# 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 ](https://www.shoplift.ai/pricing)(to run A/B tests).
* [Heatmap](https://www.heatmap.com/pricing) (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**

1. Install Shoplift and create/launch your A/B test
2. Ensure the Heatmap tracking snippet is installed on the same pages
3. 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](https://intercom.help/heatmapcom/en/articles/12060151-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

1. **Name variants clearly**: Use descriptive names (e.g., “Blue CTA Button” instead of “Variant A”). These names appear in Heatmap and make analysis easier.
2. **Create variant-specific snapshots/filters**: Analyze each variant in isolation; use Compare for side-by-side views.
3. **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.
4. **Focus each test on one major change**: Easier to attribute behavior changes to a specific element.
5. **Document your hypothesis**: Define expected behavior differences ahead of time; use heatmaps to validate or refine your assumptions.
6. **Save frequently used filters**: Save experiment/variant filters so your team can quickly revisit analyses.
7. **Use recordings and scroll depth alongside clicks**: Look for rage clicks, dead clicks, and unexpected movement patterns to explain performance differences.
8. **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](https://intercom.help/heatmapcom/en/articles/12060151-a-b-test-filters-shoplift-integration)

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