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Author: Mengying Li
_Mengying Li_
Reading time: 22 minutes
Synopsis
The book Growth Data Analytics Playbook (2025) gives you new ways to use data. This helps you make decisions based on facts, not just feelings, when you want your products to grow. You will learn how to measure if people really want your product by seeing if they keep using it. You will also find out who your most important users are. This guide shows you how to make your product grow steadily. It helps you look past numbers that just look good and build systems that make your product grow on its own for a long time.
What’s in it for me? Change your feelings into a clear plan for growth.
So, you did it – you made a product that people really like. At the start, you used your strong feelings and talked directly with customers. You even knew every customer’s name. But as your product gets bigger, you can’t talk to everyone face-to-face anymore. It becomes harder to know what users want. You are trying to grow a feeling, trying to keep that personal connection when you can no longer look your customers in the eye.
In this Blink, you will learn how to use data to connect your feelings with growing big. You will learn to look deeper than simple numbers. You will see why people stay, why they leave, and why they tell others about your product. These ideas will help you stop just reacting to problems. Instead, you can plan how to grow, with a clearer idea of how your product is doing and what to do next.
Let’s start.
Blink 1 – Using data instead of feelings and checking if your product is good.
Imagine you have a friend who spent seven years learning to be a wine expert. Only a few hundred people in the world are this good. When she works in a restaurant, she has a special skill. She can look at your clothes, listen to how you speak, guess your mood, and suggest the perfect wine. She knows what you want before you do. It feels like magic because she understands customers so well.
But here is the sad part: when this same expert tries to sell wine online, that special skill disappears. The screen breaks her personal connection. She cannot guess what people want anymore.
This is the exact problem you face when you grow a product. In the beginning, you use your founder’s intuition. This is a gut feeling about who your users are. But as you grow, you lose that close feeling. You cannot look every user in the eye. To keep going, you have to replace that feeling with data.
This brings us to the idea of a North Star metric. This is one single number that shows you the way. A true North Star metric does not just count things that look good, like how many people signed up or clicked. These numbers can be misleading. Instead, it measures what real value users get. Think of a tool for working together. A simple, not very useful number would be how many people signed up. But a North Star metric would be “number of teams working together daily.” This shows that people are truly using your product as you hoped.
Now, once you have your guide, you need to check if people really want what you are building. This is called Product Market Fit, or PMF. And there is one hard way to measure it: retention. This means, do users come back after they use your product for the first time?
When you draw a graph of active users over time, you get a retention curve. In a bad situation, this line goes down until it hits zero. This means you are losing customers faster than you get new ones. But in a good product, something different happens. The line goes down a little first, then stays flat, or even goes up. This is called a “smiling curve.” That smile is the sign of a successful business. It means you have found a main group of users who find your product very important.
The problem with retention is that it tells you about the past. It is like driving while only looking behind you. By the time you know users are not coming back after six months, it is often too late. To plan for growth early, you need early signs. These are specific things users do early on that show they will stay for a long time. For a video app, this might mean that if a user watches more than ten minutes in their first week, they are very likely to keep using it. Find that best moment – the exact time a person who uses it sometimes becomes a regular user. Then you can help other users do the same.
Blink 2 – How to count your growth carefully.
Great – now you know which early signs predict who will probably stay. You are probably feeling ready to go fast. But many teams get surprised here.
You might see your total user count grow month after month and think everything is going perfectly. Then, six months later, you realize it was all built on sand. The problem? Most teams think of growth like a slot machine. They put in money for marketing, pull the lever, and get users out. What you really need is like a bank statement. It tracks every movement carefully.
Think of it like this: a bank manager would never just look at a bank balance and finish for the day. They need to know where every cent came from. The same idea applies to your users. You need a record book. And at the center of that book is one simple-looking equation: net growth = new users + resurrected users – churned users.
It is almost like a bucket filling with water. New users are the tap flowing in at the top. Churned users are the leak at the bottom. If you open that tap wide – spending a lot of money to get new users – the water level rises. This happens even if there is a big hole underneath. Your net growth looks fantastic. Everyone is happy. But the moment you spend less on marketing, that bucket drains right out. Counting growth this way forces you to look directly at that hole. It helps you see the difference between real growth and growth you are simply paying for.
For our equation to work, every user must fit into one of four groups for the time you are measuring. There are the new users, who use your product for the first time. The retained users are your main, healthy users who used it last time and came back again. The churned users were active before but have now stopped using it. And then there is a group that is often forgotten: the resurrected user. This is someone who left at some point but has come back. This difference matters a lot because these groups need completely different things. Some need to be introduced; others need a reason to believe something has changed.
Now, how accurate all of this is depends on how you divide time. A common mistake is using fixed calendar months. For example, comparing who was active on January 1st to who was active on February 1st. Real human behavior does not follow these dates. Someone might sign up January 25th and vanish by February 5th. A fixed monthly look misses that completely.
The fix? Use moving, overlapping time periods. Compare January 1st through 30th with January 2nd through 31st. This captures how your product is used every day. It records every change – from active to stopped, from inactive to back again – right when it happens.
With this plan, you stop chasing numbers that just look good. You can actually find out what is really happening. Is your product truly growing, or are you just wasting money without making real progress?
Blink 3 – How often people use your product and who your best users are.
Great – now your record book tracks how many users are coming and going. But here is the problem: Who exactly are these people? Are they like tourists just looking around quickly, or like people living in your digital city? Counting growth tells you the total number of people, but nothing about how lively the city is. To understand that, you need to measure how much people use your product. You need to focus on how deeply they engage, not just how many there are.
The fastest way to measure this is through stickiness. This is the DAU/MAU ratio. It measures how much your product attracts people. It asks, Of all the people who visited in the last month, what percentage used it today? If you have a thousand monthly users but only a hundred use it daily, your ratio is 10 percent. You have many users, but it is not a daily habit for them. If you get that to 50 percent – half your monthly users coming back every single day – you have reached the top level of engagement. This is true for big social media apps and messaging tools. Your product has become something people need every day.
Stickiness gives you a general idea, but it can sometimes be too simple. It treats a user who visits once a week the same as one who visits every other day. Both are “active,” but their behavior is very different. That is where the L-ness system comes in. Instead of just saying “active” or “inactive,” L-ness measures the exact number of days a user used the product in a certain time, usually the last 30 days. An “L30” score of five means someone uses it sometimes. An “L30” of 25 or higher shows something totally different: these are your “power users.” These are people who have made your product a part of their daily life.
Finding these power users is important for your plan because of the power law. Studies on how people use products often show that the top 10 percent of users account for almost 90 percent of all use. These users are like the heart of your product. They try out all your features, create content that keeps others interested, and tell new users about your product. If you lose them, you lose a main support for your product.
Once you have found this important small group, your goal changes. You go from just watching them to helping others do the same. Become a behavior detective. Look back to understand how they became so committed. What did these power users do in their first week that others did not? Did they connect with five friends right away? Did they upload a profile picture on day one? Did they finish a specific guide? By understanding the “DNA” of their early journey, you can change how new users start using your product. This will guide them down the same path.
Stop hoping people will find value. Instead, start guiding them with your product’s design towards specific actions. These actions lead to deep, lasting use. In this way, you will make more of your best customers. You will turn their lucky success into a way that works for everyone else.
Blink 4 – Make your product stronger.
Even if you have more power users and your digital city is full of active people, a quiet danger can appear. No matter how much people like your product, things eventually change. Users stop using it, credit cards expire, interests change. If attracting users is your attack, you now need a strong defense.
That starts with understanding churn correctly. It is easy to think of churn as just one number – users leaving. But that is too simple and dangerous. There is the user who stops logging in – usage churn. And there is the user who actively cancels – payment churn. Usage churn is a slow goodbye, like a relationship slowly ending. Payment churn is like a breakup letter. The difference matters because each needs a different answer.
The thing is, by the time churn shows up in your reports, the harm is already done. So you need an early warning system. These are specific actions that show a user is “at risk” weeks before they actually leave. In one AI chat app, experts found that users who had many technical errors in their first week were much more likely to stop using the app later. Once they found this sign, the team could act right away with automatic apologies and extra credits. They fixed the problem before the user even decided to leave. This changes you from fixing problems after they happen to taking care of users before problems get big.
Now, remember what we talked about regarding engagement in earlier parts? That same idea applies here, but in reverse. You are treating signs of unhappiness before they become serious.
Still, some users will leave. This brings us to getting users back – and this is where your feelings often lead you to make mistakes. Sending many “We miss you!” emails to users who have stopped usually does not work. It often makes them unsubscribe for good. The “needy ex” approach rarely gets anyone back.
Here is what does work: silence. Studies from big tech companies, like Facebook, found something surprising. Sending fewer messages actually makes users happier and keeps them using the product longer. When you send fewer alerts, each one feels more important. Instead of general requests, you wait for something truly important. This could be a close friend’s photo, or an update to the product that matches their interests. You treat their attention as something rare. You earn the right to invite them back instead of demanding it.
Finally, all this defense is directly linked to your money. Keeping users protects your income. Two numbers show this best: Gross Revenue Retention, or GRR, and Net Revenue Retention, or NRR. GRR shows how much money you kept from existing customers. It does not include new sales to them. This measures how stable your business is. NRR is the best one to aim for. It measures money kept, including upgrades and extra sales. If your NRR is more than 100 percent, your business grows only through existing customers, even if you do not get new ones.
That is the best defense: a group of customers who not only stay but also bring more value over time. This makes your growth stronger and helps your business stay strong no matter what happens next.
Blink 5 – How your product works, how fast it is, and how it grows on its own.
So you have built your defenses, and your money-making system is working well. You might feel safe. But in the digital world, being safe is not real if you are not moving. The last part of growing your product is about speed. You need to move faster than others, and your product needs to respond quickly to users.
We often think of speed as just a technical thing, something for engineers to do. But speed is actually a feature that helps keep users. When you look at data about how users act, there is an “abandonment curve.” This curve tells a harsh story: users’ patience does not slowly decrease – it drops very quickly. Studies show that many users leave after just three seconds of delay. If your app takes four seconds to load, half your users have already left before they even see what your product does.
Speed also matters for how fast you learn. This is where human feelings are not enough. Systems are complex, and what happens after product decisions is rarely simple. Think about DDT. In the 1940s, it was seen as an amazing bug killer. Nobody realized it would go into rivers, be eaten by fish, and then gather in eagles. This made their eggshells weak until they broke during hatching. That hidden chain of cause and effect is exactly what happens with product features. About 80 percent of new ideas do not give the results people expect.
What is the way to solve this? Testing things often and in a big way. Testing must become the normal way of working. The problem is usually the cost – it feels too risky to test everything. But the solution here is feature flags. They let you separate writing code from making a feature public. This means you can send the code to your live product. But you keep it hidden from everyone except, for example, a random 5 percent of users. Every update becomes a controlled test. Instead of arguing if a new checkout process works, you turn it on for a small group. You measure its effect, and let the data decide. If something breaks, you turn it off instantly.
Once you have made your speed and learning processes better, you can stop thinking about growth in a simple, straight line. Most people imagine growth like a funnel: you pour in new leads at the top, and customers come out at the bottom. But funnels are tiring. The moment you stop pouring, growth stops.
The most successful companies build flywheels instead. Imagine a very big, heavy wheel. The first push takes a lot of effort. But as it turns, it gets faster. Eventually, it spins by itself. In practice, this means creating systems where what happens in one step helps the next step. Take the “creator flywheel” on video platforms. A creator posts a video. That brings in a viewer. The viewer leaves a comment. That positive feedback makes the creator want to post more. The system keeps itself going.
Your job as a leader in growth? Find the things that are slowing things down and make them run smoothly. When you combine this self-sustaining power with a clear main goal, careful counting, and quick testing, you build something that keeps growing on its own.
Final summary
This Blink of Growth Data Analytics Playbook by Mengying Li, Joe Kumar, and Yuzheng Sun, shows that steady product growth does not come from clever marketing tricks. It comes from a careful system of counting, where keeping users is the best way to know if your product is truly valuable.
You found out that relying only on feelings does not work as you grow. This is why data-driven North Star metrics are important. They help teams work together to give real value to users. By looking at growth as a record of new, returned, and lost users, you can see past numbers that just look good. You can understand the real health of your product’s world. You also learned how finding your most active users helps you understand their habits. This creates a plan that guides people who use your product sometimes towards using it deeply. And you saw how defensive plans, like predicting when users might leave, combine with offensive plans, like testing things often. This changes simple growth funnels into self-sustaining flywheels – the kind that build their own speed.
Okay, that’s it for this Blink. We hope you enjoyed it. If you can, please take the time to leave us a rating – we always appreciate your feedback. See you soon.
Source: https://www.blinkist.com/https://www.blinkist.com/en/books/growth-data-analytics-playbook-en