From Data to Decisions: Building Better Products

Data is a product manager’s goldmine! It is essential for making any kind of product-related decisions. Simply put, good data results in good decisions, which leads to a successful business!

Many companies have adopted modern agile methodologies to stay ahead of the competition and meet customer needs. This includes constant learning, building, testing, and releasing iteratively. Data becomes the driving force for this type of development.

As a product manager, understanding how to procure and analyze data is essential, not just an advantage. A product manager should be able to derive conclusions and insights related to customer satisfaction and identify customer needs from the provided data.

It’s essential to continually learn and gain a better understanding of users and what is important to them when considering changes or improvements to the product.

How to Go About Data Collection and Analytics

Now that we understand how important data is for product development and sustainability, we need to know how to procure it and what to do with it.

Data collection and analysis begin with defining key performance indicators (KPIs). These generally include satisfaction, retention, and conversion rates. This data can be obtained through various mediums such as surveys, user feedback, application analytics, and more.

Once collected, data can be used to understand product performance based on the defined KPIs. Accordingly, more informed decisions can be made regarding product enhancements. This means constant learning is the most important part of product management.

What Kind of Data to Collect?

User Data

Without a doubt, this is the most important type of data you need to collect to understand how your product is performing. This is essential because products are made for users!

Assumptions and personal biases can hinder user understanding. To truly understand your customers and achieve product/market fit, it’s essential to conduct user research using methods like user feedback, surveys, persona creation, user interviews, and A/B testing. It is also important to pay close attention to user data from sources such as Reddit, social media platforms, blogs, articles, and YouTube. This kind of data is not quantifiable but provides useful insights into how the product is perceived in the market.

These techniques help gain insights into customer needs and future potential.

Product Data

While gathering user data is crucial for better decisions on user journeys, it is equally important to collect data on the product’s performance. This includes the number of users onboarded, the features that are used more than others, the pages with the highest drop-off rates, and the pages with the most reported issues. This will give a clear sense of how well the product is working for the target audience. These insights help determine if your product is truly effective and may spark innovation or prompt a pivot you hadn’t previously considered.

Market Data

Understanding market needs is one of the most important steps before launching a product. Identifying the target audience and their expectations is crucial. However, many don’t realize that this is not only necessary at the beginning of the product launch journey. Market trends keep evolving. To stay ahead of the competition, it is vital to understand what’s happening around you. It's important to know what your competitors are doing, find ways to differentiate yourself, and identify consistently unmet demands. Market research techniques like competitor analysis, user segmentation, and brand analysis can be used for achieving this.

What Are Some Metrics to Consider?

Once you’ve collected the data, the next critical step is focusing on the KPIs. Product metrics can be broadly categorised into the following areas:

• User Engagement Metrics: Measure how users interact with your product. Examples: Active users, session duration, feature usage.
• Customer Satisfaction Metrics: Assess how happy your customers are with your product. Examples: Net Promoter Score (NPS), customer reviews, support tickets.
• Retention and Churn Metrics: Track how well your product retains users and how many are leaving. Examples: Retention rate, churn rate, repeat usage frequency.
• Monetisation Metrics: Focus on the financial aspects of the product. Examples: Customer Acquisition Cost (CAC), Lifetime Value (LTV), Monthly Recurring Revenue (MRR).
• Product Performance Metrics: Evaluate how efficiently your product is functioning. Examples: Load times, bounce rates, error rates.
• Growth Metrics: Help gauge the overall growth of your product in terms of user base and market reach. Examples: User growth rate, market share, feature adoption rate.

Each of these categories provides a comprehensive view of your product’s health and helps guide decision-making toward continuous improvement.

Some Product Analytics Techniques Widely Used

Using different ways to analyse data is key to discovering valuable insights. These techniques help you spot trends, understand what users really want, and identify areas for improvement. Here are a few popular methods of data analysis that can make a significant difference:

1. Funnel Analysis Funnel analysis tracks the user journey through a series of steps that lead to a desired outcome, such as a purchase, sign-up, or feature use. It helps identify where users drop off in the conversion process. Example: In an e-commerce app, the funnel might be: Homepage visit → Product search → Add to cart → Checkout → Purchase. If many users drop off at the checkout step, this indicates a bottleneck that needs to be addressed.
2. Churn Analysis Churn analysis focuses on understanding why users stop using the product or cancel their subscriptions (i.e., why they "churn"). It helps identify the reasons behind user disengagement and develop strategies to retain users and reduce churn rates. Example: By analysing user behaviour before churn, such as fewer logins or decreased feature usage, product managers can spot early warning signs and implement interventions, like personalised offers or targeted campaigns, to retain users.
3. A/B Testing A/B testing (also known as split testing) is an experimental method where two or more versions of a feature, design, or content are shown to different user groups to determine which version performs better based on a specific goal (such as click-through rate or conversion rate). It allows you to make data-driven decisions by comparing user engagement and behavior across different variations. This ensures that changes improve user experience and business outcomes. Example: If a product team wants to improve the sign-up rate, they might A/B test two versions of the sign-up button (e.g., changing the color or text) to see which version leads to more conversions. After running the test and analyzing the results, they can confidently implement the better-performing version.
4. Retention Analysis Retention analysis focuses on how often users return to use the product over time. It helps to understand how effectively the product is engaging users and whether it’s creating long-term value. Example: If a product has a high initial sign-up rate but poor retention, it means users are not finding value over time. Retention analysis can show whether users come back after one day, one week, or one month.
5. Cohort Analysis Cohort analysis segments users into groups based on shared characteristics or behaviors within a specific time frame (e.g., users who signed up in the same week or month). It allows you to compare how different cohorts behave over time, helping you understand trends and patterns in user engagement and retention. Example: You can analyze the retention of users who joined in January versus those who joined in March to see if changes made during that period impacted user engagement.

Some Tools for Product Analytics

• Pendo : A powerful product analytics tool that helps you understand how users interact with your product, collect valuable feedback, and guide them with in-app tips to boost engagement and satisfaction.
• Mixpanel : Provides advanced analytics to monitor and understand user interactions and behaviours within your product.
• PostHog : An open-source product analytics platform that gives you a clear view of how users are using your product, helping you improve experiences and keep them coming back—all with full control over your data.
• Google Analytics : Provides a snapshot of how users engage with your site, helping you spot trends, track behaviour, and optimise for better experiences.

Best Practices for Data-Driven Product Management

A data-driven product management approach involves several key strategies for creating scalable and accessible offerings. These practices include:

• Conducting Market, Customer, and Competitor Research: It’s vital for product managers to grasp market dynamics and customer needs to effectively develop data products.
• Creating User Personas and Customer Profiles: By building detailed user personas, product managers can gain deeper insights into their target audience, allowing them to tailor products to meet specific needs.
• Establishing Metrics and Setting KPIs and OKRs: Defining clear objectives for the product team requires access to reliable data and the skills to analyse it. A strong understanding of data is crucial for product managers in this process.

Conclusion

In today’s fast-moving market, making data-driven decisions is crucial. By gathering and analysing the right information, you can create products that truly connect with users, improve continuously, and outperform competitors. Use these insights to enhance your product management approach and drive success.

References

• The analytics alchemy : Transforming product through data
• Data-Driven Product Management: Using Analytics To Make Better Product Decisions
• Product Analysis 101: Expert Insights and Tips
• What is Data Driven Product Management?
• Google