Prateek Majumder — Published On December 21, 2022 and Last Modified On December 22nd, 2022
Beginner Business Analytics Business Intelligence Data Analysis

This article was published as a part of the Data Science Blogathon.


Software Products can be very complex to manage and, at the same time, must be relevant to the customers. An essential part of that process is understanding how the customers use the product.

People who create products care about more than just revenue; they care about what is best for all customers. It’s critical to keep an eye on a product to ensure it’s meeting the needs of the end users.

Analyzing a product keeps the product connected to the user. It provides the data required to understand how people use a product, where it may fall short, and how to identify opportunities for improvement. The result is a market-ready product that reaches the intended market and achieves the desired results.

Product Analytics


This is where Product Analytics comes into play. Product analytics helps product managers and other senior management get an overview of the product’s functioning and understand how it works. Product Analytics is an essential topic for  product managers, product analysts, data analysts, etc. Here, we discuss some important questions and answers related to product analytics.

1. What is Product Analytics?

The process of analyzing how users interact with a product or service is known as product analytics. Product analytics is used by businesses to track, analyze, and visualize the user experience. This allows teams to optimize product features based on user data and feedback rather than gut feelings and hunches.

For example, A food delivery company like Zomato might want to understand which customers search for the word “Pizza” most or which people are more likely to order if they are given a coupon. They might want to understand how the App’s users interact with it and how the product team is progressing with features and upgrades. All this can be achieved with Product Analytics.

2. What is the Importance of Product Analytics?

Analytics is a great tool for product teams, as they can transform their ability to develop ideas and design user experiences. It allows them to imagine and design a product with features that meet prospects’ needs and then launch that product with metrics in place to analyze its usage and service experience.

Product analysis is important for optimization, diagnosis, correlation, and ensuring everyone is on the same page because products are complex and frequently require many decisions to be made by many people.

3. What are Some Product Analytics Tools?

A Product team needs product analytics tools to understand and analyze the data. The tools will have many features that will help the product analysis team. There are many important product analytics tools. Some of these tools include Google Analytics, Intercom, Heap Analytics, and Mixpanel.

4. What is Trends Analysis?

Trend analysis is a strategy for forecasting the future based on historical data. It allows you to compare data points over time to identify uptrends, downtrends, and stagnation.

It lets businesses see whether feature adoption increases or decreases over time. UX Designers can zoom in on specific features and track changes in user configuration over time. Product teams can identify the most and least used product features, as well as how the usage of each feature compares to the past.

Product Analytics


For example, in a Taxi Application, if management can see that not many people are using a 7-seater Taxi, they can give a limited amount of time and resources for maintaining that.

5. What is A/B Testing?

A/B testing is a product experiment in which you divide your audience and test a variety of product screens and features to see which performs best. In other words, you can show version A to half of your audience and version B to the other. Split testing is another name for it. A/B testing is helpful because different audiences behave differently. Something that works for one company might not work for another.

To conduct an A/B test, we must create two distinct versions of a single feature, each with a variable changed. Then you’ll show these two versions to two similar-sized audiences and see which one performed better over a specific period (long enough to make accurate conclusions about your results). A/B testing allows product managers to see how one version of marketing content compares to another.

A/B Testing


For example, in a food delivery app, a product manager might want to check if placing various payment orders in different orders can lead to cancellations. Cash on delivery orders is more likely to get canceled. So, the product team can try this experiment by placing them in different orders.

6. What is Attribution Analysis?

Successful customers can be a great source of insights. Product teams can use the Attribution Analysis to pinpoint the touchpoints most responsible for their success. Utilizing the user flow data, attribution analysis focuses on the users who have finished their journey and does a reverse touch analysis on their touches.

For instance, a product manager can analyze the revenue generated by the most recent attempts to use premium features before conversion. The most beneficial aspects of a product can often be determined by looking at customers who use it more frequently.

7. What is a Product Tree?

Product management can be visually gamified with a product tree. The arrangement of the concepts and materials resembles a tree with all of its leaves, with the branches and trunk standing in for various concepts and the order in which they should be prioritized.

Like a tree’s roots nourish it, a product’s roots stand for the materials and technical specifications necessary for the product to function. The branches’ leaves represent many thoughts applied to the function while the branches’ main functions are carried out.

The project hierarchy, features, and ideas that must be worked on to finish a project are shown in the product tree.

8. What is Cohort Analysis?

In addition to many other considerations, a company’s product messaging, website design, documentation, onboarding process, brand awareness, and product complexity will change as it grows. These changes will significantly impact every cohort’s perception of the product or services. To show how this perception is evolving, product metrics should be segmented by cohorts.

An older cohort that first used the product when it was simpler may outperform a newer cohort that just started using it. As opposed to the previous cohort, the new cohort won’t see a modest and steady rise in product complexity.

Additionally, there are numerous ways to define cohorts. A cohort can be identified when they visit the website for the first time, register for the service, speak with a salesman, or upgrade to a paid subscription. Every cohort analysis should have a different, dynamic cohort definition.

Product Analytics


A product manager, for instance, can see user signups split by the date that each user first signed up for the product.

9. How Can The Marketing Team Use Product Analytics?

Marketing teams can use Product Metrics to run marketing campaigns in a better way. They can:

  • Calculate the conversion rates for offers made in-product.
  • View the impact of page load times, design, and other UX elements on in-product purchases.
  • Test various CTA and marketing copy positions.
  • Determine the features that clients find most enticing so you can use that information in external marketing campaigns.

10. What Are Daily Active Users and Monthly Active Users? What is the importance of the DAU/MAU Ratio?

The Daily Active Users (DAU) metric counts the number of users who actively use your app daily. For instance, the DAU for a single user would still only be one, even if they launched your program several times daily. This gives you a precise estimate of the number of unique daily visits you receive.

The number of unique users who use your software within 30 days is measured by Monthly Active Users (MAU).

The proportion of monthly active users who use your app in a single day is measured by the DAU to MAU ratio, often known as stickiness. Users interact with your app on average 6 times out of every 30 days if your DAU/MAU ratio is 20%.

An app can be more successful in terms of usage if it has 10,000 downloads and 1500 active users than if it has 50,000 downloads and 100 active users.


A successful digital customer experience can differentiate a company’s success and failure. Understanding who your customers are, what they want, and how to meet their needs is the first step in creating that experience.

To Conclude:

  • Product Analytics is essential to the entire Data Driven decision-making process to make a better product.
  • We discussed some important Product Analytics related Questions and Answers.
  • Access to product analytics is the most effective and trustworthy approach to gathering important information about customer journey maps, funnel analysis, user segmentation, and other topics.

The case for product analytics is highly compelling since it allows you to understand how users interact with your product, find “Eureka” moments and the behaviors of your top customers, and integrate with marketing data and business information.

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About the Author

Prateek Majumder

Prateek is a final year engineering student from Institute of Engineering and Management, Kolkata. He likes to code, study about analytics and Data Science and watch Science Fiction movies. His favourite Sci-Fi franchise is Star Wars. He is also an active Kaggler and part of many student communities in College.

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