In the article “From Data to Insights: An Introduction to Product Analytics”, we walk you through the basics of product analytics, providing you with a high-level approach to get started with it.
This time, we’d like to dig in deeper to dissect every step of the product analytics process, assuming that the main goal is to improve user engagement and retention.
User engagement and retention are two of the most important metrics for any product, but they represent different aspects of a user's relationship with a product or service:
By leveraging product analytics, businesses can gain a deeper understanding of how users are interacting with their product and identify areas for improvement to increase user engagement and retention.
Here are some key steps businesses can take to improve user engagement and retention with product analytics:
The first step in using product analytics to improve user engagement and retention is to define the key metrics and goals that will be used to measure success. This will vary depending on the product and business objectives, but some common metrics include:
By setting specific goals for these metrics, businesses can track their progress over time and identify areas for improvement.
For example, let's say a mobile app company wants to increase user retention. They define their goal as increasing the percentage of users who return to the app within 7 days of their first use from 20% to 30%. This gives them a specific metric to track and a clear goal to work towards.
The next step is to collect data on user behavior within the product. This can be done using a tool like Countly.
Collecting data on user behavior is a crucial step in understanding how users are interacting with your product or service. This step allows you to identify areas where users may be struggling, areas where they are finding value, and potential opportunities for improvement. To collect data effectively, it's important to use a combination of quantitative and qualitative metrics.
Quantitative metrics are numerical data points that help measure user behavior in a more objective way. This can include metrics like the number of users, the number of daily active users (DAU), weekly active users (WAU), monthly active users (MAU), user retention rates, conversion rates, and more. These metrics give you a broad overview of how many people are using your product, how often they are using it, and how they are interacting with it over time.
Qualitative metrics, on the other hand, are more subjective and often require direct feedback from users. These can include user surveys, reviews, and user feedback. These types of metrics provide more detailed insights into user behavior, attitudes, and motivations. User feedback is particularly important because it helps you understand why users are behaving in certain ways and what they like or dislike about your product.
When collecting data on user behavior, it's important to gather information on user demographics such as age, gender, location, and any other relevant factors. This helps you identify any patterns or trends in user behavior based on these factors, which can help inform product improvements or target specific user groups with marketing campaigns.
Once you have collected data on user behavior, it's essential to analyze it to identify patterns and trends. Analyzing data helps you gain valuable insights into how users interact with your product or service and how you can improve the user experience.
One technique that is commonly used to analyze user behavior is cohort analysis. This involves grouping users based on a shared characteristic, such as the month they signed up or their geographic location, and analyzing their behavior over time. This can help you understand how different user groups behave and identify any trends or patterns that emerge.
Another technique is funnel analysis, which involves tracking user behavior through a series of steps or stages. This helps you understand where users drop off in the conversion process and identify any barriers to engagement or conversion. For example, you might track the steps a user takes to complete a purchase on an e-commerce site and identify any issues that prevent them from completing the process.
Segmentation is another useful technique for analyzing user behavior. This involves dividing users into groups based on common characteristics, such as demographics or behavior. This can help you target specific user groups with marketing campaigns or tailor the user experience to better meet their needs.
Regardless of the technique used, the goal of analyzing user data is to identify areas where users are dropping off or where engagement is low. For example, if you find that engagement drops off after the first month, you may want to investigate specific features or actions that users are not taking and make changes to improve the onboarding process or simplify the user interface.
After identifying patterns and trends in user behavior, it's important to test and implement changes to improve user engagement and retention. This step involves experimenting with different features or designs and measuring the impact of those changes on user behavior.
One common technique for testing changes is A/B testing. This involves creating two different versions of a feature or design and testing them with different groups of users to see which version performs better. For example, you might test two different versions of a landing page to see which one results in more sign-ups or purchases. A/B testing can help you make data-driven decisions about which changes to implement and which to discard.
Another approach is to make changes based on insights gained from data analysis. For example, if you notice that users are dropping off after the first few levels of a game, you might experiment with different difficulty levels or reward structures to see if this improves retention. This approach can be useful for making small, iterative changes that are based on a deep understanding of user behavior.
When testing and implementing changes, it's important to track the impact of those changes on user behavior. This can be done by analyzing again the quantitative metrics such as user engagement, retention rates, or conversion rates, as well as qualitative metrics such as user feedback or surveys. By measuring the impact of changes, you can iterate and improve your product or service over time.
Related topic: How to Benefit from A/B Testing on Mobile
Continuously monitoring and iterating on a product is a crucial step in ensuring its success. Even after a product has been launched, it's important to keep track of how it's performing and how users are engaging with it. This allows for changes to be made that will improve the user experience and increase retention.
One way to monitor a product is to collect data and track metrics over time. This data can be used to identify trends and patterns in user behavior and to measure the effectiveness of any changes that have been made to the product.
If changes are not having the desired impact, it's important to iterate and try new approaches. This might involve experimenting with different features, user interfaces, or marketing strategies. It's important to be open to feedback from users and to take their suggestions into account when making changes to the product.
As a closing note, here are 3 examples of successful product analytics strategies:
Spotify uses product analytics to personalize the user experience and improve user engagement. By collecting data on user listening habits and preferences, they are able to create custom playlists and recommendations for each user. This has helped to increase user engagement and retention, with over 320 million monthly active users as of 2021.
Slack is making use of product analytics to understand how users are interacting with the product and identify areas for improvement. For example, they noticed that users were struggling to find certain features within the app. They used this feedback to redesign the app and make it easier for users to navigate. This resulted in increased user engagement and a higher Net Promoter Score (NPS).
Airbnb uses product analytics to personalize the user experience and improve user retention. By collecting data on user preferences and behavior, they are able to recommend relevant properties and experiences to each user. This has helped to increase user engagement and retention, with over 150 million users over the years.
For all marketers and business strategists out there, product analytics is a powerful tool for improving user engagement and retention. By defining key metrics and goals, collecting data, analyzing patterns, testing changes, and monitoring progress, businesses can gain a deeper understanding of user behavior and make data-driven decisions to improve the product.