Understand What is Product Analytics

Manik Chhabra • Last Modified: December 29th, 2022

Understanding Product Analytics

Modern businesses now track almost every operation that a user performs on any of their products along with any action they take that involves their product in some way. The engagement of users with the product starts being tracked the moment they view the first advertisement and is tracked until they stop using the product completely. This results in a huge volume of data being collected. Product Analytics and Data Analytics are applied on data to analyze how to optimize the operations to generate bigger returns.

Data-driven decision-making is a key factor that is responsible for the success of most businesses today. This is due to the fact that the data based on which all decisions are made is collected directly from the users and is hence considered to be accurate. Various forms of analytics such as Marketing, Business, or Product Analytics are applied to this data to understand the root cause for anything that happens in various stages of user engagement and product growth.

This article will help you understand what Product Analytics is, why it’s important, the various Metric Frameworks along with the best Product Analytics tools available in the market.

Table of Contents

What is Product Analytics?

Product Analytics
Image Source: https://medium.com/stotle-inc/product-analytics-strategies-to-boost-the-roi-of-your-analytics-app-26035ba355d5

Product Analytics can be seen as the process of tracking user engagement to assess the performance of the digital experience of your product. This is primarily done to understand if users are facing any challenges while using the existing features of your product and what new features your product requires to make the user experience better. The goal of Product Analytics is to take an evidence-based approach to ensure that the product being built by any business has high User Engagement and a low Churn Rate. 

Product Analytics should ideally help any business answer the following 6 questions which are also referred to as the 5W1H approach.

  • Who’s using the product?
  • When is the product being used?
  • What features of the product are being used?
  • What percentage of time is the product used to accomplish a task as compared to the competition?
  • What are the attributes and behavioral traits of the users who are most and least likely to recommend the product to their peers?
  • How is the product being used?

If any analysis that is performed on the product is not able to answer all the six questions stated above, it indicates that the correct User Engagement metrics are not being tracked and it might lead to incorrect or poor insights. This can lead to wrong implementations and updates which can cause customers to stop using your product and switch to your competitors. 

Most modern businesses now use the insights from Product Analytics as a primary decision-making factor. Some examples of these businesses are as follows:

  • Netflix analyzes usage and viewership data to decide which movies and TV shows they should invest in for further releases. Since the next releases are decided on what kind of content most people like watching, it leads to high User Engagement since users tend to come back to view similar content if they come across something that they liked previously. 
  • Spotify uses behavioral and usage data to understand what kind of music most people listen to. This allows them to make sure that they introduce the right set of artists on their platform. If this analysis was not performed, Spotify would either have to randomly introduce artists to their platform based on guesses or try to introduce as many artists as they possibly can. Both approaches would lead to high resource utilization with no guarantee of return. Proper implementation of insights from Product Analytics allowed them to ensure that their investment would ensure high User Engagement.
  • Amazon performed an analysis of what products users buy or view on a regular basis. This data was then used to introduce a “Subscribe and Save” feature which allowed users to subscribe to automatic monthly orders of a product that they regularly use at some discount for each order. This allowed users to save money on products they buy regularly and Amazon to ensure some revenue that they will definitely get from these subscribed products. 

Simplify ETL Using Hevo’s No-code Data Pipeline

Hevo is a No-code Data Pipeline that offers a fully-managed solution to set up data integration from 100+ data sources and will let you directly load data to a Data Warehouse or the destination of your choice. It will automate your data flow in minutes without writing any line of code. Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully-automated solution to manage data in real-time and always have analysis-ready data.

Let’s Look at Some Salient Features of Hevo:

  • Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
  • Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer. 
  • Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.
  • Schema Management: Hevo can automatically detect the schema of the incoming data and maps it to the destination schema.
  • Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within pipelines.
  • Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

Explore more about Hevo by signing up for the 14-day trial today!

Importance of Product Analytics

Product Analytics allows organizations to carry out a comprehensive analysis and gain holistic insights into their users and their requirements. Since businesses are now able to understand the needs of their users in a better way, updates are rolled out accordingly to ensure those needs are met. A recent report based on a study by Forbes magazine revealed that businesses that rely on data-driven decision-making are six times more likely to be profitable and that almost 76% of the businesses feel that proper use of Product Analytics lead to increased user engagement and 53% believed that their businesses became more consumer-centric.

Product users aren’t always considered to be the most reliable narrators and hence, qualitative methods of feedback such as surveys, support tickets, etc. don’t always give a clear idea of their needs. These qualitative methods can be used to understand the most recent issues but not the ones that are causing the most amount of concern or Churn amongst customers.

Instead of relying on qualitative data, Product Analytics relies on quantitative data. It processes huge volumes of data that is collected from all its users and can help businesses understand the patterns behind customer behavior and actions instead of individual perceptions.

Teams Using Product Analytics

The various teams in any organization that rely on Product Analytics are as follows:

1) Product Management Team

Product Analytics allows Product Managers to understand how customers are using their products and allows them to make data-driven decisions when building new features needed by customers or running experiments.

2) Marketing Team

Product Analytics allows the Marketing Team to understand which sources bring in the most traffic and which Marketing Campaigns bring in visitors that are the most likely to convert to customers.

3) Development Team Leaders

Allows them to understand which features in their product have bugs that have to be fixed and which features have to be improved to make the User Experience better.

4) UX Design Team

Product Analytics allows the UX Design Team to understand what elements in the User Experience are popular and what customers find confusing. This can allow them to identify key points of abandonments and introduce updates accordingly.

5) Growth Managers

Product Analytics gives them a full overview of User Engagement allowing them to formulate User Retention strategies accordingly.

Product Analytics Metric Frameworks

There are a lot of metrics that businesses can track. These metrics could be Monthly Recurring Revenue (MRR), Lifetime Value (LTV), Customer Acquisition Cost (CAC), etc. These metrics however alone are not enough since they cannot give you an idea of how they contribute to the value of your product. These metrics should ideally be a part of a Framework. These Frameworks are used to give context to your metrics.

The 3 most widely used Frameworks are as follows:

1) Pirate Metrics (AARRR)

This Framework states that all metrics that are defined should be a reflection of the user experience across one of the five following categories:

  • Acquisition: People using your product for the first time.
  • Activation: Users having a valuable experience with your product.
  • Retention: Users coming back to use your product again.
  • Referral: Users referring your product to their peers.
  • Revenue: Users taking any action that can generate revenue for your business.

The Pirate Metrics for a Music Streaming Service will be as follows:

Pirate Metrics Example
Image Source: https://amplitude.com/product-analytics

2) Google HEART Framework

The Google HEART Framework states that all metrics that are defined should be a reflection of the user experience in one of the five following categories:

  • Happiness: Users feel good about using your product.
  • Engagement: Meaningful user interaction takes place with your product.
  • Adoption: Users trying out new features or new users signing up.
  • Retention: Users come back to use the product on a regular basis.
  • Task Success: Users are able to complete key tasks without facing any issues.

These categories are usually tied to various goals and metrics are defined to measure how well those goals are being achieved.

The Google HEART Framework for a Music Streaming Service is as follows:

Google HEART Framework Example
Image Source: https://amplitude.com/product-analytics

3) North Star Framework

In the North Star Framework, the entire analysis is based on one key metric known as the North Star Metric. This metric should incorporate three aspects as follows:

  • A measure of user value.
  • A representation of the product strategy.
  • An indicator of change in revenue.

For example, the North Star Metric for Netflix is the Monthly Revenue. The Monthly Revenue indicates that the users like the product enough to continue using it, is a direct measure of the company’s revenue, and is at the core of their product strategy which is to ensure growth in viewership.

How do you use a Product Analytics Platform?

Many Product Analytics Platforms are available in the market, and good platforms offer a plethora of features that allow users to drill deep down into data. Learning all the features and tools of a Product Analytics Platform is easier than it sounds, but still, one should know the capabilities of a Product Analytics Platform. The following features of Product Analytics Platform that one should use to analyze data effectively are listed below:

  • Segmentation: It helps in segmenting customers based on different metrics. It can be demographics, reward points, conversion rate, etc.
  • Tracking: It helps in monitoring the customer behavior on the apps and websites and gathering relevant customer data.
  • Profiles: This features lets you set up user categories around different metrics of choice.
  • Notifications: It helps in sending and receiving alerts to Marketing teams, Testing teams, etc., allowing users to easily communicate and stay updated.
  • Funnels: It helps in exploring different paths to conversion.
  • Dashboards: It helps users to visualize data in a meaningful way and generate insights from it.
  • Measurement Tools: It allows users to evaluate every feature’s user engagement.

How do Product Analytics platforms work?

A Product Analytics Platform tracks the actions of the user on websites and apps such as tracking their clicks, searches, form fills, time spent on a page, and other activities. A good Product Analytics Platform offers the following features listed below:

  • Automatic Data Capture: Automatically capturing user data from websites and apps helps in automating the task. Manually performing such tasks are cumbersome.
  • Data Governance: It is an important component of any Product Analytics Platform. Data Governance ensures that the data is safe and clean. The role-based access lets users access data that is needed for them and provide you control over who views and modifies your dataset.
  • Integrations: Connecting the Product Analytics Platform with other external platforms such as CRMs, ERPs, Data Warehouses, etc., provides greater flexibility to users. It helps in making data available for other use cases.
  • Data Warehousing: It is an important feature of the Product Analytics Platform as it allows users to store huge volumes of data in a clean and analysis-ready form. 
  • Cohort Analysis: It helps in monitoring how many users are returning at a specific time. Keeping customers for the long term is more important than acquiring more customers. With the help of behavioral analysis, one can analyze how the customers react to the products. 

When Should My Company Invest in Product Analytics?

Your company is required to invest in Product Analytics when you feel to improve user experience and be concerned about the users who visit your website. Though it also depends on the type of company you have.

Small scale companies require Product Analytics to run tests on the market and figure the optimal offerings they should provide.

Mid-scale companies require a Product Analytics platform to scale their business and manage their value chain, increase conversion rate and retention with lower churn rate.

Large scale companies need a Product Analytics platform to stay in the competition and ensure not taken over by young companies. They require it for constant growth and development in business.

Product Analytics platform automates some of the tedious tasks such as data auto-capture that does all the hard work for data collection. But the real value of data is determined by how you use it, how often you use it. To get maximum out of your data you need to incorporate through processes in your organization.

  • Setting KPIs: It is essential to define your metrics for business that help you monitor and understand businesses with different metrics.
  • Setting Targets: It is important to not only hit the targets but also to know where to aim them. Product Analytics can help you figure out the right targets for your business and help in achieving them.
  • Open to Explore: Standard Analytics tools require lots of manual work. Heap tracks all the data and store it in an organized way that allows users to ask any question at any point in the process.
  • Pin-Point Objectives: To increase efficiency and performance, you need to be specific for every objective. Achieve all the sub-objectives in the general objective to be more productive.

Product Analytics Implementation

The implementation of your Product Analytics strategy should be continuously evolving to accommodate every new insight, business goal, feature or change to the product, sales target, or any new idea that is being experimented. Your analysis strategy has to be reviewed regularly to update various goals, metrics or frameworks, and reports.

All these changes in the analysis strategies can be easily accommodated if a good tool that can give you enough flexibility around the metrics being tracked is used for analysis. Also, most tools nowadays have features that can perform all the background tasks required to track metrics on their own without you having to code anything manually. It is recommended that these tools are used since they can fast track the implementation of your analysis strategy and help your business save large amount of engineering resources.

Top 3 Product Analytics Tools

Some of the most well-known Product Analytics Tools are as follows:

1) Google Analytics

Google Analytics Logo
Image Source: https://commons.wikimedia.org/wiki/File:Logo_Google_Analytics.svg

Although Google Analytics was built for Digital Marketing purposes, Google has added various features that have allowed Product Managers to use this platform for Product Analytics as well since it can help businesses get valuable insights on user interaction with their websites. A major advantage of using Google Analytics is easy integration with other Google products such as Google Data Studio, Google BigQuery, etc. Over 65,000 companies including Slack, Uber, Airbnb, Spotify, etc. use Google Analytics.

Google Analytics Pricing

Google Analytics offers a free tier with limited functionality that is considered to be suitable for small to medium-sized businesses and a paid tier with advanced functionality called Google Analytics 360 which is considered to be suitable for large enterprises.

Google Analytics Pricing
Image Source: https://marketingplatform.google.com/about/analytics/compare/

Google does follow a transparent pricing model for its Google Analytics 360 tier. The final price can be decided after a discussion with the Sales Team at Google.

More information on Google Analytics can be found here and its pricing can be found here.

2) Amplitude Analytics

Amplitude Logo
Image Source: https://www.mparticle.com/integrations/category/analytics

Amplitude is a Product Intelligence platform that allows Product Managers to ensure a great user experience for all their customers. Amplitude can be used to measure KPIs in an in-depth format and present comprehensive analysis in a simple-to-understand format to all its users. Amplitude is used by a large number of Fortune 500 companies including NBC, Twitter, PayPal, Microsoft, etc.

Amplitude Analytics Pricing

Amplitude offers 2 paid tiers i.e. Growth and Enterprise along with its free tier. The various features offered by these tiers are as follows:

Amplitude Pricing
Image Source: https://amplitude.com/pricing

Amplitude also doesn’t follow a transparent pricing model for its paid tiers and the final price depends on the business and data requirements.

More information on Amplitude can be found here and its pricing can be found here.

3) Adobe Analytics

Adobe Analytics Logo
Image Source: https://www.iubenda.com/en/help/24525-adobe-analytics-gdpr-how-to-be-compliant

Adobe Analytics is considered to be one of the most mature Product Analytics tools that is available in the market. It offers a large number of features and also gives users the ability to implement their own custom features. Adobe Analytics is considered to be used by more advanced users. One significant disadvantage of using Adobe Analytics is that it does not have the best user interface.

Adobe Analytics Pricing

Adobe Analytics offers 3 tiers i.e. Select, Prime and Ultimate. One of these tiers can be selected based on the requirements of the analysis and the business.

Adobe Analytics Pricing
Image Source: https://www.adobe.com/in/analytics/compare-adobe-analytics-packages.html

Adobe Analytics does not follow a transparent pricing model for any of its tiers and the final price depends on the business and data requirements.

More information on Adobe Analytics can be found here and an in-depth comparison of the features offered in each tier can be found here.


This article provided you with an in-depth understanding of what Product Analytics is, why it’s important, and what are the various Frameworks that can be used for your analysis. It also provided you with a list of the most well-known tools that can be used for Product Analytics by your business.

It is a well-known fact that most businesses now rely on data-driven decision-making to plan their future strategies. However, businesses also use various platforms to collect data on user engagement, marketing data, etc. This results in a complex situation since a common analysis cannot be performed unless the data from all these platforms is integrated and stored in a centralized location. Data integration can be a complex task if done manually. Businesses can instead use automatic data integration platforms like Hevo.

Details on Hevo’s pricing can be found here. Give Hevo a try by signing up for the 14-day free trial today.

No-code Data Pipeline For Your Data Warehouse