Category posts
Interviews with expert PMs
We interview seasoned product people on various topics. Their expertise will help you find a path from your current career track to product management, get better at using data, and get a deeper understanding of your product in general.
Editorial

Product managers know that understanding their market and competition inside and out is vital to the success of their products. Comprehensive market knowledge tells you what problems your customers are trying to solve, what they want, and ultimately what new features or products to build. Knowing the competitive landscape helps to set your business apart, allows you to create a strategy to deal with new competitive developments, and helps to arm your sales team to win against the competition.
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When an organization says they are data-driven, they typically mean that they base decisions on data. But there can be vast differences with how data is used to make these decisions. Is data only being used to validate straightforward decisions? Are multiple sources of data combined with other factors to determine priorities like the features to be worked on next quarter? Or is an exploration of data being used to spark innovation and determine new strategy? Each situation requires different skills, tools, and ways of working with data to be successful.
This is why the concepts of data-informed and data-inspired are being added to the data-driven discussion; they allow for a more nuanced definition of how data is actually used in an organization. Data-informed and data-inspired decisions consider depending not only on data for clear-cut decisions, but on using data in conjunction with other important influences and to invent something new.
Some may argue that adding the terms data-informed and data-inspired to the data-driven discussion adds complexity and muddles the discussion around data. While that may be true in some cases, really understanding how to correctly use data based on a particular need is critical to creating products that customers love. In the end, the terminology isn’t as important as making sure you’re getting the most out of data.
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While most product managers know that using data in their daily activities can have tremendous benefits, sometimes they find themselves in an environment where doing so is not easy. Perhaps they don’t have access to the data they need, the data is unreliable, or there is no support in place to incorporate data into their processes. These product managers are not in a data-driven culture.
A data-driven culture is when an organization embraces data to make decisions at all levels. The organization has the infrastructure and talent needed to collect, transform, and analyze data, along with reliable and trustworthy data sources. There is an importance on using data to support hypotheses and resolutions. Data-driven cultures embrace data and bake it into their everyday processes.
But a data-driven culture doesn’t just happen on its own. It needs both top-down and bottom-up support in the organization. Upper management must make the decision to invest in data and infrastructure while the teams must believe that using data in their daily jobs is beneficial. And while data enthusiasts in an organization can plant the seed, the entire organization’s support is needed for a data-driven culture to blossom.
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Product managers use data at the heart of their decision making. There are two types of data that they rely on: quantitative and qualitative. Quantitative data refers to numerical data that can be measured; examples include number of clicks, number of users, and monthly recurring revenue. Qualitative data cannot be counted, but instead describes traits or features such as ease of use, user likes and dislikes, and motivations behind user actions.
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Metrics help companies to achieve goals and transform business by pinpointing areas for improvement. But sometimes, metrics can actually be harmful and counterproductive. Excessive focus on metrics can cause product teams to neglect customers’ needs.
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Many people have made a successful transition from an engineering career to a product management one. These two paths have a lot in common. They’re both focused on meeting customer needs and building great products. The two roles must work together to ensure the right solution is built. But of course there are differences. Product managers focus more on the “why” and the “what” while engineers focus on the “how.” Product managers uncover unmet customer needs and create a vision to address them, while engineering actually builds out that vision.
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As more companies aspire to be data-driven, the role of the data analyst is becoming crucial both to the organization and to product managers themselves. In fact, the World Economic Forum found that the data analyst/scientist role had the most increase in demand in 2020. Clearly these positions are incredibly needed.
What does a data analyst do? A data analyst is responsible for gathering, organizing, and interpreting data to provide business insight. Typically this insight is used to solve an issue, make a decision, or determine performance. Simply put, a data analyst interprets data to drive better business outcomes, which is exactly why product managers must collaborate with them effectively.
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Product/market fit is the make-or-break factor for a company. It helps businesses understand whether their product has market appeal and they can dive into the product growth stage with confidence.
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Working with data helps companies across the board to unlock their potential and become more productive and better at making decisions. However, making people in the team and company rely on data involves a lot of work. Product managers must often set a strategy, reinvent processes, and change organizational behavior.
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Data is an essential part of the work of every product manager. It helps to form and validate hypotheses, provide more insights about user behavior, and make better decisions and track product changes.
But the misuse of data can be harmful. One important example is selecting only data that confirms a particular hypothesis and ignores relevant contradictory evidence.
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Other content series
that you might find useful
- What is the difference between growth product manager, marketing manager, and core PM
- Two types of product work: creating value and delivering value
- Product/market fit can be weak or strong and can change over time
- Should a product be 10 times better to achieve product/market fit?
- How to measure the added value of a product
- Customer retention levers: task frequency and added value
- Product metrics, growth metrics, and added value metrics
- Product manager skills: evolution of a PM role and its transformation
- Addressing user pain points vs solving user problems better
- How product teams get the “aha moment” wrong
- How well do you articulate value during user activation? Check with the value communication framework
- Tax/benefit framework for analyzing user activation
- How to improve user activation by obtaining and leveraging additional user data
- Reducing friction, strengthening user motivation: onboarding scenarios and solutions
- Optimize user activation by reducing friction and strengthening motivation
- When to invest in optimizing user onboarding and activation
- Testing user activation fit for diverse use cases
- Why objective vs. perceived product value matters for activation
- Value windows: finding when users are ready to benefit from your product
- User activation starts long before sign-up
- Designing activation in reverse: value first, acquisition channels last
- CJM: from first encounter to the “aha moment”
- Session analysis: an important tool for designing activation
- When and why to add people to the user activation process
- Product-level building blocks for designing activation
- How time to value and product complexity shape user activation
- Time to value: an important lever for user activation growth
- How to determine the conditions necessary for the “aha moment”
- How to find “aha moment”: a qualitative plus quantitative approach
- How “aha moment” and the path to it change depending on the use case
- The dos and don’ts of measuring user activation
- User activation is one of the key levers for product growth
- When user activation matters and you should focus on it
- The values and principles of Wise. Key ideas from the Breakout Growth Podcast by Sean Ellis
- How Revolut Trading was built. The importance of industry expertise and the balance of conservative and new approaches
- How the “Slack vs Microsoft Teams” race evolves as the world switches to remote work
- Slack vs Teams vs Workplace: the intriguing dynamics of the work messenger market
- How to calculate unit economics for your business
- Guide to ARPU: formula, calculation example, LTV vs ARPU
- How to calculate customer Lifetime Value. The do’s and don’ts of LTV calculation
- Designing product experiments: template and examples
- Mistakes in A/B testing: guide to failing the right way
- Peeking problem – the fatal mistake in A/B testing and experimentation
- Why your A/B tests take longer than they should
- Experiments where you make your product worse – the most underrated product manager tool
- Avoid this pitfall when comparing your product’s metrics with your competitor’s
- Compound and exponential growth for product managers
- What product managers must know about percentages, percentage points, and percentiles
- Arithmetic mean and median for product managers
- Ways to estimate a competing app’s downloads, revenue, reach, and traffic
- How to design and run JTBD research interviews: guide and templates
- Not every product is habit-forming, but all products can have loyal users
- Hook Model: encouraging a product habit to improve retention
- How product habits are formed and what dopamine has to do with it
- Correlation and causation: how to tell the difference and why it matters for products
- Side projects to enter product management: tips and expert insights
- How do you avoid adding unnecessary features to your product?
- 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?
- The easiest and hardest parts of a PM’s job
- Side projects to enter product management: tips and expert insights
- What qualities do successful product managers have in common?
- What teams expect of a new product manager in the first 90 days
- Where to start as an aspiring product manager?
- How to move from product analytics to product management?
- Get hired as a product manager: staying at your current company vs. looking elsewhere
- 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
- Retention: how to understand, calculate, and improve it
- Long-term retention—the foundation of sustainable product growth
- Rolling retention, Day N retention, and the many facets of the retention metric
- Traffic attribution models. Why attribution models need to change along with growth channels, product, business objective and external environment
- Errors in calculating ROI and unit economics. Impact of attribution models and incrementality on the ROI calculation of marketing channels