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Looi Qin En

3 years ago

I polled 52 product managers to find out what qualities make a great Product Manager

More on Leadership

Solomon Ayanlakin

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.

Product Management Metrics — Measuring the right metrics as a Product Leader by Solomon Ayanlakin

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:

Popular Product Metric Buckets for Product Teams

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:

  1. 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.

  2. 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.

  3. 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:

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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

Notable companies and their North star metrics

🥇 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.

Product Metrics for UberEats

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.

Sean Bloomfield

Sean Bloomfield

3 years ago

How Jeff Bezos wins meetings over

Photo by Christian Wiediger on Unsplash

We've all been there: You propose a suggestion to your team at a meeting, and most people appear on board, but a handful or small minority aren't. How can we achieve collective buy-in when we need to go forward but don't know how to deal with some team members' perceived intransigence?

Steps:

  1. Investigate the divergent opinions: Begin by sincerely attempting to comprehend the viewpoint of your disagreeing coworkers. Maybe it makes sense to switch horses in the middle of the race. Have you completely overlooked a blind spot, such as a political concern that could arise as an unexpected result of proceeding? This is crucial to ensure that the person or people feel heard as well as to advance the goals of the team. Sometimes all individuals need is a little affirmation before they fully accept your point of view.

  • It says a lot about you as a leader to be someone who always lets the perceived greatest idea win, regardless of the originating channel, if after studying and evaluating you see the necessity to align with the divergent position.

  • If, after investigation and assessment, you determine that you must adhere to the original strategy, we go to Step 2.

2. Disagree and Commit: Jeff Bezos, CEO of Amazon, has had this experience, and Julie Zhuo describes how he handles it in her book The Making of a Manager.

It's OK to disagree when the team is moving in the right direction, but it's not OK to accidentally or purposefully damage the team's efforts because you disagree. Let the team know your opinion, but then help them achieve company goals even if they disagree. Unknown. You could be wrong in today's ever-changing environment.

So next time you have a team member who seems to be dissenting and you've tried the previous tactics, you may ask the individual in the meeting I understand you but I don't want us to leave without you on board I need your permission to commit to this approach would you give us your commitment?

Bart Krawczyk

Bart Krawczyk

2 years ago

Understanding several Value Proposition kinds will help you create better goods.

Fixing problems isn't enough.

Numerous articles and how-to guides on value propositions focus on fixing consumer concerns.

Contrary to popular opinion, addressing customer pain rarely suffices. Win your market category too.

Graphic provided by the author.

Core Value Statement

Value proposition usually means a product's main value.

Its how your product solves client problems. The product's core.

Graphic provided by the author.

Answering these questions creates a relevant core value proposition:

  • What tasks is your customer trying to complete? (Jobs for clients)

  • How much discomfort do they feel while they perform this? (pains)

  • What would they like to see improved or changed? (gains)

After that, you create products and services that alleviate those pains and give value to clients.

Value Proposition by Category

Your product belongs to a market category and must follow its regulations, regardless of its value proposition.

Creating a new market category is challenging. Fitting into customers' product perceptions is usually better than trying to change them.

New product users simplify market categories. Products are labeled.

Your product will likely be associated with a collection of products people already use.

Example: IT experts will use your communication and management app.

If your target clients think it's an advanced mail software, they'll compare it to others and expect things like:

  • comprehensive calendar

  • spam detectors

  • adequate storage space

  • list of contacts

  • etc.

If your target users view your product as a task management app, things change. You can survive without a contact list, but not status management.

Graphic provided by the author.

Find out what your customers compare your product to and if it fits your value offer. If so, adapt your product plan to dominate this market. If not, try different value propositions and messaging to put the product in the right context.

Finished Value Proposition

A comprehensive value proposition is when your solution addresses user problems and wins its market category.

Graphic provided by the author.

Addressing simply the primary value proposition may produce a valuable and original product, but it may struggle to cross the chasm into the mainstream market. Meeting expectations is easier than changing views.

Without a unique value proposition, you will drown in the red sea of competition.

To conclude:

  1. Find out who your target consumer is and what their demands and problems are.

  2. To meet these needs, develop and test a primary value proposition.

  3. Speak with your most devoted customers. Recognize the alternatives they use to compare you against and the market segment they place you in.

  4. Recognize the requirements and expectations of the market category.

  5. To meet or surpass category standards, modify your goods.

Great products solve client problems and win their category.

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Gill Pratt

Gill Pratt

3 years ago

War's Human Cost

War's Human Cost
I didn't start crying until I was outside a McDonald's in an Olempin, Poland rest area on highway S17.


Children pick toys at a refugee center, Olempin, Poland, March 4, 2022.

Refugee children, mostly alone with their mothers, but occasionally with a gray-haired grandfather or non-Ukrainian father, were coaxed into picking a toy from boxes provided by a kind-hearted company and volunteers.
I went to Warsaw to continue my research on my family's history during the Holocaust. In light of the ongoing Ukrainian conflict, I asked former colleagues in the US Department of Defense and Intelligence Community if it was safe to travel there. They said yes, as Poland was a NATO member.
I stayed in a hotel in the Warsaw Ghetto, where 90% of my mother's family was murdered in the Holocaust. Across the street was the first Warsaw Judenrat. It was two blocks away from the apartment building my mother's family had owned and lived in, now dilapidated and empty.


Building of my great-grandfather, December 2021.

A mass grave of thousands of rocks for those killed in the Warsaw Ghetto, I didn't cry when I touched its cold walls.


Warsaw Jewish Cemetery, 200,000–300,000 graves.


Mass grave, Warsaw Jewish Cemetery.

My mother's family had two homes, one in Warszawa and the rural one was a forest and sawmill complex in Western Ukraine. For the past half-year, a local Ukrainian historian had been helping me discover faint traces of her family’s life there — in fact, he had found some people still alive who remembered the sawmill and that it belonged to my mother’s grandfather. The historian was good at his job, and we had become close.


My historian friend, December 2021, talking to a Ukrainian.

With war raging, my second trip to Warsaw took on a different mission. To see his daughter and one-year-old grandson, I drove east instead of to Ukraine. They had crossed the border shortly after the war began, leaving men behind, and were now staying with a friend on Poland's eastern border.
I entered after walking up to the house and settling with the dog. The grandson greeted me with a huge smile and the Ukrainian word for “daddy,” “Tato!” But it was clear he was awaiting his real father's arrival, and any man he met would be so tentatively named.
After a few moments, the boy realized I was only a stranger. He had musical talent, like his mother and grandfather, both piano teachers, as he danced to YouTube videos of American children's songs dubbed in Ukrainian, picking the ones he liked and crying when he didn't.


Songs chosen by my historian friend's grandson, March 4, 2022

He had enough music and began crying regardless of the song. His mother picked him up and started nursing him, saying she was worried about him. She had no idea where she would live or how she would survive outside Ukraine. She showed me her father's family history of losses in the Holocaust, which matched my own research.
After an hour of drinking tea and trying to speak of hope, I left for the 3.5-hour drive west to Warsaw.
It was unlike my drive east. It was reminiscent of the household goods-filled carts pulled by horses and people fleeing war 80 years ago.


Jewish refugees relocating, USHMM Holocaust Encyclopaedia, 1939.

The carefully chosen trinkets by children to distract them from awareness of what is really happening and the anxiety of what lies ahead, made me cry despite all my research on the Holocaust. There is no way for them to communicate with their mothers, who are worried, absent, and without their fathers.
It's easy to see war as a contest of nations' armies, weapons, and land. The most costly aspect of war is its psychological toll. My father screamed in his sleep from nightmares of his own adolescent trauma in Warsaw 80 years ago.


Survivor father studying engineering, 1961.

In the airport, I waited to return home while Ukrainian public address systems announced refugee assistance. Like at McDonald's, many mothers were alone with their children, waiting for a flight to distant relatives.
That's when I had my worst trip experience.
A woman near me, clearly a refugee, answered her phone, cried out, and began wailing.
The human cost of war descended like a hammer, and I realized that while I was going home, she never would

Full article

Maddie Wang

Maddie Wang

3 years ago

Easiest and fastest way to test your startup idea!

Here's the fastest way to validate company concepts.

I squandered a year after dropping out of Stanford designing a product nobody wanted.

But today, I’m at 100k!

Differences:

I was designing a consumer product when I dropped out.

I coded MVP, got 1k users, and got YC interview.

Nice, huh?

WRONG!

Still coding and getting users 12 months later

WOULD PEOPLE PAY FOR IT? was the riskiest assumption I hadn't tested.

When asked why I didn't verify payment, I said,

Not-ready products. Now, nobody cares. The website needs work. Include this. Increase usage…

I feared people would say no.

After 1 year of pushing it off, my team told me they were really worried about the Business Model. Then I asked my audience if they'd buy my product.

So?

No, overwhelmingly.

I felt like I wasted a year building a product no one would buy.

Founders Cafe was the opposite.

Before building anything, I requested payment.

40 founders were interviewed.

Then we emailed Stanford, YC, and other top founders, asking them to join our community.

BOOM! 10/12 paid!

Without building anything, in 1 day I validated my startup's riskiest assumption. NOT 1 year.

Asking people to pay is one of the scariest things.

I understand.

I asked Stanford queer women to pay before joining my gay sorority.

I was afraid I'd turn them off or no one would pay.

Gay women, like those founders, were in such excruciating pain that they were willing to pay me upfront to help.

You can ask for payment (before you build) to see if people have the burning pain. Then they'll pay!

Examples from Founders Cafe members:

😮 Using a fake landing page, a college dropout tested a product. Paying! He built it and made $3m!

😮 YC solo founder faked a Powerpoint demo. 5 Enterprise paid LOIs. $1.5m raised, built, and in YC!

😮 A Harvard founder can convert Figma to React. 1 day, 10 customers. Built a tool to automate Figma -> React after manually fulfilling requests. 1m+

Bad example:

😭 Stanford Dropout Spends 1 Year Building Product Without Payment Validation

Some people build for a year and then get paying customers.

What I'm sharing is my experience and what Founders Cafe members have told me about validating startup ideas.

Don't waste a year like I did.

After my first startup failed, I planned to re-enroll at Stanford/work at Facebook.

After people paid, I quit for good.

I've hit $100k!

Hope this inspires you to request upfront payment! It'll change your life

Zuzanna Sieja

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:

  1. Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)

  2. Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)

  3. Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)

  4. 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.