Discount Analytics

The Analytics tab answers one question: which of my discounts are worth running? Instead of guessing whether a promotion worked, you see every discount ranked by return — and can drill into any one of them.

Every view here is free — there is no analytics feature behind a paid tier.

Headline stats

The top of the page shows four stat cards at a glance:

Stat	What it measures
Discounted / Total Orders	How many orders applied at least one discount, against the total order count in range
Total Discount Given	The summed discount value delivered to customers over the period (broken down per currency in a multi-currency store)
Active Discounts Tracked	How many of the discounts with activity in the date range are currently active
Most Used Discount	The single rule that fired most often (only shown when there’s data)

The stat cards update as you change the date range — they’re scoped to the From / To pickers at the top of the page.

Five views

The Analytics tab has five sub-tabs, each scoped to the same date range and currency:

Summary

The default view — and the heart of the tab. It puts every discount in one ranked table, best-to-worst by net ROI, below a chart that plots how deep each discount cuts against the size of the orders it brings.

The Average order value vs. discount depth chart places one dot per discount on shared axes: the discount depth (discount given ÷ gross revenue — the average % taken off the order, across the bottom) against the average order value of orders that used it (up the side). Dot size reflects order volume; dot colour shows the best and worst performer from the ranked table.

The ranked table lists every rule that fired in range, sorted best-to-worst by ROI, with these columns:

Column	What it tells you
Discount	The rule name, its type pill (e.g. Min Spend % Off, Buy X Get Y), and whether it’s currently active
Orders	Orders that included this discount
Gross Rev	Total value of those orders. Orders using several discounts share their value between them in proportion to each discount’s contribution
Discount Given	Total discount value handed to shoppers — your cost
Gifts Given	Orders that received a free-gift discount’s gift. A gift gives £0 off, so this is the figure that shows what it did. The column only appears when a gift discount is listed; other rows read ”—“
AOV	Average order value for those orders (gross rev ÷ orders)
New Cust. %	Share of orders from first-time customers (an email with no earlier order)
Net ROI	(gross rev − discount given) ÷ discount given — £ actually banked per £1 given, so a 100%-off giveaway shows £0.00. The table is ranked on this column; the best and worst are flagged. A discount that gave nothing off, such as a free gift, has no net ROI and is listed after the ranked ones.

When more than 25 discounts fired in range, the view loads the 25 used on the most orders.

An Analyse scope bar between the chart and the table lets you switch what the view covers:

• All discounts (default) — the full ranked list.
• One discount — pick a single discount for its full read: whether it was the best or worst performer, the explained numbers (revenue driven, redemptions, discount given, net ROI, new customers) and a Redemptions over time chart. When the discount has a total usage limit, a usage-limit forecast shows when it will run out at the current pace, and whether that comes before its scheduled end.
• Compare — pick a subset and re-rank just those against each other.

Comparative Trends

Time-series charts for cross-discount comparison. Pick two or more rules and overlay their redemption / discount-given curves over the date range, with a metric selector (Orders / Gross Revenue / Discount Given / AOV) and a daily breakdown table. Useful for A/B comparisons between two recipes targeting the same audience.

Single discount deep dive

Pick one discount from Analyze Dedicated Discount: to see its charts: an order-value distribution, Redemptions (Orders) Over Time, Gross Revenue Retained Over Time and Discount Cost Given Over Time.

Timing

When redemptions happen: a Redemptions by day of week grid with a Weekend lift figure per discount, a Redemption velocity reading (the share of a discount’s redemptions that land within 2 days of its first one in the range), and summary cards for the busiest window, peak day, weekend share, quietest day and logged-in vs guest split. Timing counts redemptions, not revenue — it answers when, so you can schedule around your real demand.

Coupons

Coupon-specific stats — headline cards (active coupons, codes issued, redemptions, codes remaining, discount given), Coupon Activity Over Time and Codes Remaining Over Time charts, and a per-coupon table of codes issued, redeemed and remaining, utilisation and last-used date. When a coupon unlocks a free gift, the table adds a Gifts Given column with the orders that received it in the range. This view reads coupon activity captured at checkout, so a pooled-code coupon reports against the coupon rather than the underlying rule.

Multi-currency

In a multi-currency store, each rule reports per currency — the Discount Given, Gross Rev, and AOV figures honour the order’s checkout currency, so per-currency totals never blend with FX’d values. The headline Total Discount Given card shows a per-currency breakdown, and the discount-depth chart plots each rule once per currency it was used in.

This means the Analytics tab never reports cross-currency totals from FX conversions — only natively-recorded values per checkout currency. See Multi-currency compatibility for the per-currency model.

Date-range filtering

Filter by time period to:

• Compare promotional periods — did your Black Friday tiered discount outperform last year’s flat one?
• Measure promotion impact — filter to the exact dates a promotion ran
• Track trends — is usage growing over time?

The default range is the last 30 days; pick any custom window using the From / To date inputs. Date filters use your store timezone. Analytics count completed orders, and very recent checkouts may take a short time to appear — click ↻ Refresh to reload. Download CSV exports the report for the selected range.

Deleting all analytics data

The Analytics tab has a Delete all data button for clearing your entire reporting history — useful after testing, when handing a store over, or to start a fresh measurement period.

It is deliberately hard to fire by accident:

1. Click Delete all data to open the Delete all analytics data? dialog.
2. The dialog warns that this permanently removes every recorded analytics event — your entire discount and coupon reporting history — and that it cannot be undone.
3. The confirm button stays disabled until you type the word DELETE (exactly — uppercase, no extra spaces) into the field.
4. Only then does Delete all data unlock.

There is no undo

This wipes all recorded analytics events for the store. It does not touch your discount rules, coupons, or settings — only the captured reporting history. If you might want the numbers later, record what you need before wiping — there is no built-in undo.

Independent of the cart hot path

Analytics data is captured at checkout time — each order is tagged with its discount rule IDs, and those events are summarised into a dedicated analytics-events table that the Analytics tab queries lazily. The dashboard does not add overhead to the rule-evaluation hot path that runs on every cart; opening the Analytics tab queries the events table directly, not the live engine.

This separation is why analytics queries can be heavy without affecting checkout performance. See Performance Monitoring for the hot-path budget.

Using analytics to optimise

Back the winners, cut the losers

The Summary ranking does this for you: the discounts flagged best return the most revenue per £1 given — consider giving them more prominence or budget. The one flagged worst is giving away the most for the least back — a candidate to tighten, re-target, or retire. The discount-depth chart makes the trade-off visual: a deep discount is only worth it if it brings bigger orders.

Find underperforming discounts

If a discount has very few Orders, consider:

• Lowering the qualification threshold
• Ticking Include in spend-more nudges in the discount’s Messaging section (it starts unticked), so the spend-more nudge guides customers toward it (where nudges appear is set store-wide on the Message Settings tab)
• Turning on Show on product page so shoppers know the deal exists

Adjust tiers

If most customers hit your lowest tier but few reach higher ones, consider:

• Narrowing the gap between tiers
• Tightening the next-tier nudge (“Spend [amount_left] more for [next_tier]”)
• Testing a higher first-tier discount with a lower threshold

What’s next

• Performance Monitoring — the cart-eval hot-path budget
• Cart Labels — what the customer sees in the cart
• Upsell Messages — nudges that lift the redemption count