Make Data-Driven Design Decisions With Product Analytics | Datadog

Make data-driven design decisions with Product Analytics

Author Jamie Milstein
Author Addie Beach

Published: June 26, 2024

To truly understand your users and ensure that your app provides them with the best possible experience, you need to conduct comprehensive, data-forward analyses from multiple angles. This quantitative foundation enables you to make meaningful product decisions—instead of merely guessing at what users want or trying to extract large-scale insights from piecemeal survey responses, you can see exactly what functionality is drawing in users or pushing them away.

With our Product Analytics offering, Datadog provides a variety of resources designed to help you explore product usage. We’ve organized these features into one centralized location where you can quickly perform multi-faceted user analysis via many different visualizations. These features enable you to explore every aspect of your user experience, from larger adoption trends to specific pain points, with insights grounded in concrete usage data from your app.

In this post, we’ll explore how you can:

Identify friction points with user engagement overviews

In order to help you identify areas of analysis that you’d like to focus on in greater depth, you often need to start by assessing the state of your UX at a high level. Product Analytics gives you access to broad visualizations to help you do just that, enabling you to quickly identify important behavioral patterns and unexpected user activity.

When you access Product Analytics, the Analytics Summary provides you with a comprehensive overview of user activity across your system. You can view various graphs and metrics related to engagement trends over a selected time frame, including unique visits, screen views, power users, and session durations. Additionally, you’re able to access other crucial product analytics metrics, such as data on user acquisitions and demographics, as well as frustration signals for more pointed analyses.

The Product Analytics Summary page, with user engagement metrics and visualizations displayed.

If you want a more customized view—say, to track promo code usage and loyalty program sign-ups for a specific campaign—you can also easily create your own dashboards. Together, the Analytics Summary and customized dashboards help you identify unusual trends that may indicate friction points in your journeys, providing you with starting points for further investigation.

To dig into these engagement trends more deeply, you can pivot to the User Journeys section to access a variety of diagrams that help you better understand how users move through your app. The Sankey visualization is automatically displayed, enabling you to analyze multiple journeys at once and assess which paths your users gravitate towards. For exploratory analyses, the Sankey visualization provides a convenient starting point, helping you understand overall traffic patterns across your entire application.

The Sankey visualization showing user journeys across the most popular views in an app.

If you identify a specific journey you’d like to study in more detail, you can easily create a funnel from it. In addition to helping you visualize user drop-off and retention for a specific journey, funnels also provide you with detailed conversion metrics. You view these metrics alongside average loading times and other performance data, helping you quickly determine whether high abandonment rates could be the result of system issues such as latency.

Visualize interactions to better understand user behavior

Studying user journeys can help you understand which pages your users are visiting in your app, but how can you uncover the actions that users take while they’re there? Features such as Heatmaps and Session Replay enable you to visualize your users’ actions so you can see which parts of your app are capturing their attention, giving them trouble, or being ignored completely.

For an aggregate picture of user interactions, you can access Heatmaps to see how users interact with each page of your app. Heatmaps provides visualizations tailored to a variety of actions, including a click map, a scroll map, and a map of the most popular elements. Though they focus on different aspects of your UX, each visualization ultimately helps you determine where user attention tends to be concentrated.

A heatmap showing UI elements with the most interactions for a checkout page.

If you notice a data point that you want to investigate in greater detail, you can pivot to a recording in Session Replay that includes that view or action. Session Replay helps you complete the story—if you notice an increase in frustration clicks on a certain element, viewing an individual session enables you to see what users were trying to do and what they did next.

For example, let’s say you’re trying to figure out why sign-ups for your email list have decreased after a recent redesign. By looking at the click map for your homepage, you see a cluster of frustration signals around the sign-up link. To investigate the cause of this friction, you then decide to view a replay for a session that included one of these signals. Here, you can see the user attempting to click the link multiple times and receiving an error message in response. Now that you’ve confirmed that the issue likely stems from the application code, you can report these findings to the associated engineering team.

Cement key findings and hypotheses with granular user data

For a complete picture of user interactions within your app, you often need concrete data points that answer specific questions. Looking at journeys can help you explore your UX and determine areas of interest; looking at specific interactions enables you to explore these areas as well as identify key questions and hypotheses. Studying user analytics then helps you answer these questions and track how these answers may change over time.

When you want to perform fine-grained analyses, the Analytics Explorer helps you quickly search and visualize critical data from user sessions. Alongside a straightforward list of sessions, a variety of different visualization formats are provided—including time series graphs, geomaps, and pie charts—and you can easily filter the data in each format by filters such as user email and customer tier. This helps you explore key questions with your user data, such as the amount of time that users spend on your website or where your most active users tend to be located. Additionally, you can perform an advanced search that enables you to input specific queries. For example, you could create a query that finds the number of users who spent more than $20 within the past month.

A ranked list of users who spent over $20 in the Analytics Explorer.

For further insight into your user base, you can also access User Segments to view breakdowns of your visitors by various metadata. You can define segments based on existing data from Product Analytics or quickly pull in contextual data about your users—such as their account type or industry—from third-party sources. You can then use these segments in other Product Analytics features, such as the Analytics Explorer and User Journeys, to quickly surface information related to key groups of users. For example, you can create a segment for users in your loyalty program, then use the Analytics Explorer to identify where these users are located.

A list of user segments for an app.

Lastly, to test your hypotheses based on insights from the rest of Product Analytics, you can use User Retention to track user stickiness over time. Let’s say that, based on the insights you’ve gleaned from the Analytics and User Segments sections, you identify a sharp decrease in the number of purchases that German users made in your app. After viewing a session recording for a user in Germany, you realize that there’s a bug in how the translated text is appearing to these users, causing your UI to look odd to them. After fixing the bug, you check User Retention to find the percentage of German users returning to your app, confirming this was indeed the problem. When looking at cohort return rates before and after the bug fix, you see a sharp increase in long-term retention among German users.

Start exploring Product Analytics today

Product Analytics combines features designed to help you visualize user journeys and interactions, identify friction points, and investigate potential UX changes. By viewing high-level visualizations alongside granular user data, you can quickly answer critical questions about your product design no matter the scope of your analysis.

You can get started with Product Analytics using our documentation. Or, if you’re new to Datadog, you can sign up for a today.