Orders / sessions. Below 1.5% usually = checkout / page-speed issue, not traffic.
At a glance
Orders divided by sessions over the rolling 30 days. Below 1.5% on an Ecwid-embedded widget typically means the checkout flow or host-page speed is broken, not that traffic is bad. The single most diagnostic card on the Ecwid dashboard for “where is my funnel leaking?”.
| What it counts | COUNT(orders WHERE PAID) / COUNT(unique_sessions_visiting_widget) over 30D. |
| Source data | Numerator: Ecwid orders. Denominator: Ecwid widget analytics (/v3/{store-id}/reports/storefront-views) or where connected, GA4 sessions on the host page filtered by event_name = ecwid_widget_loaded. |
| What “session” means | A unique visit to the widget over a 30-minute window per device. Ecwid’s first-party tracking does not require GA4 but supplements with it when both are connected. |
| VAT / tax | Not relevant (rate). |
| Refunds / cancellations | Refunded orders count toward the numerator (the conversion happened); cancelled-before-payment orders do not. |
| Currency | Not relevant (dimensionless rate). |
| Channel scope | Aggregated across widget instances. If the merchant runs the widget on three sites (WordPress + Instagram + Wix), the rate is for total sessions across all. |
| Bot filtering | Sessions flagged as bot or crawler are excluded from the denominator. |
| Time window | 30D vsP. |
| Alert trigger | <1.5%. The hero-tier “your funnel is broken” floor for an Ecwid embedded widget. Healthy Ecwid stores run 2 to 4%. |
| Roles | owner, marketing. |
Calculation
Calculated automatically from your Ecwid data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.Worked example
A small UK hobby photographer running an Ecwid widget on their WordPress blog, rolling 30D ending 27 Apr 26.| Metric | This 30D | Prior 30D | Change |
|---|---|---|---|
| Widget sessions | 1,820 | 1,650 | +10.3% |
| Orders (PAID) | 47 | 52 | -9.6% |
| Conversion rate (this card) | 2.58% | 3.15% | -0.57pp |
- Traffic-source mix shifted. A new content piece (e.g. blog post that went viral) brought in browsers rather than buyers. The traffic is real but the intent is lower. Check the host site’s referrer breakdown.
- Checkout broke partially. Possibly a Stripe webhook issue, broken coupon code, or shipping-rate misconfiguration. Test the checkout flow as a buyer to confirm.
Sibling cards merchants should reference together
| Card | Why it matters next to conversion rate | What the combination tells you |
|---|---|---|
| Total Revenue | Outcome. | CR up + revenue flat = AOV dropped; CR down + revenue flat = traffic surged. |
| Total Orders | Numerator. | Order count drops without traffic drop = funnel issue. |
| Average Order Value | Trade-off. | High CR + low AOV = mass-market impulse buying; low CR + high AOV = considered-purchase niche. |
| Out-of-Stock Products | Blocking input. | OOS hero SKUs convert at 0%; rising OOS depresses CR mechanically. |
| Refund Rate | Quality. | High CR + high refund rate = low-quality conversions; check what is being sold. |
| New Customers | Acquisition. | Sessions and new customers should track; if sessions outpace new customers, traffic is browse-heavy. |
| Top Products | Conversion concentration. | Identify which SKUs drive the bulk of conversions; OOS on those is high-impact. |
Reconciling against the vendor’s own dashboard
Where to look in Ecwid’s own dashboard:
Ecwid Control Panel (my.ecwid.com) -> Reports -> Storefront analytics
Shows session count, order count, and computed conversion rate for the chosen window.
Why our number may legitimately differ from Ecwid’s Control Panel:
| Reason | Direction | Why |
|---|---|---|
| Session definition | Marginal | Ecwid uses 30-minute inactivity windows; some merchants use GA4-style 30-minute event-based sessions. |
| Bot filtering | Either | Ecwid filters known bot UAs; we additionally filter on suspicious behaviour patterns. |
| Time zone | Boundary days | Ecwid uses store-local; we use UTC. |
| Multi-instance widgets | Identical | Both sides aggregate across widget instances. |
| Mobile app POS | Either | POS sessions are typically not counted in storefront analytics; we follow Ecwid’s exclusion. |
ecwid_conversion_rate = ecwid_total_orders / ecwid_widget_sessions over the same window.