Heatmap Integration
Shoplift now integrates with Heatmap, enabling you to pair conversion results with visual behavior analytics. Compare heatmaps by variant to pinpoint the UX changes behind performance lifts.
Prerequisites
Get a subscription
You’ll need active accounts for:
Shoplift (to run A/B tests).
Heatmap (to capture and analyze behavior).
Quickstart
No code required. If both Heatmap and Shoplift are installed on your site, Heatmap automatically detects Shoplift experiments and tracks variant participation.
Verify installation
Shoplift: Ensure your experiment is active and traffic is flowing to variants
Heatmap: Confirm the Heatmap script is present on the pages under test
How to set up
Install Shoplift and create/launch your A/B test
Ensure the Heatmap tracking snippet is installed on the same pages
That’s it! Heatmap will automatically attribute users to their Shoplift variant and make variant filters available within 1-2 hours of traffic
Here’s the full Heatmap guide: A/B test Filters: Shoplift Integration
Using Shoplift test data in Heatmap
View a single variant
Log in to Heatmap
Open your heatmap for the tested page
Click Filter → A/B Tests → Platform: Shoplift
Pick the experiment, then select a specific variant
Apply to see variant-specific clicks, scrolls, and movement
Compare variants side-by-side
Follow the steps above to choose your first variant
Click Compare instead of Apply.
Select the second variant to view side-by-side behavior differences
What you’ll see
Variant screenshot and label (as named in Shoplift)
Click heatmap, scroll depth, and movement for that variant only
Visitor counts by device type for the selected variant
Rage/dead clicks and time-on-page patterns where available
Best practices
Name variants clearly: Use descriptive names (e.g., “Blue CTA Button” instead of “Variant A”). These names appear in Heatmap and make analysis easier.
Create variant-specific snapshots/filters: Analyze each variant in isolation; use Compare for side-by-side views.
Ensure adequate sample size and time: Aim for at least 500–1,000 visitors per variant and run through full business cycles (include weekends) to avoid misleading patterns.
Focus each test on one major change: Easier to attribute behavior changes to a specific element.
Document your hypothesis: Define expected behavior differences ahead of time; use heatmaps to validate or refine your assumptions.
Save frequently used filters: Save experiment/variant filters so your team can quickly revisit analyses.
Use recordings and scroll depth alongside clicks: Look for rage clicks, dead clicks, and unexpected movement patterns to explain performance differences.
Post-test review: After you declare a winner in Shoplift, use Heatmap insights to understand “why” and identify follow-up test ideas.
Troubleshooting
Quick fixes
Don’t see Shoplift in Filters?
Confirm your experiment is Active, traffic is flowing, and both scripts are installed.
Give it 1–2 hours after starting the experiment; ensure enough traffic (50+ visitors/variant).
Variant preview looks off
Verify the variant renders correctly on your site; clear cache; watch for dynamic/personalized elements.
Seeing IDs, not names
Update variant names in Shoplift; allow 1–2 hours for the names to sync; refresh Heatmap.
No data for a variant
Check split, confirm visitors are allocated to the variant, and ensure the date range covers the test duration.
Understanding the data
Tracked per variant
Clicks, moves, scrolls
Time on page
Rage and dead clicks
Scroll depth and hover patterns
Timing
Collection starts as soon as the experiment runs and traffic flows
Variant filters typically appear within 1–2 hours
Reliable patterns emerge after 500+ visitors per variant
Notes
Historical data prior to enabling the integration cannot be backfilled by variant
A visitor’s assigned variant persists for their session for consistent attribution
Data updates in real-time once the initial setup completes
Analysis tips
Compare heatmaps between control and variants to spot engagement shifts
Look for unexpected click clusters or dead zones introduced by a variant
Use scroll depth to evaluate whether new layouts alter content discovery
Investigate rage-click hotspots as signals of confusion or broken affordances
Pair Heatmap behavior insights with Shoplift conversion metrics to connect “why” and “what”
Link to the Heatmap guide
Heatmap <> Shoplift Integration Guide
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