Why I quit a $500K job at Amazon to work for myself
I quit my 8-year Amazon job last week. I wasn't motivated to do another year despite promotions, pay, recognition, and praise.
In AWS, I built developer tools. I could have worked in that field forever.
I became an Amazon developer. Within 3.5 years, I was promoted twice to senior engineer and would have been promoted to principal engineer if I stayed. The company said I had great potential.
Over time, I became a reputed expert and leader within the company. I was respected.
First year I made $75K, last year $511K. If I stayed another two years, I could have made $1M.
Despite Amazon's reputation, my work–life balance was good. I no longer needed to prove myself and could do everything in 40 hours a week. My team worked from home once a week, and I rarely opened my laptop nights or weekends.
My coworkers were great. I had three generous, empathetic managers. I’m very grateful to everyone I worked with.
Everything was going well and getting better. My motivation to go to work each morning was declining despite my career and income growth.
Another promotion, pay raise, or big project wouldn't have boosted my motivation. Motivation was also waning. It was my freedom.
Demotivation
My motivation was high in the beginning. I worked with someone on an internal tool with little scrutiny. I had more freedom to choose how and what to work on than in recent years. Me and another person improved it, talked to users, released updates, and tested it. Whatever we wanted, we did. We did our best and were mostly self-directed.
In recent years, things have changed. My department's most important project had many stakeholders and complex goals. What I could do depended on my ability to convince others it was the best way to achieve our goals.
Amazon was always someone else's terms. The terms started out simple (keep fixing it), but became more complex over time (maximize all goals; satisfy all stakeholders). Working in a large organization imposed restrictions on how to do the work, what to do, what goals to set, and what business to pursue. This situation forced me to do things I didn't want to do.
Finding New Motivation
What would I do forever? Not something I did until I reached a milestone (an exit), but something I'd do until I'm 80. What could I do for the next 45 years that would make me excited to wake up and pay my bills? Is that too unambitious? Nope. Because I'm motivated by two things.
One is an external carrot or stick. I'm not forced to file my taxes every April, but I do because I don't want to go to jail. Or I may not like something but do it anyway because I need to pay the bills or want a nice car. Extrinsic motivation
One is internal. When there's no carrot or stick, this motivates me. This fuels hobbies. I wanted a job that was intrinsically motivated.
Is this too low-key? Extrinsic motivation isn't sustainable. Getting promoted felt good for a week, then it was over. When I hit $100K, I admired my W2 for a few days, but then it wore off. Same thing happened at $200K, $300K, $400K, and $500K. Earning $1M or $10M wouldn't change anything. I feel the same about every material reward or possession. Getting them feels good at first, but quickly fades.
Things I've done since I was a kid, when no one forced me to, don't wear off. Coding, selling my creations, charting my own path, and being honest. Why not always use my strengths and motivation? I'm lucky to live in a time when I can work independently in my field without large investments. So that’s what I’m doing.
What’s Next?
I'm going all-in on independence and will make a living from scratch. I won't do only what I like, but on my terms. My goal is to cover my family's expenses before my savings run out while doing something I enjoy. What more could I want from my work?
You can now follow me on Twitter as I continue to document my journey.
This post is a summary. Read full article here
More on Personal Growth
Alex Mathers
3 years ago Draft
12 practices of the zenith individuals I know
Calmness is a vital life skill.
It aids communication. It boosts creativity and performance.
I've studied calm people's habits for years. Commonalities:
Have learned to laugh at themselves.
Those who have something to protect can’t help but make it a very serious business, which drains the energy out of the room.
They are fixated on positive pursuits like making cool things, building a strong physique, and having fun with others rather than on depressing influences like the news and gossip.
Every day, spend at least 20 minutes moving, whether it's walking, yoga, or lifting weights.
Discover ways to take pleasure in life's challenges.
Since perspective is malleable, they change their view.
Set your own needs first.
Stressed people neglect themselves and wonder why they struggle.
Prioritize self-care.
Don't ruin your life to please others.
Make something.
Calm people create more than react.
They love creating beautiful things—paintings, children, relationships, and projects.
Hold your breath, please.
If you're stressed or angry, you may be surprised how much time you spend holding your breath and tightening your belly.
Release, breathe, and relax to find calm.
Stopped rushing.
Rushing is disadvantageous.
Calm people handle life better.
Are attuned to their personal dietary needs.
They avoid junk food and eat foods that keep them healthy, happy, and calm.
Don’t take anything personally.
Stressed people control everything.
Self-conscious.
Calm people put others and their work first.
Keep their surroundings neat.
Maintaining an uplifting and clutter-free environment daily calms the mind.
Minimise negative people.
Calm people are ruthless with their boundaries and avoid negative and drama-prone people.
Zuzanna Sieja
3 years ago
In 2022, each data scientist needs to read these 11 books.
Non-technical talents can benefit data scientists in addition to statistics and programming.
As our article 5 Most In-Demand Skills for Data Scientists shows, being business-minded is useful. How can you get such a diverse skill set? We've compiled a list of helpful resources.
Data science, data analysis, programming, and business are covered. Even a few of these books will make you a better data scientist.
Ready? Let’s dive in.
Best books for data scientists
1. The Black Swan
Author: Nassim Taleb
First, a less obvious title. Nassim Nicholas Taleb's seminal series examines uncertainty, probability, risk, and decision-making.
Three characteristics define a black swan event:
It is erratic.
It has a significant impact.
Many times, people try to come up with an explanation that makes it seem more predictable than it actually was.
People formerly believed all swans were white because they'd never seen otherwise. A black swan in Australia shattered their belief.
Taleb uses this incident to illustrate how human thinking mistakes affect decision-making. The book teaches readers to be aware of unpredictability in the ever-changing IT business.
Try multiple tactics and models because you may find the answer.
2. High Output Management
Author: Andrew Grove
Intel's former chairman and CEO provides his insights on developing a global firm in this business book. We think Grove would choose “management” to describe the talent needed to start and run a business.
That's a skill for CEOs, techies, and data scientists. Grove writes on developing productive teams, motivation, real-life business scenarios, and revolutionizing work.
Five lessons:
Every action is a procedure.
Meetings are a medium of work
Manage short-term goals in accordance with long-term strategies.
Mission-oriented teams accelerate while functional teams increase leverage.
Utilize performance evaluations to enhance output.
So — if the above captures your imagination, it’s well worth getting stuck in.
3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers
Author: Ben Horowitz
Few realize how difficult it is to run a business, even though many see it as a tremendous opportunity.
Business schools don't teach managers how to handle the toughest difficulties; they're usually on their own. So Ben Horowitz wrote this book.
It gives tips on creating and maintaining a new firm and analyzes the hurdles CEOs face.
Find suggestions on:
create software
Run a business.
Promote a product
Obtain resources
Smart investment
oversee daily operations
This book will help you cope with tough times.
4. Obviously Awesome: How to Nail Product Positioning
Author: April Dunford
Your job as a data scientist is a product. You should be able to sell what you do to clients. Even if your product is great, you must convince them.
How to? April Dunford's advice: Her book explains how to connect with customers by making your offering seem like a secret sauce.
You'll learn:
Select the ideal market for your products.
Connect an audience to the value of your goods right away.
Take use of three positioning philosophies.
Utilize market trends to aid purchasers
5. The Mom test
Author: Rob Fitzpatrick
The Mom Test improves communication. Client conversations are rarely predictable. The book emphasizes one of the most important communication rules: enquire about specific prior behaviors.
Both ways work. If a client has suggestions or demands, listen carefully and ensure everyone understands. The book is packed with client-speaking tips.
6. Introduction to Machine Learning with Python: A Guide for Data Scientists
Authors: Andreas C. Müller, Sarah Guido
Now, technical documents.
This book is for Python-savvy data scientists who wish to learn machine learning. Authors explain how to use algorithms instead of math theory.
Their technique is ideal for developers who wish to study machine learning basics and use cases. Sci-kit-learn, NumPy, SciPy, pandas, and Jupyter Notebook are covered beyond Python.
If you know machine learning or artificial neural networks, skip this.
7. Python Data Science Handbook: Essential Tools for Working with Data
Author: Jake VanderPlas
Data work isn't easy. Data manipulation, transformation, cleansing, and visualization must be exact.
Python is a popular tool. The Python Data Science Handbook explains everything. The book describes how to utilize Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter for beginners.
The only thing missing is a way to apply your learnings.
8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Author: Wes McKinney
The author leads you through manipulating, processing, cleaning, and analyzing Python datasets using NumPy, Pandas, and IPython.
The book's realistic case studies make it a great resource for Python or scientific computing beginners. Once accomplished, you'll uncover online analytics, finance, social science, and economics solutions.
9. Data Science from Scratch
Author: Joel Grus
Here's a title for data scientists with Python, stats, maths, and algebra skills (alongside a grasp of algorithms and machine learning). You'll learn data science's essential libraries, frameworks, modules, and toolkits.
The author works through all the key principles, providing you with the practical abilities to develop simple code. The book is appropriate for intermediate programmers interested in data science and machine learning.
Not that prior knowledge is required. The writing style matches all experience levels, but understanding will help you absorb more.
10. Machine Learning Yearning
Author: Andrew Ng
Andrew Ng is a machine learning expert. Co-founded and teaches at Stanford. This free book shows you how to structure an ML project, including recognizing mistakes and building in complex contexts.
The book delivers knowledge and teaches how to apply it, so you'll know how to:
Determine the optimal course of action for your ML project.
Create software that is more effective than people.
Recognize when to use end-to-end, transfer, and multi-task learning, and how to do so.
Identifying machine learning system flaws
Ng writes easy-to-read books. No rigorous math theory; just a terrific approach to understanding how to make technical machine learning decisions.
11. Deep Learning with PyTorch Step-by-Step
Author: Daniel Voigt Godoy
The last title is also the most recent. The book was revised on 23 January 2022 to discuss Deep Learning and PyTorch, a Python coding tool.
It comprises four parts:
Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)
Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)
Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)
Automatic Language Recognition (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)
We admire the book's readability. The author avoids difficult mathematical concepts, making the material feel like a conversation.
Is every data scientist a humanist?
Even as a technological professional, you can't escape human interaction, especially with clients.
We hope these books will help you develop interpersonal skills.
Ari Joury, PhD
3 years ago
7 ways to turn into a major problem-solver
For some people, the glass is half empty. For others, it’s half full. And for some, the question is, How do I get this glass totally full again?
Problem-solvers are the last group. They're neutral. Pragmatists.
Problems surround them. They fix things instead of judging them. Problem-solvers improve the world wherever they go.
Some fail. Sometimes their good intentions have terrible results. Like when they try to help a grandma cross the road because she can't do it alone but discover she never wanted to.
Most programmers, software engineers, and data scientists solve problems. They use computer code to fix problems they see.
Coding is best done by understanding and solving the problem.
Despite your best intentions, building the wrong solution may have negative consequences. Helping an unwilling grandma cross the road.
How can you improve problem-solving?
1. Examine your presumptions.
Don’t think There’s a grandma, and she’s unable to cross the road. Therefore I must help her over the road. Instead think This grandma looks unable to cross the road. Let’s ask her whether she needs my help to cross it.
Maybe the grandma can’t cross the road alone, but maybe she can. You can’t tell for sure just by looking at her. It’s better to ask.
Maybe the grandma wants to cross the road. But maybe she doesn’t. It’s better to ask!
Building software is similar. Do only I find this website ugly? Who can I consult?
We all have biases, mental shortcuts, and worldviews. They simplify life.
Problem-solving requires questioning all assumptions. They might be wrong!
Think less. Ask more.
Secondly, fully comprehend the issue.
Grandma wants to cross the road? Does she want flowers from the shop across the street?
Understanding the problem advances us two steps. Instead of just watching people and their challenges, try to read their intentions.
Don't ask, How can I help grandma cross the road? Why would this grandma cross the road? What's her goal?
Understand what people want before proposing solutions.
3. Request more information. This is not a scam!
People think great problem solvers solve problems immediately. False!
Problem-solvers study problems. Understanding the problem makes solving it easy.
When you see a grandma struggling to cross the road, you want to grab her elbow and pull her over. However, a good problem solver would ask grandma what she wants. So:
Problem solver: Excuse me, ma’am? Do you wish to get over the road? Grandma: Yes indeed, young man! Thanks for asking. Problem solver: What do you want to do on the other side? Grandma: I want to buy a bouquet of flowers for my dear husband. He loves flowers! I wish the shop wasn’t across this busy road… Problem solver: Which flowers does your husband like best? Grandma: He loves red dahlia. I usually buy about 20 of them. They look so pretty in his vase at the window! Problem solver: I can get those dahlia for you quickly. Go sit on the bench over here while you’re waiting; I’ll be back in five minutes. Grandma: You would do that for me? What a generous young man you are!
A mediocre problem solver would have helped the grandma cross the road, but he might have forgotten that she needs to cross again. She must watch out for cars and protect her flowers on the way back.
A good problem solver realizes that grandma's husband wants 20 red dahlias and completes the task.
4- Rapid and intense brainstorming
Understanding a problem makes solutions easy. However, you may not have all the information needed to solve the problem.
Additionally, retrieving crucial information can be difficult.
You could start a blog. You don't know your readers' interests. You can't ask readers because you don't know who they are.
Brainstorming works here. Set a stopwatch (most smartphones have one) to ring after five minutes. In the remaining time, write down as many topics as possible.
No answer is wrong. Note everything.
Sort these topics later. Programming or data science? What might readers scroll past—are these your socks this morning?
Rank your ideas intuitively and logically. Write Medium stories using the top 35 ideas.
5 - Google it.
Doctor Google may answer this seemingly insignificant question. If you understand your problem, try googling or binging.
Someone has probably had your problem before. The problem-solver may have posted their solution online.
Use others' experiences. If you're social, ask a friend or coworker for help.
6 - Consider it later
Rest your brain.
Reread. Your brain needs rest to function.
Hustle culture encourages working 24/7. It doesn't take a neuroscientist to see that this is mental torture.
Leave an unsolvable problem. Visit friends, take a hot shower, or do whatever you enjoy outside of problem-solving.
Nap.
I get my best ideas in the morning after working on a problem. I couldn't have had these ideas last night.
Sleeping subconsciously. Leave it alone and you may be surprised by the genius it produces.
7 - Learn to live with frustration
There are problems that you’ll never solve.
Mathematicians are world-class problem-solvers. The brightest minds in history have failed to solve many mathematical problems.
A Gordian knot problem can frustrate you. You're smart!
Frustration-haters don't solve problems well. They choose simple problems to avoid frustration.
No. Great problem solvers want to solve a problem but know when to give up.
Frustration initially hurts. You adapt.
Famous last words
If you read this article, you probably solve problems. We've covered many ways to improve, so here's a summary:
Test your presumptions. Is the issue the same for everyone else when you see one? Or are your prejudices and self-judgments misguiding you?
Recognize the issue completely. On the surface, a problem may seem straightforward, but what's really going on? Try to see what the current situation might be building up to by thinking two steps ahead of the current situation.
Request more information. You are no longer a high school student. A two-sentence problem statement is not sufficient to provide a solution. Ask away if you need more details!
Think quickly and thoroughly. In a constrained amount of time, try to write down all your thoughts. All concepts are worthwhile! Later, you can order them.
Google it. There is a purpose for the internet. Use it.
Consider it later at night. A rested mind is more creative. It might seem counterintuitive to leave a problem unresolved. But while you're sleeping, your subconscious will handle the laborious tasks.
Accept annoyance as a normal part of life. Don't give up if you're feeling frustrated. It's a step in the procedure. It's also perfectly acceptable to give up on a problem because there are other, more pressing issues that need to be addressed.
You might feel stupid sometimes, but that just shows that you’re human. You care about the world and you want to make it better.
At the end of the day, that’s all there is to problem solving — making the world a little bit better.
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M.G. Siegler
3 years ago
Apple: Showing Ads on Your iPhone
This report from Mark Gurman has stuck with me:
In the News and Stocks apps, the display ads are no different than what you might get on an ad-supported website. In the App Store, the ads are for actual apps, which are probably more useful for Apple users than mortgage rates. Some people may resent Apple putting ads in the News and Stocks apps. After all, the iPhone is supposed to be a premium device. Let’s say you shelled out $1,000 or more to buy one, do you want to feel like Apple is squeezing more money out of you just to use its standard features? Now, a portion of ad revenue from the News app’s Today tab goes to publishers, but it’s not clear how much. Apple also lets publishers advertise within their stories and keep the vast majority of that money. Surprisingly, Today ads also appear if you subscribe to News+ for $10 per month (though it’s a smaller number).
I use Apple News often. It's a good general news catch-up tool, like Twitter without the BS. Customized notifications are helpful. Fast and lovely. Except for advertisements. I have Apple One, which includes News+, and while I understand why the magazines still have brand ads, it's ridiculous to me that Apple enables web publishers to introduce awful ads into this experience. Apple's junky commercials are ridiculous.
We know publishers want and probably requested this. Let's keep Apple News ad-free for the much smaller percentage of paid users, and here's your portion. (Same with Stocks, which is more sillier.)
Paid app placement in the App Store is a wonderful approach for developers to find new users (though far too many of those ads are trying to trick users, in my opinion).
Apple is also planning to increase ads in its Maps app. This sounds like Google Maps, and I don't like it. I never find these relevant, and they clutter up the user experience. Apple Maps now has a UI advantage (though not a data/search one, which matters more).
Apple is nickel-and-diming its customers. We spend thousands for their products and premium services like Apple One. We all know why: income must rise, and new firms are needed to scale. This will eventually backfire.
Erik Engheim
3 years ago
You Misunderstand the Russian Nuclear Threat
Many believe Putin is simply sabre rattling and intimidating us. They see no threat of nuclear war. We can send NATO troops into Ukraine without risking a nuclear war.
I keep reading that Putin is just using nuclear blackmail and that a strong leader will call the bluff. That, in my opinion, misunderstands the danger of sending NATO into Ukraine.
It assumes that once NATO moves in, Putin can either push the red nuclear button or not.
Sure, Putin won't go nuclear if NATO invades Ukraine. So we're safe? Can't we just move NATO?
No, because history has taught us that wars often escalate far beyond our initial expectations. One domino falls, knocking down another. That's why having clear boundaries is vital. Crossing a seemingly harmless line can set off a chain of events that are unstoppable once started.
One example is WWI. The assassin of Archduke Franz Ferdinand could not have known that his actions would kill millions. They couldn't have known that invading Serbia to punish them for not handing over the accomplices would start a world war. Every action triggered a counter-action, plunging Europe into a brutal and bloody war. Each leader saw their actions as limited, not realizing how they kept the dominos falling.
Nobody can predict the future, but it's easy to imagine how NATO intervention could trigger a chain of events leading to a total war. Let me suggest some outcomes.
NATO creates a no-fly-zone. In retaliation, Russia bombs NATO airfields. Russia may see this as a limited counter-move that shouldn't cause further NATO escalation. They think it's a reasonable response to force NATO out of Ukraine. Nobody has yet thought to use the nuke.
Will NATO act? Polish airfields bombed, will they be stuck? Is this an article 5 event? If so, what should be done?
It could happen. Maybe NATO sends troops into Ukraine to punish Russia. Maybe NATO will bomb Russian airfields.
Putin's response Is bombing Russian airfields an invasion or an attack? Remember that Russia has always used nuclear weapons for defense, not offense. But let's not panic, let's assume Russia doesn't go nuclear.
Maybe Russia retaliates by attacking NATO military bases with planes. Maybe they use ships to attack military targets. How does NATO respond? Will they fight Russia in Ukraine or escalate? Will they invade Russia or attack more military installations there?
Seen the pattern? As each nation responds, smaller limited military operations can grow in scope.
So far, the Russian military has shown that they begin with less brutal methods. As losses and failures increase, brutal means are used. Syria had the same. Assad used chemical weapons and attacked hospitals, schools, residential areas, etc.
A NATO invasion of Ukraine would cost Russia dearly. “Oh, this isn't looking so good, better pull out and finish this war,” do you think? No way. Desperate, they will resort to more brutal tactics. If desperate, Russia has a huge arsenal of ugly weapons. They have nerve agents, chemical weapons, and other nasty stuff.
What happens if Russia uses chemical weapons? What if Russian nerve agents kill NATO soldiers horribly? West calls for retaliation will grow. Will we invade Russia? Will we bomb them?
We are angry and determined to punish war criminal Putin, so NATO tanks may be heading to Moscow. We want vengeance for his chemical attacks and bombing of our cities.
Do you think the distance between that red nuclear button and Putin's finger will be that far once NATO tanks are on their way to Moscow?
We might avoid a nuclear apocalypse. A NATO invasion force or even Western cities may be used by Putin. Not as destructive as ICBMs. Putin may think we won't respond to tactical nukes with a full nuclear counterattack. Why would we risk a nuclear Holocaust by launching ICBMs on Russia?
Maybe. My point is that at every stage of the escalation, one party may underestimate the other's response. This war is spiraling out of control and the chances of a nuclear exchange are increasing. Nobody really wants it.
Fear, anger, and resentment cause it. If Putin and his inner circle decide their time is up, they may no longer care about the rest of the world. We saw it with Hitler. Hitler, seeing the end of his empire, ordered the destruction of Germany. Nobody should win if he couldn't. He wanted to destroy everything, including Paris.
In other words, the danger isn't what happens after NATO intervenes The danger is the potential chain reaction. Gambling has a psychological equivalent. It's best to exit when you've lost less. We humans are willing to take small risks for big rewards. To avoid losses, we are willing to take high risks. Daniel Kahneman describes this behavior in his book Thinking, Fast and Slow.
And so bettors who have lost a lot begin taking bigger risks to make up for it. We get a snowball effect. NATO involvement in the Ukraine conflict is akin to entering a casino and placing a bet. We'll start taking bigger risks as we start losing to Russian retaliation. That's the game's psychology.
It's impossible to stop. So will politicians and citizens from both Russia and the West, until we risk the end of human civilization.
You can avoid spiraling into ever larger bets in the Casino by drawing a hard line and declaring “I will not enter that Casino.” We're doing it now. We supply Ukraine. We send money and intelligence but don't cross that crucial line.
It's difficult to watch what happened in Bucha without demanding NATO involvement. What should we do? Of course, I'm not in charge. I'm a writer. My hope is that people will think about the consequences of the actions we demand. My hope is that you think ahead not just one step but multiple dominos.
More and more, we are driven by our emotions. We cannot act solely on emotion in matters of life and death. If we make the wrong choice, more people will die.
Read the original post here.
Solomon Ayanlakin
3 years ago
Metrics for product management and being a good leader
Never design a product without explicit metrics and tracking tools.
Imagine driving cross-country without a dashboard. How do you know your school zone speed? Low gas? Without a dashboard, you can't monitor your car. You can't improve what you don't measure, as Peter Drucker said. Product managers must constantly enhance their understanding of their users, how they use their product, and how to improve it for optimum value. Customers will only pay if they consistently acquire value from your product.
I’m Solomon Ayanlakin. I’m a product manager at CredPal, a financial business that offers credit cards and Buy Now Pay Later services. Before falling into product management (like most PMs lol), I self-trained as a data analyst, using Alex the Analyst's YouTube playlists and DannyMas' virtual data internship. This article aims to help product managers, owners, and CXOs understand product metrics, give a methodology for creating them, and execute product experiments to enhance them.
☝🏽Introduction
Product metrics assist companies track product performance from the user's perspective. Metrics help firms decide what to construct (feature priority), how to build it, and the outcome's success or failure. To give the best value to new and existing users, track product metrics.
Why should a product manager monitor metrics?
to assist your users in having a "aha" moment
To inform you of which features are frequently used by users and which are not
To assess the effectiveness of a product feature
To aid in enhancing client onboarding and retention
To assist you in identifying areas throughout the user journey where customers are satisfied or dissatisfied
to determine the percentage of returning users and determine the reasons for their return
📈 What Metrics Ought a Product Manager to Monitor?
What indicators should a product manager watch to monitor product health? The metrics to follow change based on the industry, business stage (early, growth, late), consumer needs, and company goals. A startup should focus more on conversion, activation, and active user engagement than revenue growth and retention. The company hasn't found product-market fit or discovered what features drive customer value.
Depending on your use case, company goals, or business stage, here are some important product metric buckets:
All measurements shouldn't be used simultaneously. It depends on your business goals and what value means for your users, then selecting what metrics to track to see if they get it.
Some KPIs are more beneficial to track, independent of industry or customer type. To prevent recording vanity metrics, product managers must clearly specify the types of metrics they should track. Here's how to segment metrics:
The North Star Metric, also known as the Focus Metric, is the indicator and aid in keeping track of the top value you provide to users.
Primary/Level 1 Metrics: These metrics should either add to the north star metric or be used to determine whether it is moving in the appropriate direction. They are metrics that support the north star metric.
These measures serve as leading indications for your north star and Level 2 metrics. You ought to have been aware of certain problems with your L2 measurements prior to the North star metric modifications.
North Star Metric
This is the key metric. A good north star metric measures customer value. It emphasizes your product's longevity. Many organizations fail to grow because they confuse north star measures with other indicators. A good focus metric should touch all company teams and be tracked forever. If a company gives its customers outstanding value, growth and success are inevitable. How do we measure this value?
A north star metric has these benefits:
Customer Obsession: It promotes a culture of customer value throughout the entire organization.
Consensus: Everyone can quickly understand where the business is at and can promptly make improvements, according to consensus.
Growth: It provides a tool to measure the company's long-term success. Do you think your company will last for a long time?
How can I pick a reliable North Star Metric?
Some fear a single metric. Ensure product leaders can objectively determine a north star metric. Your company's focus metric should meet certain conditions. Here are a few:
A good focus metric should reflect value and, as such, should be closely related to the point at which customers obtain the desired value from your product. For instance, the quick delivery to your home is a value proposition of UberEats. The value received from a delivery would be a suitable focal metric to use. While counting orders is alluring, the quantity of successfully completed positive review orders would make a superior north star statistic. This is due to the fact that a client who placed an order but received a defective or erratic delivery is not benefiting from Uber Eats. By tracking core value gain, which is the number of purchases that resulted in satisfied customers, we are able to track not only the total number of orders placed during a specific time period but also the core value proposition.
Focus metrics need to be quantifiable; they shouldn't only be feelings or states; they need to be actionable. A smart place to start is by counting how many times an activity has been completed.
A great focus metric is one that can be measured within predetermined time limits; otherwise, you are not measuring at all. The company can improve that measure more quickly by having time-bound focus metrics. Measuring and accounting for progress over set time periods is the only method to determine whether or not you are moving in the right path. You can then evaluate your metrics for today and yesterday. It's generally not a good idea to use a year as a time frame. Ideally, depending on the nature of your organization and the measure you are focusing on, you want to take into account on a daily, weekly, or monthly basis.
Everyone in the firm has the potential to affect it: A short glance at the well-known AAARRR funnel, also known as the Pirate Metrics, reveals that various teams inside the organization have an impact on the funnel. Ideally, the NSM should be impacted if changes are made to one portion of the funnel. Consider how the growth team in your firm is enhancing customer retention. This would have a good effect on the north star indicator because at this stage, a repeat client is probably being satisfied on a regular basis. Additionally, if the opposite were true and a client churned, it would have a negative effect on the focus metric.
It ought to be connected to the business's long-term success: The direction of sustainability would be indicated by a good north star metric. A company's lifeblood is product demand and revenue, so it's critical that your NSM points in the direction of sustainability. If UberEats can effectively increase the monthly total of happy client orders, it will remain in operation indefinitely.
Many product teams make the mistake of focusing on revenue. When the bottom line is emphasized, a company's goal moves from giving value to extracting money from customers. A happy consumer will stay and pay for your service. Customer lifetime value always exceeds initial daily, monthly, or weekly revenue.
Great North Star Metrics Examples
🥇 Basic/L1 Metrics:
The NSM is broad and focuses on providing value for users, while the primary metric is product/feature focused and utilized to drive the focus metric or signal its health. The primary statistic is team-specific, whereas the north star metric is company-wide. For UberEats' NSM, the marketing team may measure the amount of quality food vendors who sign up using email marketing. With quality vendors, more orders will be satisfied. Shorter feedback loops and unambiguous team assignments make L1 metrics more actionable and significant in the immediate term.
🥈 Supporting L2 metrics:
These are supporting metrics to the L1 and focus metrics. Location, demographics, or features are examples of L1 metrics. UberEats' supporting metrics might be the number of sales emails sent to food vendors, the number of opens, and the click-through rate. Secondary metrics are low-level and evident, and they relate into primary and north star measurements. UberEats needs a high email open rate to attract high-quality food vendors. L2 is a leading sign for L1.
Where can I find product metrics?
How can I measure in-app usage and activity now that I know what metrics to track? Enter product analytics. Product analytics tools evaluate and improve product management parameters that indicate a product's health from a user's perspective.
Various analytics tools on the market supply product insight. From page views and user flows through A/B testing, in-app walkthroughs, and surveys. Depending on your use case and necessity, you may combine tools to see how users engage with your product. Gainsight, MixPanel, Amplitude, Google Analytics, FullStory, Heap, and Pendo are product tools.
This article isn't sponsored and doesn't market product analytics tools. When choosing an analytics tool, consider the following:
Tools for tracking your Focus, L1, and L2 measurements
Pricing
Adaptations to include external data sources and other products
Usability and the interface
Scalability
Security
An investment in the appropriate tool pays off. To choose the correct metrics to track, you must first understand your business need and what value means to your users. Metrics and analytics are crucial for any tech product's growth. It shows how your business is doing and how to best serve users.