Key product management quotes from Zero to One

There are a few books that I consider must-read for anyone working on products. Today I’ll be talking about my favourite ideas from Peter Thiel’s iconic book Zero to One: Notes on Startups, or How to Build the Future

Here’s what the scholar Nassim Taleb has to say about Zero to One: “When a risk taker writes a book, read it. In the case of Peter Thiel, read it twice. Or, to be safe, three times. This is a classic.”

Taleb’s remark on Zero to One surprised me. And here is why.

At this point, I was already familiar with a few books written by Nassim Taleb. I had also read (for a few times) Thiel’s class notes that later became the manuscript for Zero to One. I really liked the harmony and sequence of thoughts in each of these books, but at the same time for me the perspectives on life of the authors seemed rather opposite.

One way or another, Zero to One is beyond amazing. I enjoy re-reading it every once in a while, and each time I find something new. And this is exactly what sets aside great books from good ones.

P.S. If you want to learn how data can help you build and grow products, take a look at GoPractice! Simulator.

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How to forecast key product metrics through cohort analysis

Forecasting the dynamics of revenue, audience, and other key metrics is an important process for any product that is in its growth phase. Having a good forecast helps to prioritize projects at the planning stage, and then helps to keep track of how quickly you are growing against the forecast, allowing you to spot problems as early as possible.

The very process of creating a forecasting model allows you to synchronize the team in terms of understanding the product’s growth model. It also provides a tool for assessing the impact of working on different areas of the model.

Today we will talk about building audience and revenue forecasts for your product using cohort analysis. We will also find out the pitfalls and difficulties of this process.

P.S. If you want to learn how data can help you build and grow products, take a look at GoPractice! Simulator.

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Why every team member should know the key product metrics

When I worked at Facebook, the Workplace analytics team had a cool tradition: The team’s weekly meetings always started with a small data quiz.

The winner of the previous week’s competition would prepare a question about the product’s key metrics. For example, “what was last month’s MAU?” or “how many new users joined last week?” or “what proportion of the new companies reach 10 users?” or “what was last month’s revenue?” The question had one requirement: Its answer had to be found on the team’s dashboard.

The participants were to write down the answer without getting help from computers, which meant we could only use our memory to do so. The person whose answer was closest to the correct number got +1 point in the chart, and the person who was the farthest lost 1 point. Every six months, a winner was chosen and the game started again.

I participated in five seasons and won three of them. In one of the final rounds, I was tied with another analyst. The team arranged the final round, where we had to answer five questions in a blitz quiz. I managed to score the winning point and won the mug that you see in the photo below.

I told this story not because I wanted to brag about winning the quiz (well, this too, to be honest). In almost every quiz, the respondents’ guesses on metrics were distributed across a wide range, which I found surprising.

Why? Well, first of all, it was the analysts who played the game. They were the people who worked with data most of their time and should have been good at navigating it. Second, these analysts were working at Facebook, a company that has a very advanced and strong data culture. At Facebook, each team has clear goals, dashboards are available to all the company’s employees, and all meetings start with progress updates on key metrics. How could these people be so wrong in answering questions about the product they were working on?

If you decide to play this game with your company’s employees, you will most likely be as surprised as I was. It will turn out that most people have very vague ideas about the key metrics of your product and business. And some people will have no idea at all.

In this essay, we will discuss why it is important for team members to remember at least approximate values ​​of the key product metrics, why this usually doesn’t happen, and how to get there.

P.S. If you want to learn how data can help you build and grow products, take a look at GoPractice! Simulator.

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Growing your company through launching a successful product for new audiences

In the previous essays of this series, we discussed how established companies and products can grow by entering new markets through movement into adjacent dependent segments in the value chain and building new products for an established user base.

Today we will talk about growing through expanding your product to new audiences. Here, the most typical growth paths are geographical expansion and movement up and down the market (B2C – SMB – Mid Market – Enterprise).

We will explore several examples where companies have successfully—and at times, not so successfully—used this growth path. We will also try to define a set of questions that will help to make a more informed decision about choosing this vector for a particular company.

P.S. If you want to learn how data can help you build and grow products, take a look at Simulator by GoPractice!

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Long-term retention – the foundation of sustainable product growth

The most common metrics gaming companies focus on are Day-1, Day-7, and Day-30 retention rate. While these metrics are of great help early in the journey, it’s long-term retention which is key to lasting success and a seat in the top-grossing charts. This post makes a case for long term-retention and why your focus should be first and foremost there.

P.S. If you want to learn how data can help you build and grow products, take a look at Simulator by GoPractice!

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How the backlog issues prioritization saved the company from closure during the pandemic: Stories of launching three products

I first met Vitaly almost five years ago in Palo Alto when he was participating in an acceleration program at 500 Startups with a product called Concert With Me (an event recommendation service). Over the next two years, the service reached a multimillion dollar annual revenue, but then shut down due to unexpected changes in Facebook’s policies. Then the team created a new go-to-market strategy and released Tendee, a SaaS marketing automation product for the event industry. It met a product-market fit in Europe and the US and brought several large customers and reached dozens of thousands of dollars in MRR. But it had to be put on hold due to the Covid19 pandemic.

In need of a survival plan, the team decided to introduce to the world the prioritization tool they had used within the company for almost two years.

The new product led to striking results, as well as some curious realizations and insights into how sharply the experience of product development differs in conformity with the added value level and market type.

I got in touch with Vit a week ago, and he shared his story with me. It was a gripping conversation, so it occurred to us we could develop it into an article where Vitaly would share his experience in the product development process, as well as reflections on the fundamental differences between the launch of his latest backlog-grooming product (Ducalis.io) and previous event marketing products.

P.S. If you want to learn how data can help you build and grow products, take a look at Simulator by GoPractice!

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How the “Slack vs Microsoft Teams” race evolves as the world switches to remote work

After my recent essay “Slack vs Teams vs Workplace: The intriguing dynamics of the work messenger market,” I didn’t plan to revisit the competition between Slack and Microsoft Teams just yet. Despite the rapid development of the work communication market, it is still a B2B market that is changing relatively slowly.

However, something extraordinary has happened: We are in the midst of one of the greatest “experiments” of our time, and a great part of the world has switched to remote work due to the COVID-19 pandemic. The outbreak has greatly sped up the development of remote work tools. This situation has propelled us several years ahead in time, much faster than it was destined to happen, allowing us to look into the future of the market today.

Another reason to take a closer look at Slack vs Teams is that recently, a lot of new data has surfaced about Slack. Even before the coronavirus outbreak, several large companies decided to adopt Slack as the communication tool for all their employees, including IBM with its 350,000-strong workforce and Uber, which has more than 38,000 employees. The number of paid Slack customers has grown by more than 7,000 over the past one and a half months, surpassing the growth in the entire previous quarter. This week, Slack CEO Stewart Butterfield shared the sequence of the recent events in a series of posts on Twitter, all of them conveying a clear message: Slack is growing very fast.

All of the above made me doubt the assumptions I’ve made in my past essays, in which I didn’t put too much faith in Slack’s chances of winning the race against Microsoft Teams. In light of the new data, I decided to take another look at the market and figure out what was going on.

And here’s what I realized: Slack is a great business, but it still stands no chance against Microsoft Teams in dominating the messenger market. The recent weeks have further confirmed this assumption.

P.S. If you want to learn how data can help you build and grow products, take a look at Simulator by GoPractice!

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How launching new products for existing audiences can help grow your company

In the previous essay of this series, we discussed how established companies and products can grow by entering new markets through movement into adjacent dependent segments in the value chain. Today, we will talk about another growth path: building new products for an established user base.

We will explore several examples where companies have successfully—and at times, not so successfully—used this growth path. We will also try to define a set of questions that will help to make a more informed decision about choosing this vector for a particular company.

P.S. If you want to learn how data can help you build and grow products, take a look at Simulator by GoPractice!

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The hidden risks of growth models relying on paid marketing

In late 2013, Fab, a fast-growing e-commerce startup that had raised $330 million in funding, realized that it had a serious problem: Its business model wasn’t working. The company started on a downward sloped that began with laying off many employees (including its co-founder). In 2015, the company, which had been valued at more than $1bn, was acquired by PCH Innovations for a mere $15 million.

What went wrong? Well, like all commercial failures, Fab’s story is complicated and unique. But one recurring theme can be seen in the demise of this once-promising startup: over-reliance on paid marketing.

In this essay, I’ll talk about a growth model based on paid marketing. I will discuss the limitations and the hidden risks of these models, and the consequences of ignoring these risks. I will also discuss how to make the growth model based on paid marketing sustainable and secure.

Let’s assume you’ve reached the product/market fit, which means your product generates value for a specific market segment. You’ve also found the advertising channels where LTV (Lifetime Value) of the acquired users exceeds CAC (Customer Acquisition Cost)—you have created effective channels for delivering the product to your target audience.

That’s great news because few products get this far. The vast majority get eliminated from the race at earlier stages. But it’s not yet time to sit back and relax. There are new dangers ahead, especially if the product’s growth becomes highly dependent on paid user acquisition channels.

P.S. If you want to learn how data can help you build and grow products, take a look at GoPractice! Simulator.

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How to find new markets through value chain analysis

I have been familiar with the concept of the value chains for quite a long time. However, it wasn’t until I read Ben Thompson’s Stratechery blog (I highly recommend reading it at https://stratechery.com/) that I came across its practical applications in market analysis. Looking at products and companies from the point of view of their position in the value chain opens up a new perspective that helps to understand them, find not-so-obvious opportunities, and make more effective decisions.

Today I’ll talk about value chains within the context of expanding an established business or product to new markets. There will be more essays devoted to companies entering the new markets. In each, I will focus on different aspects of this process.

In this series of essays, I want to analyze the possible ways for businesses and products to enter adjacent and new markets. I will also discuss the advantages, nuances and pitfalls of these methods.

In this post, I focus on one of the most risk-free ways to enter a new market, which basically involves moving along the value chain towards the related dependent segments.

P.S. If you want to learn how data can help you build and grow products, take a look at Simulator by GoPractice!

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