Product Leadership: Interview with Director of Product Management at Twitter

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.

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

Setting up event tracking for a mobile app

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

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 game developers can leverage the hidden potential of their data

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…

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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This little-noticed change in iOS 13 can put an end to Calm’s 1B valuation and make the App Store better

 “You never know who’s swimming naked until the tide goes out.”
― Warren Buffett

The last few years were big for non-gaming subscription-based apps in the Apple App Store. During this period, lots of simple apps started making millions of dollars per month.

For example, Celebrity Voice Changer makes over $3M per month and raked in almost $30M in the past few years. QR Code Reader by Tinylab made over $800K last month and over $13M over the last few years. An app called Life Advisor generated over $1M in revenue in the last month alone.

Some of the companies developing pretty basic apps have even become unicorns. In February, Calm raised $88M in funding at a $1B valuation. The maker of Facetune app raised $135M at a unicorn valuation In July.

But how do they do this?

These apps aren’t powered by any innovative technology; they don’t have network effects; and they are pretty easy to copy. So what is their secret sauce? Subscriptions. To be more specific, the obscure way that subscriptions used to work in iOS.

I said “used to work” because Apple made a small change to subscriptions in iOS 13, the latest version of its mobile operating system. This change went unnoticed by media outlets covering iOS 13’s release, but I believe it will have a profound impact on the mobile apps ecosystem in the Apple App Store.

In this article I will use revenue and downloads estimates provided by Datamagic to explain why I believe the following will happen after Apple’s new adjustment to iOS app subscriptions:

  • The revenue of Calm and Facetune on iOS will drop by a factor of 2-4, and they won’t be able to justify 1B valuation.
  • Lots of sneaky subscription apps will first stop growing, and then disappear altogether from the App Store over time.

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

Calm's iOS revenue forecast

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How to estimate the revenue, downloads and audience of a competitor’s app?

All of us are curious about how many downloads a competitor’s app has, how it acquires users, how much audience it has, and, of course, how much it earns.

Finding out about the performance of a competitor’s app is both useful and interesting. Today I will talk about the tools that will help you do this.

As a bonus, at the end of the article, we will verify if Telegram’s MAU is actually 200M active users as it claims.

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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Day N retention, rolling retention, and the many facets of the retention metric

Retention rate is one of the fundamental metrics in product management. We all use it regularly, yet few of us know that there are many different ways to calculate retention rate. And it is very important to know which one to use when you’re making decisions based on retention data.

Let me start with a story. When I worked at Zeptolab (popular game development company, creator of Cut the Rope, King of Thieves, CATS) once we got an email from a gamedev studio that wanted us to publish their game. We were getting many similar emails, but that one got our attention. We were impressed by the metrics of the game, which had just recently soft launched. According to the developers, Day 1 retention rate of the game was over 55%, and Day 7 retention rate was over 25%.

However, when we started playing and testing the game, something felt wrong. The gameplay was not engaging enough to justify >55% Day 1 retention rate. And the meta game design was not good enough to users in the longer term.

Further investigation revealed that what this game development company called retention was actually “rolling retention.” Classic Day N retention of the game happened to be unimpressive.

This is just one example of how retention metrics can misguide you. There are many nuances in how you can calculate it.

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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Looking for spikes. How to increase the effectiveness of your dashboard

Consider this:

A product manager asks an analyst: “Please make a dashboard where we can see Day 1, Day 3 and Day 7 retention rates in dynamics.”

“Are you sure?” the analyst asks. “These charts will be quite noisy. Just look how much the metrics alter from day to day. Maybe it’s a better idea to monitor the weekly retention rates instead. In this case, any random fluctuations will be smoothed out. ”

They called it a deal.

Now a new dot appears on the dashboard once a week. This dot has “Everything is fine, nothing has changed” written all over it. But sometimes, the storms of everyday life are hidden behind this apparent calm: days of ups and downs, victories and defeats that happen during weekdays and weekends.

But no one on the product team finds out about them because everyone is looking at weekly metrics. Consequently, they’re missing both the random and meaningful fluctuations.

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

Looking for spikes. How to increase the effectiveness of your dashboard

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Analytics without numbers: Viewing products through users’ eyes

Here are two things you’ll hear a lot from product teams:

1. A lot of data is needed to reduce uncertainty and get an accurate picture of users’ needs and behavior.

2. People working on products understand and know their users well.

The above statements are misconceptions, and in both cases, the reverse is true. In this post, I will discuss how analyzing user behavior without big data (debunking the first premise) will help us avoid the unpleasant outcomes of thinking you already know your users (debunking the second premise).

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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ASO optimization in practice: How a game I made over the weekend amassed 2 million downloads

A couple of years ago, I wanted to turn my theoretical app store optimization (ASO) knowledge into a working skill.

So I decided to develop a mobile game. My goal was to validate the hypothesis that in the super-competitive mobile gaming market, you can launch a product that will grow into something large solely through organic traffic.

Let me say right away that I did validated this hypothesis: A mobile game we created over the weekend ended up amassing over 2 million downloads, and received over 30,000 new users per day at its peak, all through organic traffic.

But the path to success looked nothing like the original plan.

Here is the story of how the project evolved and how one small change increased the number of downloads by 200%.

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

ASO optimization in practice (App Store Optimization)

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To reduce your product’s churn rate, first find out why users stay

Most of us have learned to think about products in terms of user churn at specific steps in the funnel. We keep asking ourselves “Why do users leave?” and then we try to find and fix the reasons for this. We assume that solving those issues and removing friction will improve the key product metrics.

Funnel optimization surely is a good approach to improve key product metrics. However, it doesn’t work in all situations. And when it does work, it usually only brings incremental improvements, not fundamental changes.

Today, we’re going to talk about a different approach when examining your product. This approach can boost your product, and sometimes it can take your product to a completely new direction.

I suggest posing the question “Why do users stay?” before “Why do users leave?”

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

To reduce your app’s churn rate, first find out why users stay

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