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Making data-driven business decisions nowadays is both easier than ever before and harder than ever before. Easier because we have a wealth of data at our disposal - including those about our customers and how happy they are with our service. But harder because companies are regularly overwhelmed with all those types of numbers, sets, variables, metrics, and approaches. It’s simply not that easy to crunch the infinite sets of data and do something valuable with them.
So in the following post, we’ll tell you more about how you can measure client satisfaction the right way. We’ll also mention:
If it was a regular blog post, I would go with quotations that the world doubled data volume in the last year or something like that. I tried doing something a bit different and typed into Google: “metrics in startups”. Results? 24.5 million (!) search queries.
It’s a lot to handle. Some of them are essential, and you can’t do much without those (e.g., MRR, ARR, or CLTV), but if you did everything right, they’re already in the process. Usually, we have them embedded in the systems, and pulling them out is not a problem. Let’s call them “owned” types of data. We’re not going into them today (and Philip is much more of an expert in it than I am btw.).
What you need to remember is that they are numerical, quantitative, and it’s easy to calculate them, but much harder to interpret.
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The real challenge starts when you want to perform more “informative” analysis, e.g., surveys or customer feedback comments, customer interviews, and focus groups. To simplify, let’s name them as “gathered.” These are hard to gather and analyze but relatively easy to interpret.
From my experience, the majority of people don’t know how to create a proper survey and conduct research. There is a common misconception that survey is easy-to-do, and pretty much everyone can create it. I’ve seen hundreds of badly written questions. Some of them suggest an answer, others mess up the scale, or asks the wrong thing to the responder.
Surveys are the tip of the iceberg. For instance, we also have string variables with vast amounts of text data, which are hard to assess, and at least they’re time-consuming. Natural language programming is still developing, so we can’t rely on any type of software as well (trust me, I’ve tried them).
If I am not knowledgeable about research, then I’m doomed to perform the right type of analysis? The answer is: yes, but there is a turnaround here. Top-tier consulting firms call it an 80/20 approach. You may think of it as the Pareto rule - you need 20% of the data to have 80% of the results.
There are multiple ways of how-to-handle quick analyses, but today let’s focus on the one I’ve recently developed, which is a combination of Net Promoter Score and KCD questions (What to Keep/Change/Delete)?
You’ve probably heard of Net Promoter Score. If yes, move to the next paragraph, if not, here you go.
The NPS is a 0-10 scale question you can ask your customers:
„How likely is that you would recommend X to a friend or colleague?”.
Then answers:
You calculate it in an equation: % of “Promoters” in the sample - % of “Detractors”; you need to skip the “Passives.”
Promoters” are the ones you need to grow -they are the loyal ones, will remain customers, and you may cross/up-sell them.
“Passives” you need to convert into loyal, boost their satisfaction, and try to satisfy them more as they can smoothly go to the competition.
“Detractors” are unhappy customers who have high churn and definitely will tell a lot of lousy word-of-mouth stuff, you can try to fix it, but it will be hard.
The score goes from -100 to +100. Everything positive is good, but everything +50 is incredible. You can check top companies' NPS for free here.
Two-thirds of Fortune 1000 companies applied it in their daily operations, so it might be useful to know it. London School of Economics scientists even discovered a statistically significant correlation between revenue growth and companies’ NPS scoring as according to the research, an NPS increase by 7 points correlates with 1% growth in revenue.
Now let’s move to the 2nd point.
I frequently have classes with university students. If you want to excel as a teacher, you need to evaluate your work and try harder. So I’ve asked my students to rate me on an NPS scale and asked them three feedback questions:
Simple as that, I immediately got four relevant data points to check the hypotheses:
Now, I have one quantitative metric, which I separate into three buckets and three questions for three separate groups: Promoters, Passives, and Detractors.
I had classes with two separate groups of students. If I had calculated the quick survey results with an average, I would score 9.62 for the first and 9.00 for the second one. It’s not a statistically significant difference to start thinking about it, but what if we take the NPS scale?
In the first group, my NPS was “+93,” and in the second, it was only “+70”. It was still terrific, but the difference was already visible.
I started to think, also looking at qualitative Keep/Change/Delete answers. Some students in the 2nd group mentioned I should be “more to the point,” “keep the tempo”, “be more focused.” It got my mind, so I began to A/B differentiate the groups and found out that the variable, which had an impact on opinions, was “time of classes.” One class was earlier than the other.
Simple as that, I realized that I was more tired and less focused and productive as time went by. I was too engaged in the first group, using about 70% of my energy, and didn’t have it for the second one. It was game-changing for me and the way I teach classes right now. Nowadays, I try to be calmer, slowed down, and more balanced so I don’t exhaust too fast and save energy for later. Overall, teaching is also like long-distance running.
I have done this analysis after a few pieces of training I’ve conducted; one can use it as a feedback question on retrospective team sessions; your customer success manager can ask it directly to the client. I keep on asking these questions to my clients, students, and even co-workers. It’s mighty, considering how simple it is.
This type of quant/qual analysis can be improved, changed, and tailored for your needs. The only thing you need to remember is the pattern: foundation (quantitative) question + informative deep-dive (qualitative). It gives you excellent 80/20 coverage, which you can easily leverage in the future. It is much more insightful than just a standard Net Promoter Score, which many companies heavily use.
As you see, there is a considerable difference between the average opinions and those calculated with NPS. Standard calculations wouldn’t be right to see the hidden patterns. Applying quantitative metrics + qualitative insights is a potential way to go for every business but also a performance-focused person.
If you aim for the “100” in your NPS scale, you may achieve loyal customers, satisfied employees, and an engaged audience. If there are any deviations from the top score, you can always deep-dive and see the reasons.
It’s an easy hack for those who are not experts in survey-typed questions or don’t have time to focus on that while growing their business. You can even put a threshold: “if it’s lower than X, I’m going to investigate.” I love rules-of-thumb, and in this case, it’s the way to do it!
I hope this helps you. Please shoot me an e-mail at maciejwilczynski24@gmail.com or comment if you like to discuss or ask for anything.
Bonus: Questions to think.
There are a number of ways in which you can do it, but the most useful way is by gathering and analyzing feedback from customers after they use a product or service. You can do this through running surveys, reading reviews, or asking them directly for feedback. Analyzing these responses helps understand how satisfied customers are.
Net Promoter Score (NPS) is a very simple way in which you can measure customer satisfaction and loyalty. Customers are asked how likely they are to recommend a product or service to others, and based on their answers, they are grouped as “Detractors”, “Passives,” or “Promoters.” To get your score, you need to subtract the percentage of detractors (unhappy customers) from the percentage of promoters (satisfied customers).
Measuring and analyzing their customer satisfaction levels helps businesses understand how well they're meeting customer needs and are there things that are making their customers frustrated or upset. Satisfied customers are more likely to become repeat customers and recommend the business to others, so by boosting their satisfaction, businesses can improve their own revenue.
Customer satisfaction surveys are questionnaires that ask customers about their experiences. You now quickly create those by using online survey tools - just remember to keep the questions tailored to your business. Make the surveys short and easy to understand - otherwise customers might not be eager to answer those.
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Expert in B2B pricing, monetization and value-based selling strategies. Over the past year, he has completed over 40 consulting projects in Europe. Prior to founding Valueships, he worked at McKinsey & Company, mainly in the TelCo, software, and banking industries. He completed his doctorate in pricing in SaaS start-ups at the University of Economics in Wrocław, where he also lectures.
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Expert in B2B pricing, monetization and value-based selling strategies. Over the past year, he has completed over 40 consulting projects in Europe. Prior to founding Valueships, he worked at McKinsey & Company, mainly in the TelCo, software, and banking industries. He completed his doctorate in pricing in SaaS start-ups at the University of Economics in Wrocław, where he also lectures.
