While heatmaps aggregate user-experience data, they can also overwhelm you with thousands of data points that can take days to fully analyze.
This means you’ll have plenty of evidence of how users interact with your app design, CTAs, and new features, but struggle to get the detailed insights you need to optimize the user experience (UX).
So how can you use this behavioral analytics tool at scale without spending hours sifting through recorded user sessions to find actionable takeaways?
In this article, we share five heatmap examples, how to use them on their own, and how to use them in combination with other tools, such as AI analysis, in-product surveys, and session replays.
You’ll see common use cases for different types of heatmaps, learn how to get the data you need for confident product decision-making and feature optimization, and know how to automate heatmap analysis so you can reduce your workload.
1. Click heatmaps with AI for understanding user behavior at scale
Sprig click heatmaps show where people click across your product to quickly identify patterns and trends in their experienceClick heatmaps visualize areas where users click on your product and features. Unlike regular heatmaps, which aggregate click areas, click heatmaps will show individual clicks, revealing where users click more or less often.
You can better understand how users interact with your product with click maps as they can reveal important design, layout, or navigational issues. For example, a heatmap could show repeated clicks on non-interactive elements, signaling a design flaw, like a hidden or deactivated button.
By integrating AI with click maps, you can automate data analysis. AI can detect patterns and anomalies (like users frequently clicking on a section that isn't interactive) and make product recommendations based on its findings. This means you can grasp friction points in minutes instead of hours or days to improve the user experience (UX).
With Sprig, you can launch a click heatmap study in just a few clicks and base it on custom triggers to get granular user insights. For example, you could choose to run heatmaps on a specific URL or a no-code event page.
These pages allow you to track user interactions, like clicks or form submissions, without needing to write any code. They can range from landing pages and URL parameters (like UTM query strings) to wildcard URLs with dynamic content or logged-in user experiences, such as opening an account.
Once you’ve run a heatmap study with Sprig, you can filter AI-analyzed results based on specific attributes, like time period, event, or a specific user group. You'll have findings grouped into themed patterns so you can identify:
- Behavior patterns
- Bugs
- Errors, such as hidden buttons
- Friction points, like confusing navigation
Sprig’s AI summary helps you understand what to fix much faster than if you had to manually review hundreds of heatmaps — so you can get to work on implementing and testing product changes faster.
2. Scroll heatmaps to identify engagement with new features
Sprig scroll heatmaps show you how far users scroll down a pageScroll heatmaps highlight where users scroll and spend the most time on your product pages. They help provide insights into which page sections pique or lose user interest. These heatmaps can guide new content additions, feature placement, and page layout.
For example, if you just added a new feature below the fold of your product page, a scroll heatmap might reveal that users rarely reach that section. AI could suggest placing that new feature higher or change the page layout, enticing users to scroll further down.
However, while scroll heatmaps offer visual insights, they don’t completely reveal user intent. You won’t know why users stop scrolling or ignore certain sections. So, if a heatmap shows users never reach the bottom of a home page, it won’t reveal whether this is due to a technical issue, like slow page loading times, or confusing design or layout.
You can solve this limitation by combining scroll heatmaps with in-product surveys. For example, when a scroll heatmap shows users don’t engage below the fold, you can set up a trigger to survey users who drop off at that point in the user journey and ask them why.
AI can then analyze this feedback together with the scroll data and offer actionable recommendations, like improving the layout or adding relevant content that keeps people scrolling down.
Suppose an SaaS platform places a competitor pricing comparison table below the fold of their pricing plan page. The scroll heatmap data shows users rarely reach that point, while the in-product survey triggered for this scenario reveals users find the pricing options above the fold unclear.
With a solution like Sprig’s AI Recommendations, analysis of both heatmap and survey data might suggest simplifying the pricing content above the fold to make it less confusing and moving the comparison table higher up.
A combined use of scroll heatmaps, in-product surveys, and AI analysis lets you hone in on user engagement issues and understand their causes — so you can make strategic and data-backed improvements to your product.
3. Move heatmaps for refining feature placement and design
Sprig move heatmaps show you how your users interact in your product and on your websiteMovement heatmaps visualize how users move their mouse or cursor across your product. By highlighting areas of high interaction or hesitation, these heatmaps can reveal patterns in user behavior, such as which elements draw attention or where users may encounter friction.
With Sprig AI, you can analyze and summarize movement heatmap results in seconds to identify user patterns, helping to see where people naturally focus their attention. This type of data can help you refine feature placement and design.
Let's take a SaaS analytics platform as an example. Suppose the main analytics dashboard is located in the product’s sidebar, but movement heatmaps show that users tend to focus more on the top navigation bar. AI might recommend repositioning the dashboard within the top navigation area so users can discover it more quickly and use it more intuitively.
Further AI suggestions could include:
- Adding visual boosts, like highlighted buttons or icons, to aid user interaction
- Adjusting the size or positioning of key elements on the dashboard to increase visibility and better align with user engagement areas
- Incorporating tooltips or guided tours triggered by cursor movements to aid user onboarding
4. AI-powered heatmaps to optimize CTA placement
The results page of a Heatmap study in Sprig — including the AI Summary and movement heatmap, scrollmap, and clickmap dataYou can combine data from different heatmap types—like scroll depth, mouse movement, and clicks—to optimize conversions on your CTAs. Additionally, by toggling a session replay on your heatmaps, you can gain not only insights at scale but also a deeper understanding of specific friction points.
AI analysis then quickens aggregating and summarizing heatmap results into themed recommendations, helping to reduce the time from insight analysis to CTA optimization.
For example, imagine you’ve recently implemented design changes to your website and this has coincided with a drop in CTA click-through rates and conversions. With a continuous stream of heatmap data, you can get early insights into the core issue.
To do this, you might decide to run different types of heatmaps. If the combined heatmap data shows a higher concentration of user activity in the upper right-hand corner of your page, but your CTA button sits lower down, AI analysis might recommend moving it to where there's higher engagement.
With Sprig’s AI Explorer, AI analyzes user behavior and sentiment data across your entire product. This could also recommend altering the CTA text to be more action-oriented or introducing a hover effect to draw more attention in line with the user behavior patterns of a specific segment elsewhere within your product.
You can then change the CTA placement, design, and copy, and run another combination of heatmaps to test the optimized version.
Whether your CTAs are form submissions, subscribe buttons, or specific landing page links, combining varied heatmap data with AI can help you increase conversions without relying on resource-intensive manual investigation.
5. Heatmap and AI-driven insights to reduce cart abandonment
Heatmap combined with AI-driven insights can be crucial for reducing cart abandonment and understanding user behavior.
For example, with Sprig's AI capabilities, you can go beyond just analyzing and summarizing results. You can use AI to refine triggered studies and meet specific goals, such as improving the first-time user experience or identifying UX friction points.
Sprig's AI will then analyze your heatmap data based on your specific objectives and provide summaries tailored to your needs, whether it’s optimizing a new feature or uncovering areas for improvement.
To automate these data-driven insights, you can trigger heatmaps to record clicks using no-code events, like targeting logged-in product experiences or when users land on a specific URL (such as a landing page).
Additionally, you can customize your studies based on user attributes, such as geographic location, language, device type, and product experience, to meet the specific needs of your audience.
By leveraging heatmaps with AI insights, you can identify friction points at critical stages of the user journey, such as during checkout or when users abandon their carts.
For instance, on a tiered pricing page, heatmaps might reveal that users often hover over pricing details before leaving. Based on your study goal of fixing friction points, AI might suggest simplifying the pricing fields or offering more flexible plans to make the options easier to understand and more compelling.
Discover deeper user insights with Sprig's AI-powered heatmaps
Combining different kinds of heatmaps will give you greater insights into user behavior but it may make analyzing the data more complicated and time-consuming, while not revealing the why behind user actions.
By integrating AI with tools like triggered surveys and continuous feedback, you can strengthen your understanding of user intent and speed up your product improvements.
Sprig heatmaps go one step further by automating the data-gathering process. You can use AI to help create targeted studies that are tailored to make findings based on your specific criteria and goals, and triggered by user attributes or key events.
By reducing your time-to-insight, you can efficiently optimize your product and the user journey—with less manual work and a greater return on investment.
Frequently asked questions about heatmap examples
What types of heatmaps are most useful for product teams?
Product teams can find click, scroll and movement heatmaps the most useful for analyzing user behavior and interaction with their products.
For example, when you combine the results of different heatmaps, you can reveal placement optimization insights, like where a CTA button can be more effective in increasing conversions.
AI-powered heatmaps can be especially helpful in reducing result analytics times, helping product teams work on optimizing the user journey and product features faster.
How can heatmap analysis improve user experience and engagement?
Effective analysis of heatmaps can highlight where users engage with your product the most or encounter navigational friction points. Heatmap results analysis can reveal a confusing onboarding journey, helping product teams to optimize the experience by changing the page layout or simplifying steps.
However, analyzing hundreds or even thousands of data points can be highly resource-intensive when done manually. Look for AI-driven heatmaps that provide automated result analysis that can group findings into themed recommendations.
What are the best practices for implementing heatmaps effectively?
Product teams using heatmaps will want to focus on analyzing key user journey moments, like checkout or onboarding, or high-traffic pages, like the home page, pricing or checkout.
Other best practices for heatmaps include user segmentation to understand how different groups engage with your product. Combining different heatmaps or even adding replay sessions can enable product teams to dig deeper into initial findings.