Category posts
Product analytics: tools and hints
Editorial

It’s a competitive world out there. To win over users, products have to contend with the alternatives already on the market.
There are ways of measuring audience size, revenue, and downloads for competing apps so that you get a better understanding of the situation and market trends. With these tools, you can adjust your growth strategy and find benchmarks for key metrics for products similar to yours.
In this article, we will look at the tools that can help you to do this. Special emphasis will go to those available for free.
(more…)Editorial

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.
(more…)Editorial

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.
(more…)
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
- How to move from marketing to product management?
- Key data skills for product managers: experienced PMs sharing their thoughts
- Data mistakes to know and avoid as a product manager
- Data cherry-picking to support your hypothesis. What is it? Why is it bad?
- How can PMs encourage more teammates to use data?
- Metrics to focus on before and after product/market fit. How to better understand your product at different stages?
- How to establish effective collaboration between product managers and data analysts
- How to move from engineering to product management?
- Losing sight of real users and their needs behind the metrics. How can product teams avoid this?
- Quantitative vs qualitative data: what is the difference and when should you use one instead of the other
- Pros and cons of a data-driven culture
- Data-driven, data-informed, and data-inspired product decisions. What are the differences and when should you use each one?
- Using data to understand competitive and market dynamics
- Moving from a startup to an enterprise as a product manager
- The downsides of a data-driven culture
- Using data for strategic decisions
- Product sense demystified. The importance behind the buzzword
- Common mistakes made by junior product managers and how to overcome them
- Is product management the right choice for you? This is your checklist
- 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