Top 11 Product Analytics Software

Product analytics software provide product managers and marketing teams detailed insights into customer behavior, user sessions, and user journeys to enable data-driven decision-making.

Check out top 10 product analytics software with their key and differentiating features.

Top 10 product analytics software

Sorting is based on the number of reviews gathered from B2B review platforms such as G2 and Capterra.

Differentiating features list for each vendor

*Please note that we only included features that are native to the product. Features provided with third-party integrations are not shown.

Google Analytics

Google Analytics focuses on built-in automation where users can quickly access insights from their Analytics data, forecast customer behavior, and utilize advanced modeling tools:

• Predictive analytics: By leveraging Google’s machine learning models, Analytics analyzes data to forecast potential user actions, such as making purchases or canceling subscriptions. Users can then create targeted audiences based on these predictions to enhance conversions or improve retention.
• Automated insights: Analytics automatically identifies and highlights important insights from the data, such as key changes, emerging trends, and growth opportunities that users should consider.
• Quick answers: Users can ask questions in natural language via the search bar to quickly find the metrics, reports, or insights they need.

Amplitude

Amplitude Analytics is a product analytics tool designed to help businesses track and understand user behavior in real time.

Amplitude provides AI powered features such as:

• Ask Amplitude: Allows users to instantly generate insights by asking plain-language questions, with large language models interpreting queries and creating visualizations.
• Data assistant: Automates data governance tasks like cleaning and organizing datasets.
• Intelligent monitoring: Uses machine learning to detect anomalies in product metrics and notifies teams of unexpected changes in user behavior.
• Predictions: Uses transformer-based models to forecast user behavior to help teams optimize campaigns and tailor strategies by segmenting users based on predicted actions.
• Personalized assistance: Simplifies complex analytics tasks, like cohort analysis or chart building, by offering AI-driven suggestions.

Amplitude’s Session Replay feature allows teams to visualize a user’s journey through a website or app by capturing interactions such as clicks, scrolls, and navigation. It recreates the experience in a video-like format, enabling businesses to see exactly how users engage with their product.

Figure 1: Amplitude’s session replay dashboard.

Pendo

Pendo AI enables identifying workflow challenges and leverages AI-driven recommendations to enhance user navigation.

The platform also provides insights into user behavior, highlights opportunities for improvement, and offers automatic feedback summaries. Additional features include data standardization, content localization, analysis of NPS data, and visual data replays to add context to user interactions and improve overall user experience.

Figure 2: Pendo product usage analytics dashboard.

Mixpanel

Mixpanel’s Spark AI, powered by OpenAI, allows users to interact with product data using natural language, without needing to know SQL or specific data events. Key features of Spark AI include:

• Natural language querying: Users can ask product, marketing, or revenue questions and get visualized data insights.
• Follow-up prompts: To refine analysis and enable follow up, users can as additional questions.
• Report transparency: Users can see how visualizations were created by viewing the underlying data.

Spark AI allows users to ask objective, data-driven questions, such as “how many videos were watched in the last month?” or “break down this data by country.” However, it does not handle subjective “why” questions, like “why are users purchasing fewer items this month?” Spark focuses on providing actionable insights from concrete data rather than interpreting user behavior or motivations.

Figure 3: Mixpanel’s conversational AI dashboard.

Heap

Heap’s CoPilot is designed to make analytics more accessible, regardless of a user’s experience level. It allows users to ask questions in plain language and provides analysis without the need for complex setup.

CoPilot also generates automatic chart summaries for sharing and suggests follow-up questions to help users explore data more deeply. CoPilot is supported by Heap’s documentation and best practices and helps simplifying the process of understanding and collaborating on data insights.

Figure 4: Chart summary generation by Heap CoPilot.

Smartlook

Smartlook’s cross-platform analytics enable organizations to track and analyze user behavior across web and mobile applications. It integrates data from multiple platforms to create a unified view of user interactions.

Cross-platform analytics includes session recordings, event tracking, funnel analysis, and heatmaps, to reveal how users navigate and interact with an application. The system ensures that each user’s journey is consistently identified across devices, which helps in understanding behavior patterns and optimizing user experience across platforms.

Figure 5: Smartlook click heatmap example.

Hotjar

Hotjar specializes in heatmaps that reveal user interactions such as movement, clicks, and scrolling. This data helps identify areas of friction and highlights elements that attract or miss user attention.

Hotjar also enables behavior analysis both before and after changes, with saved heatmaps and charts tracking click trends over time.

The platform facilitates design optimization across desktop, mobile, and tablet devices by comparing user behavior and presenting engagement patterns in various formats.

Additionally, Hotjar’s feedback widget allows users to leave quick feedback, generating an average feedback score for each page and aiding in continuous site improvement.

Figure 6: Hotjar’s heatmap creation dashboard.

Glassbox

Glassbox’s Insights Assistant, GIA, is an AI-powered tool that manages data analysis by capturing all user interactions and technical events on websites and mobile apps.

It provides instant and actionable insights through a chat-based interface that allows users to search and summarize sessions or reports quickly. GIA is designed to decentralize data access across teams, and simplifies understanding user behavior, locating friction points, and making data-informed decisions without requiring advanced technical skills.

Figure 7: Glassbox Insights Assistant dashboard.

Fullstory

Fullstory’s autocapture feature automatically collects user interaction data without manual tagging, which allows for comprehensive behavioral insights.

Autocapture leverages AI to capture every user action to help product teams identify issues and optimize experiences with tools such as heatmaps, session replays, and journey mapping. This helps reducing the need for manual monitoring and ensuring privacy by excluding sensitive data.

Moreover, Fullstory utilizes AI for its Metric Insights feature to deliver important insights on the data that matters. It automatically identifies notable trends or anomalies in KPIs, while presenting insights that allow businesses to quickly recognize and respond to interesting patterns or significant shifts.

Figure 8: Fullstory metrics insights with AI dashboard.

Userpilot

Userpilot’s no-code event tracking feature helps product teams collect data on user interactions within applications. This feature enables event tracking without needing custom code.

The Event Autocapture feature automatically records all user interactions, such as clicks and form submissions to provide a complete overview without manual tagging. No-code event tracking allows non-technical teams to customize which events to track while offering flexibility, real-time insights, and feature adoption metrics.

Figure 9: Userpilot’s trend analysis dashboard.

Quantum Metric

Built on Google Cloud Platform’s Gemini Pro model, Felix AI helps improve the process of analyzing customer sessions by summarizing them.

With Felix AI, Quantum Metric can reduce the need to watch lengthy session replays. It allows users to quickly identify and measure the impact of friction points across their audiences. These insights can be integrated into existing workflows through built-in tools or a RESTful API, to enable sharing and use of session summaries.

Felix AI also acts as a resource for teams to ask specific questions about user sessions, to provide targeted insights and support better decision-making.

Figure 10: Quantum Metric’s Felix AI summary dashboard.

What is product analytics?

Product analytics is essential for product and marketing teams to understand how users engage with digital products, including mobile apps and websites. By using product analytics tools, teams can track user interactions, analyze user behavior, and gather quantitative data on key metrics such as feature usage, customer retention, and user engagement.

With capabilities including event tracking, cohort analysis, session replay, and funnel analysis, product analytics tools give teams a complete understanding of how users navigate and interact with a product. This allows businesses to identify drop-off points, user needs, and customer pain points while driving improvements in the user experience.

Through path analysis, retention analysis, and impact analysis, product analytics software help teams optimize product performance and deliver personalized experiences that increase customer satisfaction.

Customizable dashboards and interactive reports from product analytics software offer actionable insights and enable businesses to measure product success and drive growth. These tools help teams interpret data across different devices and tech stacks, to ensure a comprehensive view of the customer journey.

Whether analyzing user behaviors or marketing strategies, these product analytics tools are critical for improving customer churn and delivering a smooth user experience. By leveraging deeper insights from machine learning and integrating with other tools, product teams can make data-driven decisions that enhance product usage and impact business performance.

Key features of product analytics software

Event tracking

Event tracking monitors specific user actions or interactions within a product. It helps businesses gain insights into how users engage with different features and areas of the product.

By collecting data on key events like clicks, page views, and purchases, businesses can analyze usage patterns and identify opportunities for product improvements or optimizations.

User segmentation

User segmentation involves categorizing users into distinct groups based on specific attributes, such as demographics or behavior. This allows businesses to tailor their marketing efforts and product experiences for different types of users.

Segmentation also helps in analyzing user behavior within each group, providing more targeted insights for growth strategies.

Funnel analysis

Funnel analysis examines the steps users take to complete specific goals, such as signing up or making a purchase.

By tracking the user journey through each stage, businesses can identify where users drop off and what problems they encounter. This analysis helps optimize processes and improve the overall conversion rate by addressing bottlenecks in the user experience.

Dashboards and reports

Dashboards and reports display key performance metrics and provide insights into product usage and engagement.

Dashboards present real-time data in a visual format for product teams to monitor performance, while reports offer more in-depth analyses of trends over time. Both tools are essential for keeping stakeholders informed and making data-driven decisions.

User behavior data analysis

User behavior data analysis focuses on understanding how users interact with a product over time by examining data from various sources like event tracking, heatmaps, or session recordings.

This analysis uncovers patterns in engagement and highlights areas where users may face challenges or where product improvements can be made to enhance the overall user experience.

Differentiating features of product analytics software

A/B testing

A/B testing, also known as split testing, is a method used in product analytics to compare two versions of a product, feature, or user experience.

A/B testing contributes to product analytics by allowing product teams to test different versions of a feature or change, measure user responses, and determine which version performs better. This helps optimize user experience, improve conversion rates, and reduce the risk of implementing changes that might negatively impact user behavior.

How it works?

• Version creation: The team creates two versions of a feature: the original (Version A) and a modified version (Version B).
• User segmentation: Users are randomly divided into two groups. One group is shown Version A, while the other group sees Version B. The distribution is typically equal, but it can vary depending on the testing goals.
• Data collection: The product analytics tool collects data on user interactions with both versions. This data can include metrics like click-through rates, time spent on a page, purchase completions, or any other key performance indicators (KPIs).
• Statistical analysis: The tool analyzes the data to identify whether there is a significant difference in the performance of Version A and Version B.
• Results interpretation: If one version shows statistically significant better results (e.g., higher conversions), the team can decide to roll out that version to all users.

Generative AI integrations

Generative AI integrations enable product analytics tools to help teams derive meaningful insights, automate data analysis, and improve decision-making processes. Here are some key integrations and applications of generative AI in product analytics:

• Natural Language Querying (NLQ): Users can ask questions in plain language and receive automatic insights and data visualizations.
• Automated insights: AI analyzes data patterns and generates summaries or recommendations for product improvements.
• Personalized recommendations: AI suggests product features or UX improvements based on user behavior and data.
• Automated reporting & data storytelling: AI generates automated reports with visualizations and narratives to summarize key findings.
• Prediction and scenario simulation: AI predicts outcomes and simulates scenarios to help decision-making based on product data.
• User behavior analysis and anomaly detection: AI identifies unusual user behavior and generates alerts or explanations for anomalies.
• Cohort analysis and segmentation: AI automatically creates user cohorts and suggests segmentations based on behavior patterns.
• User journey mapping and optimization: AI generates optimized user flows and suggests improvements for user journey efficiency.
• Conversational interfaces and AI chatbots: AI-powered chatbots gather feedback and summarize insights for product improvement.

Session replay/session recording

Session replay (also known as session recording) is a feature in product analytics tools that allows teams to capture and replay users’ interactions on a website or app. It records things like mouse movements, clicks, scrolling, form inputs, and page transitions. Here are the key components of session replay:

• Mouse movements and clicks: Tracks where the user moves their cursor and which buttons they click.
• Scrolling behavior: Shows how far down a page a user scrolls.
• Form inputs (with privacy considerations): Tracks how users interact with forms (although sensitive data like passwords are typically masked).
• Navigation paths: Tracks the series of pages or screens a user visits during their session.
• Device and browser environment: Captures details about the device, operating system, and browser the user is using, which can influence the user experience.

Session replay in product analytics is important for understanding how users interact with a product, revealing areas of difficulty, frustration, or confusion. It helps troubleshoot bugs by allowing teams to see exactly what users experienced while making it easier to replicate and fix issues. By identifying where users drop off or struggle, session replays help improve conversion rates and optimize design. They also validate user feedback, ensure compliance in regulated industries, and provide customer support teams with better context for resolving issues.

Heatmaps

In product analytics, heatmaps are visual tools that represent data with varying colors to show the frequency, intensity, or value of user interactions on a product’s interface. The colors typically range from cool (blues and greens) to warm (reds and yellows), where “hotter” colors indicate higher interaction levels, such as where users click, scroll, or spend the most time.

Here are different types of heatmaps in product analytics:

• Click heatmaps: Show where users click the most on a page. Areas with more clicks are represented by warmer colors.
• Scroll heatmaps: Indicate how far down users scroll on a page, showing which sections are getting the most and least engagement.
• Movement heatmaps: Track how users move their cursor across the screen, often used to infer eye movement and interest in different parts of the page.
• Attention heatmaps: Represent areas where users spend the most time reading or viewing, giving insight into the content that captures the most attention.

By understanding where users focus most of their attention, businesses can better position key content or features. Heatmaps can help identify dead zones or areas of the interface that users are ignoring, guiding design improvements.

When running A/B tests on different designs, heatmaps can provide qualitative data to validate quantitative results. They show how user behavior changes between variations to increase data driven decision making.

Automation

Product analytics can benefit from a wide range of automation types that streamline data collection, analysis, and insights generation:

Data collection automation

• Event tracking: Automatically tracking user interactions such as clicks, scrolls, form submissions, and navigation.
• Data pipelines: Automating the flow of raw data from multiple sources (e.g., apps, websites) into a centralized analytics platform.

Insight generation

• Automated reports and dashboards: Creating regularly updated dashboards and reports with key metrics (e.g., retention rates, user engagement).
• Anomaly detection: Using AI/ML algorithms to automatically detect unusual patterns or outliers in product usage data.
• Predictive analytics: Automating forecasts for user behavior trends, such as churn predictions or feature adoption likelihood.

User journey analysis

• Funnel analysis automation: Automatically tracking and updating conversion funnels to show how users progress through different steps of a process.
• Path analysis: Mapping user journeys to identify common paths users take within an app or website.

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