Filtering Session Data
To explore how session filters work, check out How Do I Filter Session Data?
In this article, we'll share examples of when you might choose to use particular Heatmap session filters and how to implement them.
What filters can I use on a Heatmap?
On the Observe Basic plan, Recordings can only be filtered by Date or Path/URL
The Observe Basic plan allows for filtering of Recordings by Date or Path/URL. Additional filters are available on our paid Observe plans which you can find out more about on the Hotjar pricing page.
On the Observe Basic plan, Recordings can only be filtered by Date
The Observe Basic plan only allows for filtering of Recordings by Date. Additional filters are available on our paid Observe plans which you can find out more about on the Hotjar pricing page.
Below we list the available filters to segment session data across Heatmaps.
Additionally, you can filter Heatmaps by date. Our date ranges include relative ranges and a custom date range option which are useful if you want to compare Heatmap data before and after changes were made to your web page.
You can save filters with your Heatmaps
When saving a Heatmap, any filters or date ranges that are applied will be saved with it, allowing you to more easily focus on the specific users.
Use cases for filtering Heatmaps
Filtering to compare how different groups of users interact with specific areas of your product can help you uncover new opportunities or validate existing assumptions. Below are some suggested use cases for applying filters to Heatmaps.
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Compare new vs returning users
It's possible to compare Heatmap data for a page between new and returning users to see how first-time users interact with your product. For example, you can compare new and returning visitors to your homepage to help build a specific experience for each audience. You might consider if returning users are finding your log-in button easily enough.
Filter by:
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Use location and technology to analyze visitor trends and troubleshoot issues
Get a clearer picture of user behavior based on their country, device, browser, and operating system.
Filter by:
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Focus on users who enter and exit your product on a specific page
Get a better understanding of users who enter your product on a specific page, and then exit without visiting any other pages. Filter by users who both landed on and exited your product on the same page to learn more about why they are leaving.
Filter by:
- Landing page: https://example.com/
- Exit page: https://example.com/
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Compare A/B test variants
Compare Heatmap data for pages you’re running an A/B test on and find out which one performs better. Using our Events API, you can send an event to Hotjar each time a user loads a specific page variant during an A/B test. You’ll then be able to filter by each variant to see how users are interacting with each page.
Setup event for variants for:
- Variant: AB_checkout_Dec21_variant
- Control: AB_checkout_Dec21_control
Filter by:
- Event: AB_checkout_Dec21_variant and AB_checkout_Dec21_control
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Compare users who made a purchase against those who didn’t make a purchase
Comparing the behavior of these user groups can uncover insights that can help drive conversion improvements. To do this, you’ll need to set up User Attributes in Hotjar so you can filter by users who both did and didn’t make a purchase.
Setup User Attributes for:
- Users who made a purchase: made_a_purchase = true
- Users who didn’t make a purchase: made_a_purchase = false
Filter by:
- Landing page: https://example.com/checkout
- Exit page: https://example.com/checkout
- User attribute: made_a_purchase = true. Compare this against made_a_purchase = false
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Perform an A/B test by comparing two versions of the checkout page
By comparing behavior between users who made a purchase against those who didn’t, you may have spotted some areas for optimization. It’s good practice to run an A/B test so you can validate whether the changes you made to the checkout will drive more conversions. Add a feedback filter to better understand the sentiment around the page. Here we’ll combine the Events and User Attributes we set up in the examples above:
Filter by:
- Event: AB_checkout_Dec21_variant and AB_checkout_Dec21_control
- User attribute: made_a_purchase = true. Compare this against made_a_purchase = false
- Feedback: Users who gave positive vs negative feedback
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See where users get frustrated during the checkout flow variations
By focusing on users that made it to your checkout page but didn't end up completing their purchase, you can see if there is some part of the process that is causing friction. Viewing rage clicks can help you understand where users get frustrated during the checkout flow, highlight bugs early on, and know how to redesign a page to boost conversions.
Filter by:
- Event: AB_checkout_Dec21_variant or AB_checkout_Dec21_control
- User attribute: made_a_purchase = false
- Rage click: Page URL is https://example.com/checkout
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Focus on users who view a landing page from a specific paid campaign
Using the Traffic channel filter, paired with the Engagement zones map, can help you understand how specific users interact with your page so you can make adjustments. For example, you can see if users from a paid campaign are scrolling down far enough to see your Call to Action button, or if another area is getting more than expected.
Filter by:
- Traffic channel: Campaign name contains "example_campaign"
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