The Importance of Data Analysis for Product Managers

We always say that the best Product Managers are data-driven. But what does that mean exactly? No digital product can be built, maintained, or disrupted without data. So, surely every PM knows how to use it the right way, right?

You would hope so, but we may not be there yet.

Many mistakes are made with product analytics every day. Sometimes it can be a critically underused resource. 

In this article, let’s talk about why data analytics matters for Product Managers. We’ll explore examples and see how it aids in building better products. 

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What Does Data-Driven Product Management Mean?

“A key skill that we want product managers to be really good at is interpreting data and making sense of it for business or user behavior.... If you say a metric is going up, good. But it’s about really understanding why it’s going up and what change made our users act differently.”

— Cem Kansu, VP of Product at Duolingo on The Product Podcast

You may hear ‘data-driven’ in a few different contexts when it comes to Product Management. It may be listed as a necessary characteristic for PMs on job postings, and it may also refer to a product.

A data-driven product is one that is backed up by data. The decisions that are made in order to build it are backed up by research and insights, rather than by intuition and guesswork. The same goes for data-driven Product Managers as well as Data Product Managers, who use data to influence their decision-making.

In an ideal world, data-driven really means data-first. It means having a Data Product Strategy in your organization.

The team seizes every opportunity to dig into the data and use it to drive product innovation, product development, and decision making. When a data-driven PM has a new idea for a feature, they don’t just go ahead and start pitching it to product leadership. They dig into the data and reveal the truth about how useful it may be.

What Kinds of Data Should a Product Manager Use?

We could go into the nitty gritty and talk about each and every type of data point that every single Product Manager could ever need…but there are far too many of them! Instead, let’s start with the main categories of data that are useful for Product Managers every day.

1. User data

User research is perhaps the most important type of research that any company can conduct. If you don’t understand your customers, how can you serve them and build a successful product? Short answer – you can’t.

Assumptions are fatal when it comes to understanding your customers. With user interviews, usability testing, card sorting, and A/B testing, you can find out how your customers think, what their behaviors are, and start hypothesizing what their future needs will be.

Using user data helps you to stay user-focused and ensures that you’re building the right product at the right time. It ensures product-market fit and makes it easier to align product teams on a common goal.

“Why do we need to build this thing this way?”

“Because the data shows that the customer likes it that way.”

Types of user data:

• Demographics

• Behaviors

• Online reviews

• NPS scores

2. Product data

You have to know what’s happening inside your product, as much as you need to understand what’s happening inside your users’ minds. You need to know what the typical user flow looks like, how many people successfully complete onboarding, which features are used more than others, and where people drop off.

This applies whether you have an app, software, or a website. If you can track what goes on inside your product, you absolutely should, as it’ll give you a live view of whether your product is working or not.

Product data can also reveal new insights and provide surprising opportunities for product innovation, and perhaps even a pivot that you never would have considered otherwise.

Types of product data:

• Product adoption metrics

• User flows

• Meta data

• Bounce rates

• Abandonment/adoption rates

• Heatmaps

3. Market research

Market research is also essential to launching a product and setting it up for success. 

You could build the world’s best snowsuit, but that’s no use in a desert. It’s critical to understand the environment that your product will be expected to thrive in before you can build something that serves the needs of your customers.

One mistake that some companies make is that they conduct market research only at the start of their journey. But the competitive landscape shifts, and before you launch any new product or feature, you need to have a clear picture of what it looks like. 

You need to run product analysis to understand what your competitors are doing, how you can set yourself apart from them, and what needs are continually being unmet.

Types of market research:

• Competitor analysis

• Brand positioning analysis

• Consumer insights

• User segmentation

4. Business data

While user and product data help you build the right thing, business data helps you build the smart thing. It keeps your product decisions grounded in what actually moves the needle for the company.

You can build the coolest feature in the world—but if it doesn’t improve user retention, bring in revenue, or support your company’s goals, is it really worth it?

By tracking things like acquisition costs, churn, and revenue per user, you’re not just making something customers love — you’re making something the business can sustain and grow.

Types of business data:

• Customer acquisition cost (CAC)

• Churn rate

• Customer lifetime value (CLTV)

• Revenue per user

• Subscription metrics

• Sales team feedback

5. Technical data

Data-driven decisions aren’t just about what the customer sees — they’re also about how the product works behind the scenes. Technical data helps you understand how scalable, reliable, and efficient your product is.

Think of it as the health check for your product’s engine. If your API calls are failing, pages are loading slowly, or bugs keep popping up, it doesn’t matter how great the UX is; it’s going to hurt the product experience.

Technical data also gives product managers a shared language with engineers. It helps you prioritize technical debt, performance improvements, and backend work that often gets overshadowed by shiny new features.

Types of technical data:

• Page load times

• Error rates

• Bug reports

• System uptime

• Latency

• Technical debt indicators

Qualitative vs. Quantitative Data

The debate among Product Managers is about the various benefits of both qualitative and quantitative data. It’s not that one type is ‘good data’ and the other type is ‘bad data’, it’s that they both need to be applied in different ways. You need both to build the best possible data-driven product.

Quantitative data: Sometimes referred to as hard data, it involves data points that are expressed in numeric values. Includes things like NPS scores, and more typical digital metrics.

Qualitative data: Sometimes referred to as soft data, it involves information that can’t be boiled down easily to numerical data. Includes things like app store reviews, recorded conversations with customers, and customer service comments.

You need both types of data to build a product, and combining the two paints a more complete data story.

How Product Managers Use Data (Step-by-step)

Everyone involved in product development uses data analytics to get their jobs done. Marketers use it to create super clickable campaigns, designers use it to craft the best user experience, and leadership uses it to drive innovation and to make the right business decisions.

So how do Product Managers use it?

1. Start by defining the problem or question you want to answer

Before pulling any data, product managers must start with a clear question or hypothesis. Are you trying to figure out why users are churning? Which feature to prioritize next? Whether a new onboarding flow is improving activation rates?

Without a focused question, it’s easy to get lost in vanity metrics or irrelevant dashboards. Good data work starts with curiosity tied to product vision, product strategy, and product goals.

Examples of focused questions:

• “Why is user activation down this month?”

• “Is the new search feature improving engagement?”

• “What segment of users is most likely to convert after a free trial?”

This clarity upfront ensures that all data collection and product analytics efforts are targeted and useful.

2. Identify what type of data you need, and where to find it

Once you know what you’re trying to solve, determine the type of data that can help you answer it. As we’ve covered, this could be user research data, product analytics, market research, business data, or technical metrics.

From there, ask: Where does this data live? You might be looking at Mixpanel for behavioral data, Salesforce for business data, Hotjar for heatmaps, or any other Proddy-awarded tool.

Your job is to:

• Know where your company’s data lives

• Understand what’s available and what’s not

• Work with analysts, data scientists, or engineers if you need help accessing it

3. Collect, clean, and segment the data

Raw data is often messy. Before you can draw insights, you need to make sure what you’re looking at is complete, relevant, and accurate.

That may involve:

• Filtering out irrelevant segments (e.g., internal users)

• Cleaning up duplicate entries

• Normalizing datasets so you can compare apples to apples

• Breaking down data by user cohort, device, region, or plan tier

Segmentation is especially powerful — it helps you uncover patterns you’d miss in aggregate numbers.

Example: If product adoption is low overall but high among power users, that’s a very different story than if it’s low across the board.

4. Analyze the data to generate insights

This is where the magic happens. Once you have clean data, start digging.

Look for trends, outliers, and correlations that answer your original question. Use product management tools like Amplitude, spreadsheets, dashboards, and visualization tools to make the data tell a story.

Ask yourself:

• What’s the trend over time?

• What segments behave differently?

• Are there any clear drop-off points or bottlenecks?

• What user actions are associated with success?

Be cautious not to jump to conclusions — correlation isn’t causation. But strong patterns can guide your next hypotheses.

5. Use data to make and support product decisions

Now that you have insights, it’s time to put them to work. The goal isn’t just to understand your product; it’s to improve it.

Use data to:

• Prioritize features with the biggest business or user impact

• Create product roadmaps aligned with real needs

• Justify decisions in cross-functional collaboration

• Identify where to double down, optimize, or pivot

Good PMs use data not as a crutch, but as a compass. This compass helps them make smarter, faster, and more strategic choices.

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6. Leverage AI to scale data analysis and decision-making

This is where modern PMs get supercharged. AI tools can help you move faster and uncover patterns that would take hours — or days — to find manually.

Here’s how AI product managers are implementing AI in their data workflows:

• AI product manager assistants: Tools like ChatGPT or Klarity can help PMs summarize customer feedback, spot recurring themes, or simulate user personas.

• AI-powered analytics: Platforms like ThoughtSpot or Tableau Pulse use natural language queries and AI to deliver instant insights—no SQL required.

• AI for business use cases: Forecast churn, predict revenue impact from roadmap changes, or simulate what-if scenarios for product launches.

• AI-enhanced product strategy: Use large-scale data to generate product hypotheses, test messaging resonance, and model the impact of pricing changes.

• AI-driven roadmaps: AI can assist in collecting, organizing, and prioritizing information, ultimately guiding your team from the starting block to the finish line.

PMs who embrace these tools can reduce analysis time, make better decisions, and focus more on the creative and strategic side of their role.

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7. Communicate insights clearly to stakeholders

Even the best data doesn’t matter if no one understands it. A core PM skill is translating complex findings into simple, actionable narratives.

Use clear visuals, crisp summaries, and storytelling techniques to communicate what the data says, why it matters, and what to do next.

Tailor your message based on your audience:

• Engineers may want details on key metrics for product management

• Executives care about business impact and outcomes

• Product designers might focus on user behavior and feedback

Clear communication ensures alignment, faster decisions, and stakeholder trust.

8. Close the loop: Measure outcomes and refine

The last step? Don’t stop once you’ve shipped. Use data to see whether your decisions had the intended effect.

• Did the new feature drive the expected engagement?

• Did the UX redesign improve conversion rates?

• Did customer satisfaction increase?

Closing the loop with data helps you build a learning engine. You’re not just guessing. You’re improving, iterating, and evolving your product based on evidence.

Examples of Data Analytics in Product Management

To make all this more concrete, let’s look at how real-world product teams use data analytics in their day-to-day work. These aren’t abstract use cases. They’re the kinds of decisions PMs face all the time.

1. Improving user onboarding

A PM at a SaaS company notices that trial users aren’t converting. Instead of guessing why, they dig into product analytics and see a major drop-off during the onboarding flow, specifically at the step where users are asked to import data.

With this insight, the team experiments with a simplified import process. A/B testing shows a 22% improvement in onboarding completion and a noticeable uptick in trial-to-paid conversions.

2. Prioritizing features based on usage data

The product team is debating whether to improve an existing feature or build something new. Usage data shows that the current feature is being used daily by over 60% of active users, far more than expected.

The PM uses this data to support product prioritization. They argue for doubling down on that feature, adding deeper functionality and better UX. The result? Increased engagement and positive feedback from power users.

3. Diagnosing churn in a freemium mobile app

Churn is rising, but customer surveys aren’t pointing to anything obvious. The PM dives into user retention curves and segment data, discovering that users on older Android devices are experiencing frequent crashes.

The team fixes the technical issues and adds better crash reporting. One month later, churn among Android users drops by 18%.

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4. Finding product-market fit signals in user feedback

A new social app is still in early access. The PM collects qualitative data from app store reviews, support tickets, and user interviews, then runs sentiment analysis to surface themes.

They discover that users love the voice messaging feature — something they hadn’t expected to be a core value prop. This insight leads the team to make it more prominent in the UI, and product growth accelerates.

How To Learn More About Product Data Analytics

Every Product professional has different strengths. 

If you’re more of a creative thinker with a keen eye for product design and marketing, you can still leverage data to build the best possible product. By working with the designated data professionals at your company, or outsourcing your data analytics, you can be a PM without needing to be a Data PM.

While there are Data Product Manager roles out there, to get started in Product Management, you only need to know how to ask the right questions. That’s far more important than knowing SQL. That being said, to be a truly data-driven PM, you should try to learn as much about data analytics as possible.

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