Data Driven Product Management: Backing up What You Do With Evidence

Data-driven Product Management is all about finding facts. Digging up the nuggets of truth about your product and your users that you can use to lead your product development and future decisions. 

When thinking about all the different things you can do to potentially make your product better, it’s easy to be frozen stiff by choice. Which option is right? What should I choose? Should I focus on onboarding? Is it best to make changes to the UI? What if I get it wrong? What if I ruin things? Have I left the stove on? Ahh, it’s all too much! 

Breathe out. Take a chill pill 🧘. By diving into the data you can pull out information that guides you to the best thing to do. You can test your ideas and hypotheses to make sure it’s actually a valid route. 

It’s just like science. Isaac Newton once had a theory that objects were being pulled down to the ground, he tested it with data, and then boom, gravity exists. If your data-driven Product Management suggests that a certain action will improve a key metric of yours, then it’s worth trying out.

Data is central to a Product Manager’s job, and as the years go by more and more PMs are becoming dependent on it, keen to run the numbers before doing anything. That’s a good way to be – for the most part. 

Let’s run through data-driven Product Management, how to do it, and if it’s always the best approach to your product discovery and beyond.

What is data driven Product Management?

Data-driven Product Management is the practice of making product decisions based on insights gathered from data. By analyzing user behavior, feedback, and market trends, Product Managers can prioritize features, optimize roadmaps, and improve overall product performance, ensuring that actions are guided by real-world evidence rather than assumptions. Okay, but what does that mean in practice? 

Well, it means that you’re making data-driven decisions and prioritizing your roadmap and product backlog based on evidence and validated learnings. You’re not guessing about what to do, doing it based on a hunch, or changing something just because a single customer asked for it. You’re looking into the numbers and using statistics and metrics to support your decisions.

For many people, this type of Product Management is the gold standard these days. A process you should be adopting if you want any chance of being considered a good Product Manager. And that’s fair – it’s typically better to have your next steps backed up by data. 

But what data? Well, when following data-driven Product Management, there are two main types of product data: quantitative and qualitative. 

• Quantitative data: This is numbers and stuff. Data that can be measured and expressed numerically. It’s statistics and percentages and its objective. You impose the meaning onto the numbers when using quantitative methods. 
• Qualitative data: This is focused on opinions, user feedback, and insights helping you to understand the product experience and perceptions of your users. It provides context and deeper meaning. Qualitative methods include user interviews, surveys, and observations on user behavior. 

When following a data-driven approach, many forget that qualitative data is still data. Many focus on and get caught up in the numbers, forgetting that things like a customer feedback strategy and user flows provide great evidence as well. 

Data-driven Product Management forms the foundations of the Build-Measure-Learn approach to product development. This is a lean framework and cycle to follow when creating and improving upon your product. 

You start with your ideas and build a first version of your product, your MVP. From there, you measure its performance with feedback and data and then learn from those findings to make improvements to boost product performance.

That was obviously a very basic explanation. You can learn more about Build-Measure-Learn in our full glossary article below: 

What does it mean to be a data driven Product Manager? 

Being a data-driven Product Manager means that you value data highly, and that product discovery and research are the cornerstone to the way that you do things. If you’re validating your ideas with evidence to help with data-driven decision-making, congrats, you’re a data-driven Product Manager. 

Being a data-driven Product Manager isn’t a job title or sub-sect of the Product Manager role like being a Technical Product Manager or a Growth Product Manager is. Instead, you’ll likely see the need for a data-driven approach in general PM job descriptions, which shows how it’s become a core facet of the job. 

To be a data-driven Product Manager, you’re going to excel in a few specific Product Management tasks like:   

• Analyzing product usage data
• Setting and tracking KPIs and product metrics
• Conducting A/B testing and experiments
• Making data-informed decisions

How do you do data driven Product Management? 

One key skill that a data-driven Product Manager should have nailed is knowing how to create and test product hypotheses. This is the crux of data-driven Product Management. 

A product hypothesis is basically an idea – something that you think might be true. You are predicting the relationship between two variables. 

You can structure your hypothetical statements as ‘if this thing happens then this other thing must be the case’. Want a proper example? Sure:

“If we removed our AI chatbot from the navigation bar and put it as an expandable tooltip on all our pages, then more users would start using the feature.”

Sounds like a good idea. But is it really? The thing is, that you’re going to have tons of hypotheses you’re going to want to test with data. But, you can’t possibly test them all. That’s why you need to take an initial pass over them all in order to validate them and assess whether they’re worth full testing. 

One way of doing this is using a prioritization framework such as the MoSCoW framework. To do this, you evaluate your hypotheses to see if it’s worthy of testing, judging them against your current product vision and goals. You’ll put them into four categories, helping you pick out the juicy hypotheses you should test first: 

The MoSCoW prioritization model isn’t just for figuring out what hypotheses you should test. In fact, its core function is to figure out what feature or product requirements you should focus on. Learn more about MoSCoW below:

Why is data driven Product Management important?

Data-driven Product Management is an important practice because it helps you create evidence-based decisions. Data-driven decisions are likely going to be better decisions than those backed up by, well, nothing. 

For many Product Managers, data provides reassurance that you’re doing a good job. It’s kind of like a thumbs up from someone in the crowd when delivering a talk – it’s a sign to keep going. 

Data-driven Product Management also prevents you from falling foul of unconscious biases. See, everyone has preconceptions about customers and your product without really meaning to, and these preconceptions and biases can often influence your decision-making if you don’t use data. Even if you’re aware of them, it’s hard to mitigate against. By using data – especially objective quantitative data – it can help you see the real picture. 

Plus, using data-driven Product Management also ensures that you’re creating your products with users in mind. That’s because most of the data you’re going to acquire is user data. Metrics that tell you how your customers are using your product. 

By being data-driven you’re listening to the customer and learning about their wants, needs, preferences, and dislikes. When we use data in both day-to-day problem-solving and long-term strategic planning, it empowers us to build products that deliver value to users.

Here’s a full list of data-driven Product Management pros and cons to see the importance of it, and understand some of the limitations:

Data Driven product management pros & cons

Pros:

• Informed decision-making: Data reduces biases and assumptions.
• Continuous improvement: Ongoing data analysis helps teams refine and improve products over time.
• Reduced risk: Validating decisions with data helps minimize the chances of costly mistakes.
• Personalized customer experience: Data allows for deep insights into customers, allowing you to customize experiences.
• Objective prioritization: Data helps Product Managers prioritize features and resources more effectively.
• Measurable success: Tracking clear KPIs and metrics enables teams to measure the success of decisions.

Cons: 

• Over-reliance on data: Focusing solely on data can stifle creative ideas that might not be backed by numbers.
• Data misinterpretation: Poor-quality data can lead to incorrect conclusions, driving bad decisions.
• Analysis paralysis: With too much data available, teams can get stuck over-analyzing.
• Ignoring qualitative insights: Overemphasis on quantitative data can lead to the undervaluation of qualitative insights.

How do you collect data for data driven Product Management? 

To have any chance of doing data-driven Product Management properly, you need the right tools to capture and analyze user behavior effectively. A key starting point is using product analytics tools that track user actions, features they engage with, and how they interact with your product. These tools let you dig deeper into which areas need attention and which features are performing well.

In addition to analytics, session recording tools are great for gathering qualitative data as they provide real-time insights into how users navigate your product, while heatmaps visually highlight areas of high user interaction. These tools help identify usability issues or areas where users might be dropping off, allowing you to refine the user experience.

Of course, you want to make sure that all the data you capture is easy to understand. The last thing you want is data that you can’t squeeze context out of. This means setting up your product to send precise, event-based data – such as clicks, feature interactions, and conversions – into a system where you can easily interpret it. These systems, often integrated with product analytics tools, allow you to track and organize user behaviors in a meaningful way. By sending this data to platforms that offer robust analysis and visualization capabilities, like dashboards or custom reports, you can quickly interrogate the data to derive actionable insights. Without this framework, you’re essentially flying blind, making it harder to identify the most critical trends and user behaviors.

Equally important is maintaining data accuracy throughout the process. If your data capture setup is incorrect, the insights you derive could lead to poor product decisions. For instance, if your product analytics integration isn’t properly configured, you might miss tracking crucial events or incorrectly segment your users, leading to a distorted view of their behavior. 

Regularly auditing your tracking setup is vital. Are all important user actions being recorded? Are your segmentation filters correctly identifying different user groups? Are you capturing the right events to measure user engagement with key product features? The more thorough and accurate your setup, the better positioned you’ll be to make informed decisions that truly reflect user behavior, not just what the numbers superficially show.

What types of product data can be used in data driven Product Management?

To utilize data-driven Product Management well, you need to choose the right metrics to track. What do you want to learn, and crucially, what can you learn from the data you have available to you? There’s no point tracking data for something completely unrelated to what you want to achieve. That’s not giving you meaningful insights.

If your focus is to improve customer retention and user activation, you’re going to want to focus on metrics and data like adoption rate, activation rates, customer churn rates, and other product adoption metrics and user-focused data to see where you can improve and what you should prioritize. 

BUT if your goal is to increase revenue from existing customers, you’ll want to look at something slightly completely different and dive into metrics such as upsell rates, average revenue per user (ARPU), and customer lifetime value (CLV). These data points will help you identify opportunities to alter your SaaS pricing models, cross-sell complementary products, or adjust product pricing strategies for maximum impact.

That’s why it’s so important to be aligned around a single North Star Metric. This is a key metric tied to your product strategy that a company or Product Management team uses to measure long-term sustainable growth. It represents the core value that a product delivers to its users and serves as the focal point for your product vision that drives all product decisions. Examples include:

• Facebook: Daily Active Users (DAUs)
• Airbnb: Number of nights booked
• Spotify: Time spent listening
• Slack: Number of teams sending 2000+ messages

So what metrics should you be tracking? Well, There’s a boatload of metrics and KPIs you can measure to help with data-driven decision-making. Too many if you ask me. Here’s a quick, snappy list to give you an idea of the kind of things you can be looking for. 

Churn rate 

Your churn rate measures the percentage of customers who stop using your product over a period. Product Managers use churn data to identify why users are leaving, analyze patterns, and develop strategies to improve retention, such as enhancing customer support or optimizing onboarding processes with a product tour.

Customer acquisition cost (CAC)

CAC tells you how much you’re spending to acquire a new customer. Tracking CAC helps Product Managers balance marketing costs and product profitability. Reducing CAC through improved targeting or customer experience can help boost ROI.

Customer lifetime value (LTV)

LTV estimates the total revenue a business can expect from a single customer over their relationship. Product Managers use LTV to identify high-value customers and allocate resources to customer retention efforts, ensuring long-term profitability.

Retention rate

This metric tracks the percentage of customers who continue using a product over time. High retention signals product satisfaction and that users have reached the wow moment. Low retention may highlight that you have some pain points. Improving retention through feature enhancements or better communication can drive growth.

Monthly recurring revenue (MRR)

MRR measures predictable revenue from subscription-based products. Product Managers use MRR to gauge product performance and forecast revenue, making it easier to set strategic goals and identify areas for upsell opportunities or pricing adjustments.

Sales data

Sales data can tell you the number of products sold and the resulting revenue. Analyzing sales data helps Product Managers understand customer demand, identify trends, and optimize product offerings to meet market needs, boosting both revenue and customer satisfaction.

User flows

User flows illustrate how customers navigate through your product. Analyzing them helps Product Managers understand user behavior and feature usage, identify friction points, and optimize features or redesign workflows to enhance the user experience (UX) and improve conversion rates.

Bounce rates

Bounce rate refers to the percentage of users who leave after visiting just one page or interacting briefly with your product. Product Managers can investigate bounce rates to identify areas where users lose interest, refining onboarding or key touchpoints to increase engagement.

Heatmaps

Heatmaps show where users click or scroll most on a webpage or within an app. Product Managers can use heatmaps to understand user interaction patterns, optimize layout or content placement, and ensure key product features are easily accessible to improve user experience.

Net Promoter Score (NPS)

NPS measures customer loyalty by asking how likely users are to recommend your product. Product Managers use NPS to gauge overall satisfaction and loyalty, and a low NPS can signal areas where the product or customer experience needs improvement.

If you want to find out more about the core KPIs to track, check out our comprehensive eBook that outlines all the possible Product Management metrics from which you can pick your priority KPIs.

How do I get better at data driven Product Management?

So you want to use data to inform your processes? Join the club. But before we can grant you entry, there are a few house rules to follow that make sure you do it right and approach data-driven Product Management properly. Here’s our tips to make you better at using data: 

• Set clear objectives: Before you begin collecting data, define what success looks like. Having a North Star metric will help with this. Without clear goals, data can become overwhelming, leading to you not knowing what information to focus on. 
  
• Prioritize data quality: High-quality data is so important. Ensure you’re collecting accurate, reliable data, and avoid making decisions based on incomplete or biased datasets. Clean data collection methods and proper storage practices are essential for achieving actionable insights​. 
  
• Balance data with intuition: Just because the data tells you to do something doesn’t mean you should blindly follow that instruction. If a line graph jumped off a cliff would you do too? While data is helpful, the best Product Managers balance it with experience and intuition. A fix suggested by the data may not actually be a good idea in practice due to reasons like budget, resources, or unexpected customer behavior.
  
• Incorporate diverse data sources: Don’t rely solely on one type of data. Use a mix of data gathered from multiple sources to make sure that you’re not too zeroed in on a single user type or channel. The wider the scope, the more accurate the results.
  
• Collaborate with cross-functional teams: Data doesn’t live in a silo. Share insights with team members within your product team structure and beyond. Collaboration ensures that everyone is aligned with product objectives and uses data to make informed decisions across departments​.
  
• Stay user-centric: At the core of data-driven Product Management is understanding your user’s needs. Don’t ever venture too far from that. Regularly look at data that reflects user behavior, isolates their pain points, and finds their preferences. The better you understand your customers, the better your product decisions will be​.
  
• Be agile and iterative: Embrace an agile product development approach. Use data to fuel continuous discovery. A/B testing and other experiment-based methods allow you to make small adjustments and optimize your product based on real-time feedback​.

Is data driven Product Management always the right approach? 

It may seem that you NEED to be a data-driven Product Manager to have any ounce of success these days, but it’s not always the best approach depending on your situation. 

For example, if you’re in a brand new start-up business, you’re unlikely to have a large enough data set to do valuable quantitative data analysis. Trying to find trends and evaluate your metrics may not give you a fully clear picture, as your sample size may be too small and create a data bias due to an anomaly that would average out in a larger sample size. 

See, data can be wrong and create its own biases too. We tend to associate having data with having all the ‘facts.’ But not all data is clean, accurate, or relevant. While running product experiments or customer interviews, variables can always affect the integrity of the information you’ve gathered and lead to data bias.

If you don’t have the data available to you just yet, don’t feel like you need to go hard in being quantitative data-driven. Instead, focus on qualitative information like customer feedback and opinions to find value and ideas on what to do and iterate on. Of course, you can turn this into statistics, something like:

“Hey, 65% of our users mentioned frustration with the lack of collaboration tools in our product. Should we explore adding some new features to make teamwork easier?”

Another thing to remember is that data isn’t king. Using it isn’t the secret formula to build a better product, it’s just another tool. Data can’t replace common sense, but it can give you clues on what to add, change, or get rid of when building software applications. It can tell you how popular your product is, and alert you to problems. But it never provides the full picture. You need to use your noggin to ensure that your next step is actually viable.

Using data the right way

Data-driven Product Management helps take the guesswork out of the equation, empowering you to make smarter, evidence-informed decisions. By focusing on metrics, user behavior, and feedback, you can lead your product to success while avoiding costly mistakes. But don’t let the data overwhelm you – balance it with experience and intuition to ensure you’re truly solving the right problems.

While data is your trusty compass, it doesn’t replace the importance of context and human insight. Not every decision can (or should) be purely driven by numbers. Balancing quantitative metrics with qualitative feedback allows you to see the full picture, giving you the chance to innovate, meet customer needs, and, most importantly, avoid getting stuck in analysis paralysis. Remember, it wasn’t just the numbers that sparked Isaac Newton to discover gravity, he also had to watch the real-world action of an apple falling off a tree to get the idea. 

If you’re looking for a product management tool that has data-driven insights built-in, ProdPad has you covered. We can help you manage your product backlog and roadmap – and so much more! With ProdPad you can gather and analyze customer feedback, linking that insight to your product Ideas, helping you back up your decisions with evidence while keeping your team aligned and on track. 

It’s time to stop guessing and start building a well-prioritized data-driven roadmap to help you create an outstanding product. See what you can do with a free trial. Register now 👇.