If you try to do it yourself, you will struggle to notice an emerging theme and fail as soon as your volumes get into 100’s.
Fortunately, modern technology allows you to break the trade-off between quality and quantity of insight. AI-powered text analytics tools can analyse large-scale responses, surfaces key themes, and reveal what is truly significant to your business. With machine learning, you can understand every single comment and provide you with an aggregated view of what drives your feedback in real time.
In today’s world, an NPS score alone cannot provide the full picture of the customer experience. Natural language understanding is a critical component to customer feedback mechanisms like NPS. It enables organisations to listen to the voice of the customer to develop a complete understanding of the customer’s needs.
How to Improve your NPS
Here’s how you should start the cycle of improvement:
- Discover your baseline Net Promoter Score
- Find patterns in the data
- Get more data
- Make an action plan based on the feed - and act on it
Discover your baseline NPS score
It’s now time to launch an NPS survey. You can begin by sending out transactional surveys soon after an interaction with a customer, such as an email after a purchase, or after a self service experience on a website.
Some businesses choose to send out relational NPS survey on a rolling basis and like to stay up to date every day. Others launch one for a fixed time, close it, and launch a new one after they’ve taken an action based on the results of the previous survey. After collecting feedback you can begin to calculate your baseline NPS score.
Pay close attention to your responses and funnel detractors to customer support. If you have large volumes of feedback it’s possible to funnel feedback and complaints to teams automatically using AI!
After analysing your results, you’ll learn what works for your customers and what doesn’t. If you’re a small business with less than 100 responses per survey, it’s possible to pinpoint themes and sentiments in the data and learn what does and doesn’t work for your customers.
If your company is receiving large volumes of feedback, it becomes an impossible task to learn what your customers are talking about and how they feel about their experiences at scale. It may be time to turn to advanced text analytic platforms to unlock data-driven insights from your feedback.
Using CX management technology to create bespoke theme structures and detect sentiment within feedback using machine learning to process data in real time. Advanced text analytic tools will highlight areas of the customer experience that demand the most attention. Making pinpointing customer pain points a quick and easy task.
Get more data
It’s essential to launch multiple feedback channels to maximise response rate to the NPS surveys. It would help if you offered surveys across both your desktop, mobile experiences, SMS messages and non-digital touch points. While you could create such tool in-house, we encourage firms to use collection tools that support feedback collection across a variety of user interfaces, such as solutions offered by Usabilla.
Don’t forget to get in touch with people who have churned and ask what made them churn, or why they switched to a competitor.
Always be sensitive to where you put your feedback channel. It shouldn’t interrupt the user’s experience, and it shouldn’t feel like a chore. Make it voluntary, short, and fun. Your data collection point is a touchpoint like any other and can make or break an impression of the brand.
Action Plan Based on Feedback
When you have enough feedback from all of your channels, it’s time to find out how to squash your bugs.
While teams spend much time looking at NPS detractors and how to address their pain points, it’s equally if not more so to spend time on promoters. Understanding what was different about their experiences to make them successful.
Finding correlations between specific customer experiences and NPS results (logins, delivery time, customer service, contact options) can help deduce what delights your customers and turn them into promoters. Then you can focus on optimising your customer experience and tracking NPS and Sentiment over time to get more of your customer base to this point. With the help of new advances in machine learning and text analytics, it is now quick and easy to chart specific themes and product features against NPS and Sentiment over time.
Systemise and Encourage Internal Buy In
Improving your customer experience is cyclical, not linear. That’s why the final stage is to systemize the process. Launch another NPS survey with the next cohort of customers. Take note of the improved score and report continuously in an easy and digestible way to share result company wide. Be sure to set up instant notifications of low scores so you can react in real time to a drop in NPS.
Remember to measure continuously over time to ensure a constant flow of data as it’s the only way to get the magical moments and optimise your customer experience.