Category posts
Product
Oleg Ya

GoPractice opens a series on how to create products that people need – on the basics of product management.
From this article, you will learn how product work differs through the prism of “problems and pains” from work through the prism of “increasing the effectiveness of the solution.”
(more…)Oleg Ya

ARPU and LTV are often used interchangeably. But in reality, they are two fundamentally different metrics. They are calculated in different ways. They answer different questions.
This article will discuss the following:
- How to calculate ARPU
- How ARPU differs from LTV
- How increasing ARPU can harm your business
- How decreasing ARPU can benefit your business
- Why you need to be careful about using ARPU to make product decisions
(more…)→ 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.
Oleg Ya

Cohort analysis is a highly effective product and marketing analytics tool. Unfortunately, few people know about it, and those who do rarely use it.
This essay will discuss the following:
- What is the essence of cohort analysis?
- What is the difference between growth metrics and product metrics?
- Why do attempts to build product analytics based on growth metrics fail?
- How to use cohort analysis in marketing and product analytics
- Which product metrics should you monitor and why?
(more…)→ 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.
Oleg Ya

LTV (Lifetime Value) is an important metric for decision-making in both marketing and product management. But measuring LTV is a bit tricky and you can easily make mistakes when calculating it. Moreover, even articles that have found their way to the first first page of Google search results contain mistakes when it comes to calculating LTV.
In this essay, I will discuss how to (not) calculate LTV, and how to avoid these common mistakes:
- Calculating LTV based on revenue instead of gross profit.
- Calculating LTV by using users’ Lifetime which is calculated as 1/churn or in any other way.
- Calculating LTV based on the average number of user purchases.
(more…)→ 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.
Oleg Ya

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.
(more…)→ 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.
Oleg Ya
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.
→ 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.

Oleg Ya

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.
(more…)→ 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.
Oleg Ya
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.
→ 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.

Oleg Ya
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.
→ 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.

Oleg Ya

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.
→ 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.

Other content series
that you might find useful
- Addressing user pain points vs solving user problems better
- Product manager skills: evolution of a PM role and its transformation
- Product metrics, growth metrics, and added value metrics
- Customer retention levers: task frequency and added value
- How to measure the added value of a product
- Should a product be 10 times better to achieve product/market fit?
- Product/market fit can be weak or strong and can change over time
- Two types of product work: creating value and delivering value
- What is the difference between growth product manager, marketing manager, and core PM
- When user activation matters and you should focus on it
- User activation is one of the key levers for product growth
- The dos and don’ts of measuring user activation
- How “aha moment” and the path to it change depending on the use case
- How to find “aha moment”: a qualitative plus quantitative approach
- How to determine the conditions necessary for the “aha moment”
- Time to value: an important lever for user activation growth
- How time to value and product complexity shape user activation
- Product-level building blocks for designing activation
- When and why to add people to the user activation process
- Session analysis: an important tool for designing activation
- CJM: from first encounter to the “aha moment”
- Designing activation in reverse: value first, acquisition channels last
- User activation starts long before sign-up
- Value windows: finding when users are ready to benefit from your product
- Why objective vs. perceived product value matters for activation
- Testing user activation fit for diverse use cases
- When to invest in optimizing user onboarding and activation
- Optimize user activation by reducing friction and strengthening motivation
- Reducing friction, strengthening user motivation: onboarding scenarios and solutions
- How to improve user activation by obtaining and leveraging additional user data
- Tax/benefit framework for analyzing user activation
- How well do you articulate value during user activation? Check with the value communication framework
- How product teams get the “aha moment” wrong
- Slack vs Teams vs Workplace: the intriguing dynamics of the work messenger market
- How the “Slack vs Microsoft Teams” race evolves as the world switches to remote work
- How Revolut Trading was built. The importance of industry expertise and the balance of conservative and new approaches
- The values and principles of Wise. Key ideas from the Breakout Growth Podcast by Sean Ellis
- How to calculate customer Lifetime Value. The do’s and don’ts of LTV calculation
- Guide to ARPU: formula, calculation example, LTV vs ARPU
- How to calculate unit economics for your business
- Experiments where you make your product worse – the most underrated product manager tool
- Why your A/B tests take longer than they should
- Peeking problem – the fatal mistake in A/B testing and experimentation
- Mistakes in A/B testing: guide to failing the right way
- Designing product experiments: template and examples
- To reduce your product’s churn rate, first find out why users stay
- What is product/market fit and how to measure PMF
- How engagement metrics can be misleading
- How to forecast key product metrics through cohort analysis
- Cohort analysis. Product metrics vs growth metrics
- Correlation and causation: how to tell the difference and why it matters for products
- How product habits are formed and what dopamine has to do with it
- Hook Model: encouraging a product habit to improve retention
- Not every product is habit-forming, but all products can have loyal users
- How to design and run JTBD research interviews: guide and templates
- Where to start as an aspiring product manager?
- How to move from product analytics to product management?
- Is product management the right choice for you? This is your checklist
- Common mistakes made by junior product managers and how to overcome them
- Product sense demystified. The importance behind the buzzword
- Using data for strategic decisions
- The downsides of a data-driven culture
- Moving from a startup to an enterprise as a product manager
- Using data to understand competitive and market dynamics
- Data-driven, data-informed, and data-inspired product decisions. What are the differences and when should you use each one?
- Pros and cons of a data-driven culture
- Quantitative vs qualitative data: what is the difference and when should you use one instead of the other
- Losing sight of real users and their needs behind the metrics. How can product teams avoid this?
- How to move from engineering to product management?
- How to establish effective collaboration between product managers and data analysts
- Metrics to focus on before and after product/market fit. How to better understand your product at different stages?
- How can PMs encourage more teammates to use data?
- Data cherry-picking to support your hypothesis. What is it? Why is it bad?
- Data mistakes to know and avoid as a product manager
- Key data skills for product managers: experienced PMs sharing their thoughts
- How to increase the effectiveness of your product analysts
- Why every team member should know the key product metrics
- How to move from marketing to product management?
- Key data skills for product managers: experienced PMs sharing their thoughts
- Product manager skills: evolution of a PM role and its transformation
- What is the difference between growth product manager, marketing manager, and core PM
- How to move from engineering to product management?
- Product growth, reinvented: what growth hacking is (and isn’t)
- Moving from a startup to an enterprise as a product manager
- Product manager interview: real questions plus guide for employers and candidates
- Rolling retention, Day N retention, and the many facets of the retention metric
- Long-term retention—the foundation of sustainable product growth
- Retention: how to understand, calculate, and improve it
- Errors in calculating ROI and unit economics. Impact of attribution models and incrementality on the ROI calculation of marketing channels
- Traffic attribution models. Why attribution models need to change along with growth channels, product, business objective and external environment