Monitor your product’s performance with product analytics

From humans to cars, checkups are a good thing, helping you catch problems early and stay on track. And products are no different. Regular assessment helps businesses understand how users interact with their offerings — whether they love them, hate them, or there’s room for improvement. 

This proactive approach to product management not only results in better products. It also drives growth. By using data to make smart decisions instead of metaphorically throwing paint at the wall, companies can ensure their products sell well and offer more than the competition. 

What is product analytics?

Product analytics refers to the process of examining how users engage with a digital product, be it a website, a mobile app, or software. 

By collecting and analyzing user data, you get direct insight into how people use a product, which features they find most valuable, and where they encounter issues or bottlenecks. 

This analysis helps product teams understand what works well and what doesn’t. This, in turn, helps them make informed decisions to improve the user experience and ultimately increase retention and revenue.

Product analytics vs. data analytics vs. business intelligence

Product analytics focuses specifically on how users interact with a product. It aims to improve product design and user experience.

Data analytics is a broader field that involves analyzing raw data from across the whole business to find patterns and draw conclusions. It could include everything from customer and operational data to social media activity and beyond. The goal is to uncover useful insights that help guide business decisions across a variety of domains.

And last but not least — BI focuses on current and historical data to inform strategic and tactical decisions. Where data analytics predicts future trends using complex analysis, business intelligence uses operational data for immediate decisions. BI is user-friendly and descriptive; analytics is technical and predictive, catering to experts.

Here’s a table so you can compare them side by side. 

Aspect	Product analytics	Data analytics	Business intelligence
Focus	User interactions with a product	General analysis of data to find trends and answer questions	Analyzing business information for strategic decision-making
Primary users	Product managers, UX/UI designers, developers	Data scientists, business analysts	Executives, business leaders
Data type	Behavioral data (clicks, views, interactions)	Varied, including quantitative and qualitative data	Operational, financial, and transactional data
Objective	Improve product design, user experience, and engagement	Discover insights, inform decisions across various domains	Inform strategic and tactical business decisions
Scope	Specific to a product’s user interactions and performance	Broad, across multiple domains not limited to product development	Comprehensive view of business performance

Why is product analytics important?

Is it important to create great products customers love? Of course it is! This is what product analysis is all about.  

1. Data-driven decisions

In an environment chock-full of opinions, product analytics offers a solid foundation on which to base product decisions. This eliminates guesswork and helps teams make informed choices.

2. User-centric product development

The customer sits at the heart of everything you do. By analyzing how users interact with a product, you can identify which features are most loved, which ones are loathed, and where users face challenges. This helps you create a more user-centric product that meets the needs and expectations of its audience.

3. Improved user retention

Understanding the user journey means you know exactly what they’re struggling with. Once you know this, you can tackle the friction points that lead to churn. By continually refining the product based on analytics, companies can boost user satisfaction and loyalty — and maybe even attract customers from competitors. 

4. Resource optimization

Product analytics you prioritize product features and improvements based on their impact on user engagement and satisfaction. This means resources are allocated efficiently, focusing on areas that offer the highest return on investment.

5. Competitive advantage

In competitive markets, the ability to quickly adapt and respond to user needs is a big advantage. Product analytics offers real-time insights that can help you stay ahead by rapidly iterating based on user feedback and behavior.

6. Increased revenue

By creating products users adore, companies will see a direct impact on their bottom line. Whether through increased user engagement, higher conversion rates, or improved retention, the insights gained from product analytics can be your golden ticket to long-term success.

Who is product analytics for?

Product analytics is for a range of roles within an organization, including product managers, UX/UI designers, developers, marketing teams, and executives. 

Product managers use it to measure the success of features and make strategic decisions. Designers rely on it to assess user interaction and improve the user experience. Developers look at performance metrics to optimize app functionality. Marketing teams analyze user engagement to refine campaigns. Executives view overall product health and growth to inform business strategies. 

Essentially, anyone involved in the product lifecycle can benefit from insights provided by product analytics.

What is empathy debt, and who is it for?

Before we move onto the ‘how’ section, let’s take a moment to explore an important concept: empathy debt. 

Empathy debt is a product development term that refers to the gap between the product team’s understanding of user needs and the actual experiences and challenges users face. 

• Designing a bike for a fish = big empathy debt
• Designing a high-protein fish food = small empathy debt

This ‘debt’ accumulates when product decisions are made without adequately considering or understanding the user’s perspective. This leads to features, interfaces, or entire products that don’t meet user expectations or solve their problems effectively. And it’s surprisingly common: Around 95% of new products fail.

Empathy debt, and the minimizing of it is relevant for anyone involved in product creation and management, including product managers, designers, developers, and marketers.

• Product managers need to ensure the product roadmap aligns with user needs and solves real problems. Accumulated empathy debt can lead to prioritizing the wrong features or misjudging the market fit.
• Designers are responsible for creating intuitive and user-friendly interfaces. A lack of empathy can result in designs that are difficult to use.
• Developers implement the product vision through code. Without an understanding of the user’s perspective, they may overlook usability issues or create features that don’t align with user expectations.
• Marketers communicate the value of the product to potential users. A disconnect caused by empathy debt can lead to messaging that doesn’t resonate with the target audience.

Addressing empathy debt is central to product analysis. Minimizing it involves actively seeking out and integrating user feedback, conducting customer discovery, and fostering a culture of user empathy within the product team. By doing so, teams can reduce the gap between their perception and the user’s reality, leading to a more useful, user-friendly product.

Product analytics metrics to measure 

Now, let’s get into the specifics. When you get started with product analytics, you’ll want to choose some metrics that feed into your broader business goals. Here are some popular stats to track. 

• User engagement: Tracks how actively users interact with the product.
• Retention rate: Measures how many users return to the product over time.
• Churn rate: Calculates the percentage of users who stop using the product.
• Conversion rate: Assesses how effectively users complete desired actions.
• Feature adoption: Evaluates how users are utilizing new features.
• Session length and frequency: Gives insight into how often and how long users engage with the product.
• Active users (daily, weekly, monthly): Helps in understanding the product’s stickiness.
• User satisfaction (NPS, CSAT): Net Promoter Score (NPS) and Customer Satisfaction. Score (CSAT) are metrics used to gauge user satisfaction with the product. They provide direct feedback on what users like and dislike.
• Revenue metrics (MRR, ARR, LTV): For monetized products, metrics like Monthly Recurring revenue (MRR), Annual Recurring Revenue (ARR), and Lifetime Value (LTV) are crucial for understanding the financial performance and sustainability of the product.
• Acquisition sources: Tracks where users are coming from, helping to identify the most effective channels for user acquisition and marketing efforts.
• Event tracking: Specific actions users take within the product. This granular data helps in understanding user behavior and preferences in detail.
  

Different types of product analysis (and what each is for)

Product analysis is a blanket term for a variety of analysis approaches, all of which are designed to help you measure one or more of the above metrics. Here are the main contenders you’ll want on your radar. Insider tip: don’t limit yourself to one! 

1. Funnel analysis

Identifies where users drop off in a sequence of actions towards a goal, like hitting ‘buy’ or ‘subscribe.’ Knowing the various steps means you’re in a better position to optimize them. Use funnel diagrams to turn the data into something a bit more digestible. 

2. Segmentation analysis

Divides users into segments based on demographics, behavior, or custom events, providing insights for targeted strategies. Think of it as being like offering a bespoke experience to different groups rather than lumping them all together.

3. Cohort analysis

Segment users based on shared characteristics or behaviors over time, revealing how different groups engage with the product, as well as their long-term value. 

3. Path analysis

Shows the actual routes users take within your product, highlighting common journeys and potential navigation issues.

5. Retention analysis

Measures how long users continue to use the product, helping identify what keeps them hooked and what may cause them to leave. 

6. Churn analysis

Focuses on identifying patterns and predictors among users who stop using the product, offering insights into reducing user attrition.

7. Milestone analysis

Examines user behavior in relation to specific milestones within the product, such as completing a tutorial or achieving a certain level in a game, to understand what drives progress and engagement.

8. Conversion analysis

Looks at how effectively users are completing desired actions, like subscribing after a trial period or making in-app purchases, and identifies factors that influence conversion rates.

9. Customer experience analysis

Uses user feedback, support interactions, and user behavior data to gauge overall satisfaction and pinpoint areas for improvement in the user experience.

10. Personalized experience analysis

Evaluates the effectiveness of personalized content, features, or recommendations in engaging users. The goal is to optimize these personalized touchpoints for happier customers.

Examples of product analytics in action

Here are three ways people apply product analytics in a business setting.

• Funnel analysis in eCommerce: eCommerce sites use funnel analysis to understand the entire customer journey, from landing on the site to making a purchase. Analyzing where customers drop off helps teams spot barriers in the checkout process, as well as opportunities to fine-tune the website for higher conversions.

• Feature adoption in software applications: Software companies track how customers use new features to determine their value. Low adoption rates may indicate a feature is hard to find, difficult to use, or not meeting user needs, guiding further development and user education efforts.

• User segmentation in mobile apps: Mobile app developers use product analytics to segment their users by behavior, demographics, or device type. They then use this to inform personalized marketing campaigns, feature development prioritizing, and user interface adjustments for different segments.

How to grow your product analytics stack 

Your product analytics efforts should encompass a stack of tools and techniques. As your organization grows, your analytics needs will get more complex. Expanding your product analytics stack to keep pace with this growth is important. 

• Start with scalable tools: Early adoption of analytics tools that can scale with your growth ensures you won’t need to perform costly migrations or overhauls down the line.

• Integrate advanced analytics and BI tools: As your data volume and complexity increase, integrating more sophisticated analytics and business intelligence tools can help you maintain a comprehensive view of your product and business health.

• Leverage data warehousing: Implementing a data warehouse can centralize your analytics data, making it easier to perform complex queries and integrate data from multiple sources, thus providing a holistic view of your user behavior and product performance.

• Adopt machine learning and AI: Utilizing machine learning and artificial intelligence can help you predict user behavior, identify trends, and automate insights, allowing for proactive rather than reactive decision-making.

• Foster a data-driven culture: Ensuring that all teams have access to and understand how to use analytics tools encourages a data-driven approach to decision-making across the organization.

Tips for product analytics success

For product analytics to truly benefit your organization, consider these tips:

• Align analytics with business goals: Make sure the metrics you focus on directly contribute to your broader business objectives. This means the insights gained from your effort have a clear impact on strategic decisions.

• Invest in quality data: The insights you gain are only as good as the data you collect. Invest time in ensuring data accuracy, consistency, and completeness to build a reliable foundation.

• Encourage cross-functional collaboration: Product analytics should not be siloed within a single team. Encouraging collaboration between product, marketing, sales, and customer success teams can lead to a better understanding of user behavior and how it impacts the entire business.

• Iterate and learn: Use analytics as a tool for continuous learning and improvement. Experiment with new features, user flows, and strategies based on data-driven hypotheses, and use the results to shape future decisions.

• Balance quantitative with qualitative insights: While product analytics provides valuable quantitative data, complementing it with qualitative insights from user interviews, surveys, and feedback provides a complete picture of the user experience.

How do product analytics platforms work?

Product analytics platforms work by tracking the actions users take on your site, from every click to pageview and beyond. 

Once the data is collected, it’s processed and stored in a database. The platform then provides tools for querying and visualizing this data, allowing teams to analyze user behavior, conversion rates, feature adoption, and more.

These platforms often offer a range of functionalities, including:

• Real-time data analysis to monitor current user behavior and product performance
• Segmentation tools to analyze specific user groups based on demographics, behavior, or custom events
• Cohort analysis to track how groups of users engage with the product over time
• Funnel analysis to identify drop-off points in the user journey
• Heatmaps and session recordings to visually understand user interactions.

No matter what your goals are, you’ll need metrics that are relevant, actionable, and measurable. 

Make the task easier with tools built for the job. Project management software, like Backlog, can help you track projects and collate feedback. Meanwhile, dedicated diagramming tools, like Cacoo, are great for creating wireframes, user journey flows, and process improvement plans you can share with your team. Incorporating the right tools not only streamlines the process but also keeps your team aligned and focused on the metrics that matter most, driving your project toward success.