Main features in Product Analytics
Posted: Mon Jan 20, 2025 3:26 am
Below we have selected some important elements to perform an efficient data analysis on your product. Check it out:
Event Tracking
Events are user interactions with the product , such as logging in and clicking a link or button.
Product Analytics tools automatically map all events and visits to your product , centralizing all data about the solution in dynamic dashboards . You can customize the way the information is displayed according to your strategies, defining which events you want to analyze.
Segmentation
For a more precise analysis, it is possible to segment users according to a multitude of criteria , such as demographic data, behavior (based on events performed by users), geographic data, time of use, time of entry into the platform, device used to log in (computer or mobile ), among others.
The choice of segmentation depends on what you portugal mobile phone number want to analyze and what the objectives involved in this process are. Always think about what you want to understand with this segmentation.
Real-time insights
Some data analysis tools update information automatically and allow you to have real-time data insights . In addition to viewing data in a personalized way according to your preferences, you can also generate reports to make more objective presentations to the team and stakeholders .
A/B testing analysis
In the process of validating product hypotheses , it is important to run A/B tests with users to understand their preferences, for example, in relation to specific features or some communication.
Testing consists of offering two options to two different groups of users and evaluating the performance of the alternatives. The one with the best performance should be implemented for the other users as well.
Product Analytics helps you collect and interpret the results of these tests to create a solution that is more aligned with customer needs.
Cohort Analysis
Cohort analysis involves segmenting a group of users based on some specific behavior and aims to find patterns.
To use this strategy, you need to define:
Groups to be studied (e.g. new users);
Metrics that will be used;
What period is considered?
Product metrics are fundamental in this approach, because they are what will indicate the pattern of user behavior over time.
This helps validate new product strategies (such as a new home page , new positioning, optimized onboarding , or different button design ) and understand how they impact user experience and satisfaction.
Integrations
For a more robust data analysis strategy, it is important to integrate Product Analytics systems with other tools, such as CRMs, customer service software and Marketing automation platforms.
The integration allows us to collect an even greater volume of data about users, which will be used for better analyses and to refine product strategies.
It is important to centralize information to optimize team productivity , as the team does not need to search for data in different sources. This is why integration between systems is so useful.
How to implement Product Analytics?
As we have seen, data analysis in Product is essential and a great approach for teams in the area and for the business in general. To implement this vision in your product culture, here are some tips on what you can't miss :
Have clear objectives for the business and the product
Effective data analysis needs to be tied to specific objectives . After all, how can you find relevant information if you don't know what you're looking for?
So, establish your business priorities , future goals, and metrics that should be analyzed. This way, when it comes time to evaluate your results, you can see if you are close to your goal and how your strategies are progressing.
Know and know how to apply Product metrics and frameworks
The Product area involves metrics and frameworks that facilitate the process of achieving established objectives – such as Heart (which associates metrics with user experience) and the AARRR Funnel (which analyzes data on acquisition, activation, retention, referral and revenue).
Know how to collect, organize and interpret data from different sources
No matter how efficient Product Analytics tools are, the Product team needs to understand and interpret the information collected by these platforms. Otherwise, the data will mean little.
Therefore, it is important that the team knows what they are looking for and defines a specific metric (even defining a North Star Metric ) to guide the analysis and evaluate the value of the solution in the users' perception.
Furthermore, based on the business objectives, link each of them to events that can be measured by Analytics tools . This makes it easier to organize the team and optimize analyses.
Master automation and data visualization tools
There are several Analytics tools on the market, such as: Mixpanel , Heap , Amplitude , FullStory and Google Analytics . The choice will depend on your strategy.
But regardless of which platform your team uses, you need to train people so that they can take advantage of all the benefits that the platforms bring to data analysis. In addition, the choice of tool also needs to be tied to the business objectives and offer the necessary features so that you can analyze important data according to your goals.
Structure a data-driven team
In addition to specific Product Analytics tools, it is essential to have people prepared to deal with data . Data analysis should be part of the team culture, but you can also count on specialist professionals to help structure and prepare a data-driven team , such as Data Product Managers and Business Analysts .
Master Product Analytics
PM3 is the leading school in Product and has already made the Product Analytics Course available . This is your chance to learn how to deal with all types of Product data, through in-depth classes with qualified professionals and real cases from Brazilian companies.
You will learn how to efficiently monitor data to support your decisions and manage stakeholders , gaining more confidence for your career.
Event Tracking
Events are user interactions with the product , such as logging in and clicking a link or button.
Product Analytics tools automatically map all events and visits to your product , centralizing all data about the solution in dynamic dashboards . You can customize the way the information is displayed according to your strategies, defining which events you want to analyze.
Segmentation
For a more precise analysis, it is possible to segment users according to a multitude of criteria , such as demographic data, behavior (based on events performed by users), geographic data, time of use, time of entry into the platform, device used to log in (computer or mobile ), among others.
The choice of segmentation depends on what you portugal mobile phone number want to analyze and what the objectives involved in this process are. Always think about what you want to understand with this segmentation.
Real-time insights
Some data analysis tools update information automatically and allow you to have real-time data insights . In addition to viewing data in a personalized way according to your preferences, you can also generate reports to make more objective presentations to the team and stakeholders .
A/B testing analysis
In the process of validating product hypotheses , it is important to run A/B tests with users to understand their preferences, for example, in relation to specific features or some communication.
Testing consists of offering two options to two different groups of users and evaluating the performance of the alternatives. The one with the best performance should be implemented for the other users as well.
Product Analytics helps you collect and interpret the results of these tests to create a solution that is more aligned with customer needs.
Cohort Analysis
Cohort analysis involves segmenting a group of users based on some specific behavior and aims to find patterns.
To use this strategy, you need to define:
Groups to be studied (e.g. new users);
Metrics that will be used;
What period is considered?
Product metrics are fundamental in this approach, because they are what will indicate the pattern of user behavior over time.
This helps validate new product strategies (such as a new home page , new positioning, optimized onboarding , or different button design ) and understand how they impact user experience and satisfaction.
Integrations
For a more robust data analysis strategy, it is important to integrate Product Analytics systems with other tools, such as CRMs, customer service software and Marketing automation platforms.
The integration allows us to collect an even greater volume of data about users, which will be used for better analyses and to refine product strategies.
It is important to centralize information to optimize team productivity , as the team does not need to search for data in different sources. This is why integration between systems is so useful.
How to implement Product Analytics?
As we have seen, data analysis in Product is essential and a great approach for teams in the area and for the business in general. To implement this vision in your product culture, here are some tips on what you can't miss :
Have clear objectives for the business and the product
Effective data analysis needs to be tied to specific objectives . After all, how can you find relevant information if you don't know what you're looking for?
So, establish your business priorities , future goals, and metrics that should be analyzed. This way, when it comes time to evaluate your results, you can see if you are close to your goal and how your strategies are progressing.
Know and know how to apply Product metrics and frameworks
The Product area involves metrics and frameworks that facilitate the process of achieving established objectives – such as Heart (which associates metrics with user experience) and the AARRR Funnel (which analyzes data on acquisition, activation, retention, referral and revenue).
Know how to collect, organize and interpret data from different sources
No matter how efficient Product Analytics tools are, the Product team needs to understand and interpret the information collected by these platforms. Otherwise, the data will mean little.
Therefore, it is important that the team knows what they are looking for and defines a specific metric (even defining a North Star Metric ) to guide the analysis and evaluate the value of the solution in the users' perception.
Furthermore, based on the business objectives, link each of them to events that can be measured by Analytics tools . This makes it easier to organize the team and optimize analyses.
Master automation and data visualization tools
There are several Analytics tools on the market, such as: Mixpanel , Heap , Amplitude , FullStory and Google Analytics . The choice will depend on your strategy.
But regardless of which platform your team uses, you need to train people so that they can take advantage of all the benefits that the platforms bring to data analysis. In addition, the choice of tool also needs to be tied to the business objectives and offer the necessary features so that you can analyze important data according to your goals.
Structure a data-driven team
In addition to specific Product Analytics tools, it is essential to have people prepared to deal with data . Data analysis should be part of the team culture, but you can also count on specialist professionals to help structure and prepare a data-driven team , such as Data Product Managers and Business Analysts .
Master Product Analytics
PM3 is the leading school in Product and has already made the Product Analytics Course available . This is your chance to learn how to deal with all types of Product data, through in-depth classes with qualified professionals and real cases from Brazilian companies.
You will learn how to efficiently monitor data to support your decisions and manage stakeholders , gaining more confidence for your career.