Latest Essays

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.

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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.

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It is common to evaluate your product performance and the impact of changes you make by using engagement metrics with active audience in the denominator. Examples of these metrics include the time spent per active user, the occurrence of certain actions (messages sent, levels played, chapters read, etc) per active user, or ratio metrics (what percent of active users perform a specific action) .

In most situations, these engagement metrics will be helpful. But in some cases, they can be misleading. And it is important to understand why and when this can happen, and what you can do about it.

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pic from http://mediainjection.com

Product/market fit is an important concept when working on a new product. All entrepreneurs and product managers are committed to it. But if you ask what the term means, very few will be able to give a clear answer. Even fewer will have an understanding of how we can measure product/market fit using metrics.

Without a clear definition, even the most useful concepts will be of little help when making decisions. In this post, we will discuss some of the most common product/market fit definitions and their advantages and disadvantages, as well as tell you about PMFsurvey.com (Product / Market fit survey by Sean Ellis) developed in collaboration with GoPractice, which is designed to give you an objective metric of how close are you to Product / Market fit.

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→ Learn data-driven product management in Simulator by GoPractice.

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Finding insights and answers to questions in data is a key skill in product analytics. And developing this skill is the area where analysts usually see their growth potential.

Talking from experience, I strongly recommend paying attention to another aspect of analytical work: communication skills. Key here is not only finding insights, but also turning them into projects and making sure they convert into real value for users. 

Getting to this point requires building relationships with the team, participating in key discussions, gaining credibility, and learning to present information in an effective way.

This article provides a series of recommendations for product analysts. However, it will be equally useful for product managers and executives who want to maximize the impact of analysts working in their teams.

→ Test your product management and data skills with this free Growth Skills Assessment Test.

→ Learn data-driven product management in Simulator by GoPractice.

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What are the traits of a good product manager? Where do you get started from? How do you know you’re on the right track? Those are the questions that should be on the mind of anyone who yearns to have successful career in product management.

While each of us will have own unique journey to success, there’s a lot we can learn from successful product managers. One of them is Vaibhav Sahgal, former Director of Product and Vice President at Zynga, and current Head of Growth at Reddit. In an interview with Anna Buldakova, Vaibhav shared tips on growing your career as a product manager, balancing short- and long-term bets, and also developing the right skillset to become a product leader.

→ Test your product management and data skills with this free Growth Skills Assessment Test.

→ Learn data-driven product management in Simulator by GoPractice.

→ Learn growth and realize the maximum potential of your product in Product Growth Simulator.

→ Join our discussion on LinkedIn. New topics to talk about every week.

Classical economics is built on the assumption that people act rationally, which means their decisions are aimed at maximizing their benefits. This statement (which is a base for the classic economics theory), is a bit doubtful, partly because people usually don’t have all the necessary information to make the best decision in a given situation. But even within the framework of the available information, people tend to make irrational decisions.

In this post I will show you some interesting experiments that highlight relevant characteristics and patterns in humans’ decision making processes. Most of these experiments are about the way people behave when deciding about purchasing something, so you can easily apply them to your business or everyday life.

I have to say, I really like the picture below as it perfectly portrays the main idea of ​​this article. Interestingly, squares A and B have the same color in this picture. (You can check it yourself using Photoshop or some other photo-editing tool if you don’t believe me.)

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Few people get to experience product management at large, successful tech companies and decide to share their expertise and outlook with others who want to get started in the field. One of them is my good friend Anna Buldakova, a seasoned product manager.

Anna sat down with Scott Eblen, Director of Product Management at Twitter, to discuss if technical background is a requirement for Product Managers, what is the difference between Google and small startups, and how to create a good vision and strategy.

→ Test your product management and data skills with this free Growth Skills Assessment Test.

→ Learn data-driven product management in Simulator by GoPractice.

→ Learn growth and realize the maximum potential of your product in Product Growth Simulator.

→ Join our discussion on LinkedIn. New topics to talk about every week.

Creating the app of your dreams can be a daunting challenge. But an even greater challenge is making sure your users find and experience its value. To do this, you need to have visibility on how users navigate through your app and interact with its features.

This is where analytics and event-tracking platforms come into play.

Analytics help you answer important questions about your users such as what do they like about your app, where are they struggling, which features need improvement, which features are hindering the user experience, and many more.

The foundation of analytics is data collection. Every analytics platform comes with a software development kit (SDK) that you can integrate into your app to send events. Developers add code that calls special application programming interfaces (API) in the locations where an event must be fired (for example, when users launch the app for the first time or when they send a message). If event logging is set up poorly, you will be blinded when working on your product, or you will work with erroneous data, which in my opinion is even worse than not having any data.

In this article I will be talking about the following:

• Common mistakes in setting up analytics for mobile apps
• The right approach to integrating analytics in mobile apps
• Some hacks that will allow you to use data more efficiently

→ Test your product management and data skills with this free Growth Skills Assessment Test.

→ Learn data-driven product management in Simulator by GoPractice.

→ Learn growth and realize the maximum potential of your product in Product Growth Simulator.

→ Join our discussion on LinkedIn. New topics to talk about every week.

The general perception is that data analytics and data-driven product management is more suited for business applications, social media apps, and communications platforms. But the reality is, any kind of product can benefit from a data-driven perspective.

One of the domains where the value of data is often underestimated is games.

In my experience, most teams working on mobile games don’t fully use the potential of data. They tend to track topline metrics, measure effectiveness of paid marketing campaigns, analyze the impact of product changes all while running meticulous AB tests. This may sound like enough, but it really isn’t. Not if your goal is to climb onto the top of the grossing charts and stay there.

There are many more ways how data can increase your chances of building and operating a successful mobile game. The key is to stop thinking of data as a way to look back at what you have done, but instead start using data as a tool that can help you make decisions, decrease uncertainty and remove main product risks as early as possible.

In this post, I will walk you through a few examples of how data can drive key product decisions at different stages of product development cycle. But first, let me tell you a story…

→ Test your product management and data skills with this free Growth Skills Assessment Test.

→ Learn data-driven product management in Simulator by GoPractice.

→ Learn growth and realize the maximum potential of your product in Product Growth Simulator.

→ Join our discussion on LinkedIn. New topics to talk about every week.