Analytics

Conversion funnel breakdown, payment-method mix, geographic distribution, and form-field friction analysis. Use it to find drop-off points and tune your checkout.

Time-range picker

Top-right of the page. Pick 24H / 7D / 30D / 90D. Every chart below updates to that window.

Conversion funnel

A vertical bar chart with four stages:

1. Sessions started — checkout page loaded.
2. Details complete — email + address submitted.
3. Payment attempted — at least one payment attempt fired.
4. Order completed — payment succeeded.

Each bar shows the absolute count plus the drop-off percentage from the previous stage. Big drop-offs are where you focus your tuning.

Typical healthy patterns:

• >80% sessions started → details complete (lower than this = address autocomplete or form is friction-y)
• >85% details complete → payment attempted (lower = the shopper hit the payment step and balked)
• >90% payment attempted → completed (lower = card decline rate is high; check fraud settings or 3DS issues)

Payment method breakdown

Pie chart + table showing the split of completed orders by payment method: Card (Stripe), PayPal, Klyme, NomuPay, Apple Pay, Google Pay, BNPL, etc.

Useful for:

• Spotting which gateway your customers actually prefer
• Deciding whether to enable / disable a slow-converting method
• Verifying that a newly-enabled method is actually being used

Geographic breakdown

A list of countries with:

• Session count
• Order count
• Conversion rate
• Average order value

Click a country to filter the rest of the page. Useful for finding markets that are converting unexpectedly well (or badly).

Form-field friction

A heatmap showing every field on the checkout details step, with:

• Time spent on field (seconds, median)
• Edit count (median — high = shoppers correcting typos repeatedly)
• Abandon rate (shoppers who reached this field but never moved past it)

Red-flag patterns:

• Phone number with high abandon rate → make it optional.
• Postcode with high edit count → your address autocomplete might be misbehaving.
• Email with high time-spent → consider whether your error message is unclear.

Tips

• Drop-off is fractal. The big-picture funnel hides micro-funnels. Use the form-field heatmap to find the per-field issues.
• Mobile vs desktop is huge. If you’re seeing a big drop-off and haven’t filtered by device, you’re missing the most likely cause. (Mobile-specific filter coming soon — for now, use UTM source if you can segment your traffic.)
• The funnel reflects what shoppers do, not why. Combine quantitative data here with qualitative tools — talk to your customers, run a Hotjar-style session replay, A/B test variants.