“What grade did you get?”

Do you remember getting asked that question in grade school? Or maybe you were the one asking it? Humans like to know how they’re doing compared to everyone else.

This carries over into customer experience as well. At Wootric, we advise companies on setting up an effective Net Promoter Score (NPS) program. We get asked questions about NPS industry benchmarks all the time.

In general, we believe focusing on an external NPS benchmark is not incredibly helpful.

The Net Promoter System is the quantification of customer loyalty and the process for improving it over time. The power of this system lies in the analysis of feedback and the action taken based on that analysis.

However, net promoter score benchmarks are still useful in certain cases, which is what this article is all about.

If you’re unfamiliar with NPS, here’s a quick rundown:

Net Promoter Score (NPS) is a customer loyalty metric between -100 and 100 that captures the propensity of a company’s customers to attract and refer new business or/and repeat business.

NPS also stands for the Net Promoter System®, which was built around the Net Promoter Score. It is a model that ties a corporation’s bottom line to customer happiness and loyalty.

In the NPS survey, customers rate their likelihood to recommend your company on a scale of 0-10. To get your Net Promoter Score, take the percentage of people who are happy and willing to recommend your product or service (those who respond with a 9 or 10) — “promoters”– and subtract the percentage of people who would not be willing to recommend your product or service — (score of 0-6) “detractors.”

For example, a +50 NPS means that the company has more than 50% promoters and less than 50% detractors, so generally an NPS score of +50 is, indeed, great! You may see scales out there that say +30 is a decent score, and that +80 or greater is the ultimate dream score.

To learn more about NPS, get the ebook, The Modern Guide to Winning Customers with Net Promoter Score, which teaches how to modernize your NPS program for growth and higher loyalty.

Net Promoter Score industry benchmarks

There are two different types of NPS: absolute and relative. Absolute NPS refers to the NPS in and of itself, and comparing the score with what is generally considered a “good” or “bad” score. Relative NPS is taking into account the average NPS within an industry, which takes into account the factors that could affect an average Net Promoter Score, and can change the NPS benchmarks you set.

While an absolute NPS goal is nice and simple, it can be helpful to take a look at what others in your industry have been able to achieve, since every industry is different and has unique relative NPS results. The relative Net Promoter Scores generally achieved in each industry help construct what are called the NPS industry benchmarks. NPS Industry benchmarks give you a way to evaluate your NPS relative to your competitors. They help control for factors that often create major differences in what is considered a good NPS score.

Oftentimes, other companies in your industry have established an average NPS for you to use as a net promoter score benchmark. If you make smartphones or other tech hardware, for example, companies like Apple have been tracking NPS for years.

To get averages and examples from your industry, try reports from the Fortune 500.

NPS Benchmark variance between industries

Let’s take a look at some examples of net promoter score benchmarks according to your industry.

Let’s say you have an NPS of +50. As we explained, that’s already pretty good! But if you’re a department store or specialty store, you are actually below the NPS benchmark (+62) for the industry.

Walmart pharmacies have an NPS score of +32. Considering the highest score is +100, you’d guess that they’d be lukewarm with this score, but I’m sure that the folks in charge of customer experience there are actually ecstatic. Walmart pharmacies have one of the highest NPS scores within the drug store & pharmacy industry.

Compare this number to the software industry, where +34 is the average. Becoming a leader in the software industry would mean having an NPS in the +60 range, like Salesforce (+66) and Adobe (+62).

Caveats for using NPS industry benchmarks

Unfortunately, NPS benchmark programs aren’t always as helpful as you’d hope. This comes down to the nature of surveying for feedback. There are so many contributing factors to an NPS benchmark, such as:

• Which channels you use to survey customers
• Demographics and habits of your customer base
• Customer tolerance levels
• The size of your competition
• The difficulty of building brand loyalty
• External circumstances (such as a global pandemic)
• When and how often you ask
• Whether you have enough data to be statistically significant or not

All of these factors can have varying effects on your overall NPS score. For example, your competitor may ask the NPS question within the context of a longer annual brand survey, while you survey using just the NPS question after a transaction. These will have different consequences for the feedback you gather. If you don’t have enough feedback coming in, your NPS may vary significantly from quarter to quarter or month to month.

Bear in mind, a ‘good NPS score’ doesn’t just depend on your industry, since it’s not difficult to game the system. It’s not always fair to compare your NPS score to another company’s NPS score because you don’t know their survey methods, or their employee compensation plans.

When competitive individuals are incentivized based on NPS score, things can get ugly.

A motivated person or company could improve their numbers by letting their customers know that positive feedback would mean a lot to them or by only showing the survey to customers who are positively inclined. They might offer incentives to customers to complete the survey. Clearly, the feedback received from these methods will lead to an inflated NPS score that is not a useful comparison for those using a more objective survey process.  

Setting an NPS goal if you don’t have a benchmark

If no Net Promoter Score benchmark exists for your industry, benchmark against yourself.

The great thing about NPS is that it is an actionable metric. It’s a number that you can rally the company around as a north star to guide improvement efforts.

“A good NPS score is one that is better than the last.”
– Jessica Pfeifer, CCO & Co-founder of Wootric

Remember, NPS isn’t just a score. It’s a system that’s meant to drive business improvement in product and customer experience. It helps you identify and close the loop with unhappy customers and solve their specific problems in real time.

Your goal is to boost customer loyalty and retention, and that happens by reading verbatim comments to understand the why behind the scores you receive. By making changes based on customer feedback, and responding quickly to detractors, you will naturally see your NPS improve. And gains in NPS correlate with revenue growth.

How to report NPS

After all this, you will want to report numbers to the rest of the team on a regular basis. NPS should be shared along with other monthly or quarterly metrics like revenue, new customers and customer churn.

We understand that, so here’s what we recommend:

• Instead of fixating on your score in the absolute sense, we recommend focusing on improving your score over time. Understand NPS as a trend over several periods, like if you were looking at a stock’s price.
• Determine the business goals of your NPS program, then report NPS in relation to the goals. For instance, if you are trying to improve retention, report NPS alongside churn data.
• Pay attention to trending topics in your verbatim responses. Reporting these topics will help everyone understand what’s important to your customers, and the pain points they experience. Share what customers love and what they don’t love about your company with internal stakeholders. Then you can work to make those points as frictionless as possible. 

Note: For startups, be sure to read and respond to every single comment. As you grow, you’ll start needing aggregate and to pull themes from customer comments. To automate that process, check out AI-powered text and sentiment analysis.

• Segment your Net Promoter Score by relevant customer groups. For example, this could be by user role (in the SaaS example above), geography, or size/frequency of purchase–whatever drives your business. This will help you pay close attention to groups that are critical to your business success. Learn more about segmentation here.
  
• If you want to compare your score to a competitor, choose a company in your industry that you admire and use their score as an aspirational benchmark. Many companies have volunteered their NPS scores to research and reports such as this one by the Fortune 500.

Measure NPS and work to improve it over time.  Dig into customer comments and close the loop with customers. You will learn their needs, and their pain points, and have plenty of guidance to make those improvements. Both your NPS and your customer retention rates are sure to improve. 

What is Empathy?

Empathy is the ability to understand and share the feelings of another. For Product Managers looking to improve customer experience (CX), that definition translates to doing more than understanding the user’s pain points, but also looking at the emotional landscape of what it’s like to use the product – when it is working, and when it isn’t working.

Empathetic Product Managers ask themselves:

• How does using the product make the customer feel?
• How does the customer want to feel when using your product? What would be the best possible emotional outcome for them?
• How do I ensure the product developers understand and take the customers’ needs into consideration in their process?

The answers to those questions affect every facet of business, from acquisition to retention. It’s how, through CX, you can generate rapid growth through word-of-mouth recommendations, and sustain your success with customers who never want to leave.

Tying Empathy into CX

Empathy is a soft skill, and while those are typically difficult to measure, the effects of empathetic product development can be seen in every CX metric: Customer satisfaction (CSAT), Customer Effort Score (CES) and Net Promoter Score (NPS).

Sinead Cochrane, Senior Product Researcher at Intercom, wrote “For product teams, empathy building activities such as observing research or doing customer support is often not considered ‘real work’. However, product teams that consistently keep customer needs in mind are able to maintain and evolve their products in ways that won’t negatively impact the user experience.”

For her, empathy for product departments means “When a customer tells you something is broken, you are able to imagine the impact it’s having on the job they’re trying to get done,” and, “you realize the emotional impact the problem is having on that person.”

But I think we can go further than just recognizing the emotional impact of problems. That’s scratching the surface of what having empathy for customers can mean for producing superior customer experience.

Because empathy shouldn’t be reduced to realizing customers feel bad when a product isn’t working for them. A whole new world opens up when you also consider how you can design your product, updates, and expansions to enhance positive emotions as well.

Here are the questions that lie at the heart of empathetic product management:

• How can you get more emotionally in sync with your customers?
• Which are the most important negative emotional outcomes to manage?
• Which emotions should you seek to heighten (and how?)

To answer these questions, try these empathy building exercises.

Empathy Building Exercises for Product Managers and their Teams

1. Listen actively to discover underlying needs and emotional motivations

Often relegated to customer service and customer success departments, ‘active listening’ to find out why your customers use your product and what they really want to achieve is very important. You can’t get the depth and honesty of answers by just sending out a survey – this works much better if you do phone, Zoom or in-person interviews. In fact, Roman Pichler recommends product managers meet real users on a regular basis. You may find that your assumptions of why customers use your product aren’t accurate, or don’t tell nearly enough of the story.

“At first, our assumption was that they wanted to make more money. That often was true, but frequently we heard something different. Many simply wanted to maintain the business but run it more efficiently so they could have more free time (we heard about golfing on Fridays more than once). Others wanted to build a sustainable business they could pass on to their son or daughter.” – Jim Semick, Founder & Chief Strategist at ProductPlan

To get down to customers’ real motivations, ask open-ended questions beginning with “why” and “how.” Then make sure to record their answers in their own words (you can hand those assets to your copywriters for later use).

2. Use your own product

Empathy is often described as ‘putting yourself in someone else’s shoes’ – and there’s no better way to do this for a product manager than to actually use the product, just like any user would. You’ll empathize with users’ frustrations as you experience your own product’s shortcomings and hopefully find moments where it’s possible to create more delight.

But always keep in mind – you are not the average user. You’ll still need to listen to your users to get a complete picture of how they feel, and what problems they perceive as being severely aggravating.

3. Share verbatim comments

Someone, somewhere, is tracking customer experience metrics, sending out surveys, and collecting the answers. That someone might even be you. When reading users’ written responses, don’t just look for problems to solve and ignore the positive comments. Read them for emotion and see what conclusions you can draw about what people are feeling, and want to feel.

Pick a few relevant verbatim comments to bring to the rest of the product team. Reading these comments often helps engineers and designers feel the same joy or frustration as their users. This new emotional understanding will help you evangelize CX as a priority with everyone.

4. Mine your qualitative data and quantify customer sentiment

Those open-ended response answers are a goldmine for user research that can alert you to problems – and give you hints into the customer’s emotional state of mind. However, once you are getting more than a hundred comments a month, seeing the forest for the trees can be a difficult exercise. Qualitative feedback is notoriously tough to quantify, but it is now possible and easy to quantify sentiment with the help of machine learning.

AI-powered platforms, like InMoment CXInsight™, automatically sort your customer comments into themes while simultaneously assigning positive or negative sentiment. This provides you with a big picture understanding of how customers feel about your product and why. Categories of feedback vary by business sector and business model–payment processes & delivery for e-commerce, perhaps, while UX and usability may surface for SaaS products. Quantifying the sentiment of what your customers are talking about can help you track emotional trends over time. Presenting this kind of data alongside verbatim comments connects customer emotion with real business consequence.  

5. Set empathy KPIs

What gets measured gets done, and adding empathy into your product development work is no different. The KPIs for empathy may look a little different than your typical performance indicators, but the good news is: They’re not difficult to get. You’ll find key performance indicators like NPS, CES and CSAT are a good start, and comments in the open-ended questions can give you insight into the metric. Start identifying what kinds of ratings and qualitative answers correlate to genuinely happy customers – and frustrated customers likely to churn.

6. Chart out an empathy map

You’ve done your user journey, but even though it’s part of the buyer persona building process, you may not have done an empathy map.

• What your user sees – on competitors’ websites, common visuals in their industries, maybe what they enjoy watching or reading
• What your user says – how they measure success, what they say they want, what they say about your product
• What your user hears – what their influencers are saying, not just about your product, but about their jobs and what constitutes success, what they enjoy, what they don’t like about their experiences with your competitors, etc.
• What your users think and feel – worries, aspirations, what they really want, what really annoys them

Notice how the empathy map includes business/industry-specific observations, but also branches out into the user’s personal life and larger environment. People are not their jobs – or even their ‘jobs to be done.’ For true empathy, you have to look at the whole person.

This is a great activity to get other teams involved in – consider hosting a meeting with Customer Success, Sales, Marketing and Customer Service for a wider scope of insights.

Activities involving multiple teams help to build a shared understanding of your customers’ experiences that can strengthen the whole company.

7. Add happy moments to your Customer Journey Map

You’ve probably mapped out your customer/user journey, but you probably didn’t include this: Happy moments. See if you can take your old customer journey map and mark the points where positive, fun, delightful things happen. Can’t think of any? Then you have some serious CX work to do!

And of course, also note points where you’ve observed friction, difficulties, and problems, and address those in the order of biggest impact + easiest to implement.

8. Work in Customer Support for an afternoon

Whether that means answering the live chat questions, picking up the phone, or monitoring your product’s customer Slack channel, try out being the Customer Support agent for an afternoon to and put yourself on the front lines! There’s no better way to find problems than to let customers tell you exactly – and in great detail – what they are. And they’ll likely throw in how frustrated it makes them feel too.

9. Build a prototype to test your emotional hypotheses

By now, you probably have a few ideas on how you can improve the customer experience, and it might be time to test those theories. Create a prototype for a select group of qualified users to try (and react to). And, if possible, have them test the prototype in a testing facility that allows you to observe their reactions as they use your product.

If there is a Golden Rule for empathy, it’s a simple one: Forget your assumptions and be genuinely interested and curious about what people are feeling (not just what they’re doing) while using your product. Empathy is a learned skill that needs practice so don’t forget to try out these empathy exercises on a frequent basis for enhanced customer experience.  

To quote Maya Angelou: “People will forget what you said, people will forget what you did, but people will never forget how you made them feel.”

Data is the beating pulse of business, but customer data is more like DNA. Customer data, if we’re using it right, directs how we grow and what we develop. But what happens if that customer data becomes corrupted by our own bias?

We can’t grow or develop in the ways we need to.

But what is bias exactly? Where does it come from?

The most prevalent bias is, perhaps, confirmation bias – seeking out data that confirms our existing beliefs.

In an early study of confirmation bias, young children were asked what features in a sports ball are important to the quality of a player’s serve. Some said size, others said material, some dismissed color as a factor – but once they’d made up their minds, they failed to acknowledge evidence that was contrary to their theory – or explained away evidence that didn’t fit.

But what’s worse, especially for those of us using data to steer our businesses, is that confirmation bias caused them to not generate alternate theories unless someone asked them to. They missed exploring and finding other possibilities.

There are other types of bias too, including:

Algorithmic bias – When the data used to teach an AI machine learning system reflects the implicit values of the humans involved in collecting, selecting and using that data. You might remember the 2015 uproar around Google’s image recognition AI algorithm that auto-tagged photos of black people as gorillas? Yes, that happened. And in 2009, Nikon’s image recognition algorithms consistently asked Asian users if they were blinking.

Survivorship bias – When the data analyzed only comes from success stories.

Sample bias – When the population you collect data from doesn’t accurately reflect the population you’re trying to learn about.

Avoiding bias when gathering, analyzing and acting on data is impossible. Bias creeps in with assumptions, instincts, guesses, and ‘logical’ conclusions – and mostly, we don’t even know they exist until someone without those particular biases point them out.

But, while we can’t escape biases, we can try our best to account for them when we collect, analyze and interpret data.

How to fight bias in your data

In Forrester’s The illusion of insights recording, Forrester Vice President and Research Director Sri Sridharan makes three recommendations to reduce bias in data.

She says to “triangulate insight” by using multiple methods of arriving at an insight, and cross-validation. For example, pairing behavioral data with feedback from customer surveys to see if you arrive at the same or similar answers.

Her second piece of advice is to create a “self-correcting system of insights” that connects customer data with an effective action to create a closed loop of action, learning, and optimization. Essentially, this means testing the data by taking action and iterating based on how well you succeed in addressing the issue.

Tracking a ‘North Star’ metric like NPS or CSAT over time can be very helpful in confirming whether the changes you make are having the desired effect.

Sri’s third piece of advice is to “show your work to build trust” both internally and with customers. Your customers will be quick to correct you if your insights don’t hold true for them – and you have the bonus of showing them how hard you’re working to make sure they have what they need to succeed.

But there is also the potential for bias to happen before any of these fixes can be made – especially in Customer Discovery.

Bias in Customer Discovery, Before You’ve Even Gotten to the Data

Bias in whom you ask

Who you survey, interview or meet with can bias your results. This is called “sample bias” – but it can also turn into confirmation bias. Sample bias happens when some members of the intended population are less likely to be included than others. Think of all of the different segments of users you have – what would happen if you only surveyed one of those segments? You would get responses that don’t work equally well for all of your customers.

This can slide into confirmation bias if the population you select is more likely to give you the answers you want to hear.

And, there’s also the risk of “survivorship bias,” if the people you’re surveying are the customers who are still with you, rather than the users who have churned. Current users are much easier to collect data from, and while they can give you important insights, they can’t tell you why your churned customers left.

Bias in how you ask

How you frame questions can have a dramatic effect on the responses. In fact, by the wording you use in a survey, or even your tone of voice in a phone interview or facial expressions in an in-person interview, you can effectively steer the conversation to deliver exactly the answers you’re hoping to hear. Many of the words we use have positive or negative associations that cause people to react accordingly.

Biased question: How much do you like the color blue? (This presupposes they like the color blue at all)

Unbiased question: How does the color blue make you feel? (A much more neutral phrasing)

Or, if you aren’t specific enough about the information you want, you risk confusing your respondent and getting answers that aren’t at all helpful. Unless you have a professional market researcher on staff, you may want to stick with established questions like NPS and CSAT.

Bias in what you ask first – and last

The order of the questions you ask can also bias your results, and you’ll need to review your question order carefully to make sure the sequence doesn’t cause biased responses. Typically, you should ask general questions before specific ones, ask positive questions before negative ones, and ask questions about behavior before questions about attitude.

Bias in when you ask

Holidays and the summer months, when families often take their vacations, can be problematic for both response rates and sample bias. For example, if you send a survey during religious holidays, you’ll likely get different responses rates from different groups of people, who may or may not be taking that time off.  Be aware of your timing, including if you’re sending surveys during deadline rushes, before or after holidays, or other significant patterns that may affect who responds and how they respond. To be on the safe side, don’t send your survey at the same time every year – send a few, at different times, to get the most accurate feedback.

Objective Data Leads to More Accurate, More Valuable Insight

Sherlock Holmes famously tells Dr. Watson that he never forms a theory before he gathers all of the facts. But he’s much better at disassociating himself from the results than most of us are (and he’s fictional). Bias has a way of seeping into our results, and how we view and react to our results. But, when we put bias-countering measures in place, like gathering data from different sources, using different types of data, and checking our work through a process of action and iteration, we can get to the truth in the end.