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Professions, skills, and teams
Editorial

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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Junior product managers face both opportunities and challenges in the constantly evolving tech industry. They are learning to oversee the development and launch of new products while developing a deep understanding of the market, user behavior, and technology. Despite the industry’s ongoing changes, the potential for these new product managers to shape the product’s future and make a significant impact is vast.
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Although the term “product sense” may sound like just another buzzword, it’s important to understand the concept behind it.
Having a strong product sense means being able to create solutions that truly address your customers’ problems. By developing your product sense, you can improve your ability to identify gaps in the market, anticipate customer demand, and create products that truly resonate with your audience. So while it may be tempting to dismiss product sense as an industry fad, taking the time to truly understand its value can have a significant impact on your product’s development and your own professional growth.
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Whether you’re hiring or trying to be hired yourself, the interview process is always a challenge. Here we present the typical flow together with revealing questions to help both interviewers and aspiring product managers.
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Where a product manager works affects their life, skills, and career. Startups and enterprises, i.e. large IT companies, are at opposite ends of the tech company spectrum, but they do have some things in common. Core product management skills are used in both environments, and both places have motivated, smart, and skilled professionals that want to help their customers. Both settings offer rich experiences that grow talent, however they differ in the types of skills that are most developed.
A startup teaches product managers how to move fast, tackle new problems, and wear many hats; an enterprise provides a chance to hone the product management craft and learn from successful experts in the field. Enterprises typically move slower than startups by design, and much of the extra time is spent communicating and negotiating with stakeholders. Because of the large user base, the impact of a product manager is usually broader than at a startup.
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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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Approaches to product growth changed when growth hacking came onto the scene more than a decade ago. But like any influential concept, growth hacking has inspired criticism, diverse schools of thought, and more than a few misconceptions.
As we describe here, there is no authoritative, one-size-fits-all definition of growth hacking. But in the most general terms, it is a process aimed at communicating the key value of a product to the largest possible audience. These efforts span the entire product and marketing funnel and involve software engineers, designers, and analysts. A number of spin-off methodologies and frameworks have emerged containing many of the same principles.
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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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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
- 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 move from marketing to product management?
- 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