Latest Essays
Editorial

So you want to pursue a career in product management—great! However, the product manager job posts you’ve come across list having product management experience—not great! Don’t worry! Here’s some guidance on how you can start your product management journey.
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Starting as a product manager brings both excitement and challenges. Along the way, there are numerous valuable lessons to be learned in overseeing products, coordinating teams, and achieving success. Reflecting on my own experience, I share the top 12 things I wish I had known as a product manager, aiming to provide guidance to aspiring professionals starting their journey.
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Comparing your product’s growth metrics with those of your competitors is an essential tool for measuring progress, spotting your position in the market, and identifying areas for improvement. However, this method comes with some caveats.
One of the biggest pitfalls of comparing metrics is the potential for misleading results. Even products that are in the same category can have different mechanisms working behind their metrics, making direct comparisons difficult.
The key is to go beyond metrics and focus on the processes that they represent. Only after having a clear understanding of the problem that your product and your competitor are solving can you start determining which metrics (if any) are suitable to compare their performance.
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One of the concepts that is crucial to product work is compound interest and exponential growth. This is a topic that has been adopted from other fields of natural and social sciences into product management. It is crucial in tracking, measuring, and forecasting product growth loops. Understanding compound interest and exponential growth can help you identify growth opportunities, make correct decisions and avoid the pitfalls of product growth.
In this article, we will discuss the basics of compound interest and exponential growth and how it applies to product management.
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Considering a move from product analytics to product management? It’s a natural progression that reflects the desire to expand beyond data analysis and strategic recommendations into actively guiding the product development journey. This move combines analytical expertise with creativity to transform imaginative ideas into concrete, market-ready products.
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As product managers, we regularly use percentages in different ways to measure different metrics. But despite their intuitive simplicity, percentages have different nuances that can make them confusing or misleading if used improperly.
In this article, we’ll discuss percentages, percentage points, and percentiles, along with their uses in product management along with best practices and pitfalls to avoid.
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In May 2023 we launched the SQL Simulator for Product Analytics. The team of creators who authored the simulator—Eugene Zhulkov, Oleg Ya, and Osman Ramazanov—share details about how they discovered the need for a purpose-built SQL course for product and marketing-adjacent people. While creating the new educational product from scratch, they discovered and overcame several challenges.
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Data-driven decision-making is essential for success in product management. And one of the important parts of any kind of data-driven work is measuring and understanding central tendencies in data samples.
Central tendencies refer to the typical or average values of a set of data points, and they can provide insights into the overall performance of your product. Measuring central tendencies is becoming increasingly important in any kind of decision-making that involves data. And product managers have to make decisions based on data on a daily basis.
There are several ways to measure central tendencies, but the most commonly used methods are arithmetic mean and median. In this article, we will explore these two measures, compare them, and see how they can be used in product work.
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There are two main tracks for getting hired as a product manager: applying internally and looking for a new employer. Let’s take a look at both cases and discuss recommendations that will help you stand out.
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Are you considering a career in product management? Before taking the leap, it’s important to assess whether this path aligns with your skills, interests, and goals. With product management roles varying across industries, product types, and company sizes, it can be challenging to determine if it’s the right fit for you.
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- How to determine the conditions necessary for the “aha moment”
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- 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
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- 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
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- 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
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- 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
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- 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
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- 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
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- How to move from marketing to product management?
- Key data skills for product managers: experienced PMs sharing their thoughts
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- 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