Latest Essays

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.

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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).

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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%.

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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?”

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

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I didn’t write this article. My wife, Luba Vyaznikova, did. Luba is currently a product lead at Badoo. However, before taking this position, she had launched a very successful product. Below, she shares her story.
–Oleg

Two and a half years ago, a spark of inspiration and the passion to create something new guided me down a path to create an app with a $500,000 annual run rate.

And then, last September, Apple deleted our app from the App Store after adding its features in the new version of it iOS operating system.

But let me start from the beginning…

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* The app’s monthly revenue since its launch (source: iTunes Connect)

Slack Technologies, the developer of the popular namesake team collaboration messaging app, recently applied for a public offering on the stock market. This is not a classic IPO, but a “direct listing,” also known a “direct public offering.” This means Slack is not raising money by directly selling shares and instead allows early investors and employees to sell their shares in the public offering. Music streaming service Spotify held a successful direct listing last year.

This story caught my attention for a simple reason. In August 2016, I joined the team developing a still-undercover product called Workplace by Facebook—a direct competitor to Slack. I worked on the product for 2.5 years. Back then, I dreamed of having an opportunity to look inside Slack’s business metrics.

It may seem that Slack has revealed a lot of data about the business in their S-1 filing, a document that is almost 200 pages in length.

The reality is, they haven’t. The company had already disclosed in various ways much of the information compiled in their report.

But if we combine the data disclosed in S-1 filing and the experience I gained while working on Slack’s competitor, we’ll be able to uncover interesting details that will paint a more holistic picture.

I must say that this article contains my personal thoughts on the matter, jotted down while going through their S-1 filing, and should not be considered as investment advice.

→ 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.