Performance metrics explained
Measure what matters, in real time
Overview
Once your personalisation engine is live, the Home tab begins populating with performance data. These metrics help you understand how Liva 7 is impacting your store - from revenue and engagement to conversion behaviour.
This article breaks down each metric and how to interpret it.
Key metrics in the Home tab
1. Influenced Revenue
- The total revenue from orders that included at least one product clicked through a Liva 7 widget
- Helps quantify the direct impact of personalisation on sales
- Updated in real time as new orders are placed
2. AOV Uplift (Average Order Value)
- Compares the average order value of influenced orders vs. non-influenced orders
- Shows how Liva 7 affects basket size and upsell performance
- Expressed as a percentage increase or decrease
3. Engagement Rate
- The percentage of sessions where a shopper interacted with a Liva 7 widget
- Includes clicks, hovers, and scrolls depending on widget type
- Indicates how compelling and visible your recommendations are
4. Total Interactions
- The raw number of interactions with Liva 7 widgets
- Includes clicks, views, and other engagement signals
- Useful for tracking volume and identifying high-traffic periods
Engagement rate is a strong early indicator of how well your recommendations are resonating with shoppers.
Filters and views
You can adjust how metrics are displayed on the Performance graph using:
- Date Range Filters: Today, 7 Days, 30 Days, 60 Days
- Metric Filters: Views, Cart Adds, Purchases
These filters help you isolate trends and compare performance across different contexts.
Understanding the Learning Phase
If your engine is still in the Learning Phase, some metrics may show placeholder messages like:
- 'Liva 7 is learning your store. Insights will appear soon.'
- This means signal volume is still ramping up
- Most stores exit the learning phase within 3 to 7 days of completing setup
Final thoughts
Performance metrics are your window into how Liva 7 is working behind the scenes. By understanding what each number means - and how to filter and interpret them - you can make smarter decisions, optimise faster, and clearly demonstrate the value of personalisation.
