Shopify Total Discount Amount Analytics

Total Discount Amount measures the discounts allocated to Shopify product sales for the timeframe and filters you choose. The amount excludes tax.

How Shopify Total Discount Amount is calculated

Total Discount Amount = the discount value Shopify allocates to the relevant product sales

The amount excludes tax. Discounts are also the gap between Gross Sales and Net Sales that returns do not explain, so reporting all three together shows what share of your original demand promotions consumed.

Why you should report on what you gave away

Discounts are usually the largest line of spending a store never budgets for. They do not appear as an expense, they come straight off the top, and because they are set campaign by campaign, few merchants know their annual total.

Reporting on discount amounts tells you:

• Which products sell mainly on orders that used a discount, which is worth a pricing review
• How much discount value a promotion absorbed, next to the sales on those orders
• Which vendors and categories carry the most discount value
• Whether your discount total is growing faster than your revenue

Place Total Discount Amount between Gross Sales and Net Sales and it explains most of the gap between them. That single row of three numbers is often the most useful thing on a sales report.

Add Total Discount Amount to a Shopify data worksheet

1. Open a supported Shopify data worksheet

2. Click ＋ Shopify data in the header row of an empty column, or choose Column > Add Shopify data… from the menu bar. The field picker opens

3. Click Sales & analytics in the list of sections on the left, or type the metric's name in the search box

   

4. Click Total Discount Amount. The metric's settings open with it already chosen under Which metric?

5. Under Over what time period?, choose All time, Fixed dates, Rolling period, Calendar year, or Calendar month

6. Under Limit which orders count, add any optional filters supported by the metric

7. Click Save Column

Measure a single Shopify discount campaign

Use the Discount code filter to focus the calculation on one Shopify discount campaign. The selector is searchable and groups redeem codes under their parent campaign, so a campaign that generated hundreds of unique codes still reports as one thing.

To size up a promotion, add a matched set of columns using the same timeframe and the same campaign filter:

• Total Discount Amount for the discount value allocated to those orders
• Orders Count for how many orders used the campaign
• Gross Sales for what those orders totaled before discounts
• Net Sales for what remained after discounts and refunds

Repeat the set for a second campaign and you have a like-for-like comparison of two promotions in the same spreadsheet, which is difficult to assemble any other way.

Compare discounts with a Pivot Table worksheet

1. Click the + button beside the worksheet tabs
2. Select Reporting worksheet, then click Continue
3. Select a ready-made report under Pivot table, or choose Blank pivot table, then click Continue
4. Under Break down by, choose the breakdown
5. Add Total Discount Amount under Metrics
6. Under Time period, choose the period
7. Click Create Worksheet

Pivot Table worksheets can compare discounts by vendor, product type, product category, product tag, market, or customer segment. To compare collections, add a Total Discount Amount analytics column to a Collections worksheet instead, as described in Shopify Collection Analytics.

Track store discounts over time

When a Time Series worksheet uses Store as its breakdown, Total Discount Amount appears as a row, with columns By day, By week, By month, or By year. The ready-made Store sales and orders by month report includes discounts alongside sales, orders, refunds, and Average Order Value, so you can watch discount spending as a share of sales rather than as an isolated number.

For non-store Time Series breakdowns, Total Discount Amount is not currently offered as the single measure. Use a Pivot Table worksheet with a calendar timeframe, or add filtered analytics columns, when you need a more specific comparison.