Author: Oleg Yakubenkov

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!

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

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!

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

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!

Continue reading “How launching new products for existing audiences can help grow your company”

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.

Continue reading “The hidden risks of growth models relying on paid marketing”

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!

Continue reading “How to find new markets through value chain analysis”

Every company is unique in its own right, and starting a new job can be a daunting task. Things get even more complicated if you’re jumping into a product manager role, where you virtually interact with all the moving parts of the company. Even veterans can find the first few weeks (or months) frustrating and tiresome.

Fortunately, Anna Buldakova, a seasoned product manager who has worked at several companies, has figured out a formula that can smoothen the journey make the initiation process much more pleasant. In her latest post, Anna shares her experience in finding your way around the people, the culture, the customers, and the product of your new company.

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

Continue reading “Onboarding a product manager — do’s and don’ts in the first few weeks”

Watching new and rapidly changing markets can teach you many things. The work communication market led by companies such as Slack, Microsoft Teams and Workplace by Facebook is something I have been following for a long time.

Last year, before Slack went public, I did an analytical review of the data disclosed in Slack’s S-1 filing. At the end of that review, I shared my opinion that Slack experienced problems in the enterprise segment: the competition from Microsoft Teams and Workplace by Facebook for this market segment threatened Slack’s long-term growth prospects and its $20+ billion valuation.

A lot of things have happened in the eight months that have passed since I published that essay. A lot of new data has surfaced, with one of the biggest market intrigues fading away and a new one appearing. The leading characters once again reminded us of a number of fundamental rules the market plays by. And this is exactly what I am going to talk about in this essay.

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

Continue reading “Slack vs Teams vs Workplace: The intriguing dynamics of the work messenger market”

When conducting experiments, teams usually include all the active users in their tests, or sometimes they tend to add all the new users who join the app during this test. So when calculating the metrics for different test groups, all the data from the moment the A/B test kicks off is taken into account.

Today I’ll talk about how you can reduce the time required to get the signal on the change you are testing in a product. You can do that by changing the process of adding users to the A/B test, and in this essay I will show you how you can do it.

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

Continue reading “Why your A/B tests take longer than they should”

Many articles online compare the freemium conversion rates of Spotify, Dropbox, Slack and Evernote. In one article, the author claims that Spotify’s freemium conversion tops Dropbox by 667%. Another article states that Spotify freemium conversion rate is above 40% while “for most companies that leverage this business model, freemium conversion rates hover somewhere between 2 and 5 percent.”

In this post, I will dispel the myth that Spotify’s conversion rate is somewhere around 40-50%. More importantly, I will discuss why It is very important to be clear about what data underlies a certain metric, because people sometimes use the same metric name but in effect they mean different things.

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

Continue reading “Importance of clearly defining the metric. Or why Spotify’s paying conversion rate is not 40%”

Let’s start with a practical task.

Say a company’s management wants to allocate significant resources to the development of infrastructure that would increase their app’s speed. The hypothesis is that increasing the speed of the app will have a positive effect on the user experience and the key metrics.

Think of an experiment (an A/B test) to validate this hypothesis.

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

Continue reading “Experiments where you make your product worse – the most underrated product manager tool”