Shopify Analytics Beyond the Dashboard

The built-in reports tell you what sold. A monthly routine built on four further questions tells you why a month was better or worse, and what to do about it.

Shopify's built-in reports answer what sold and when. They are less helpful for why a month was good or bad, which is where decisions get made. This post lays out a monthly review built on four questions that go beyond the dashboard, and how to answer each from your order data.

Where the dashboard stops

The dashboard and standard reports are good at totals over time, sales by product, and sales by channel. The questions that need a second look are the ones that cross two kinds of data: orders with refunds, orders with discounts, orders with customer history. You can answer them by exporting several reports and combining them in a spreadsheet. That works and takes an afternoon. The aim of this routine is to make it a half-hour monthly habit, whichever tool you use.

Question 1: which part of net sales moved

Net sales is not one number. It is gross sales, plus discounts (negative), plus returns (negative). If net sales fell 6 percent, the cause is one of three things: fewer or smaller sales, heavier discounting, or more returns. Those lead to different actions, so decompose before you explain.

Illustrative example: gross sales flat, discounts up, returns up. The story is not demand. It is a promotion that pulled forward low-margin orders and a batch of returns from the previous month's peak. One caution: Shopify counts a return on the refund date, so returns in this month may belong to last month's orders. See attributing refunds to the right month.

Question 2: new versus returning customers

Revenue growth from new customers costs acquisition spend. Growth from returning customers mostly does not. Split orders into first orders and repeat orders, then look at three things: the number of new customers per month, the share of orders from returning customers, and average order value for each group.

Pick a window and say it out loud. Repeat purchase rate measured over 90 days is lower than over 12 months, and a report that switches windows will appear to improve. Guest checkouts have no customer record, so they sit outside a customer-based split; check how common they are in your store before treating the split as complete.

Question 3: top sellers and top returners

Rank products by net sales and by return rate, meaning returned units divided by units sold. A raw count of returns just re-ranks your best sellers. Set a floor, for example at least 30 units sold, before reading anything into a rate. A product with 2 sales and 1 return is a 50 percent return rate and a meaningless one.

When a high-selling product also has a high return rate, check variants first. Returns concentrate in one size or color more often than they spread across a product, and the fix is in the listing or the sizing guide, not the product.

Question 4: how much of the month was bought with a code

Compare orders that used a discount code with orders that did not: count, average order value, and share of net sales. If most of your volume only exists when a code is live, the code is the product. If codes mostly lift average order value through a threshold, they are working as designed. Neither conclusion is visible in a totals report.

Running the routine with Quiriz

The Shopify connector (in beta, read-only, refreshed daily) provides [Shopify] Orders with a first-order flag, discount codes, sales channel and UTM fields, [Shopify] Order line items, [Shopify] Refunds, [Shopify] Sales and [Shopify] Products. Each question above becomes one plain-English question:

• "Gross sales, discounts and returns by month this year."
• "New customers by month, and average order value for first orders versus repeat orders."
• "Which products get returned most?" followed by "return rate by product for products with at least 30 units sold."
• "Net sales and orders with and without a discount code, by month."

Because Net sales, Average order value and New customers are stored metrics in Company Context, the January answer and the June answer use the same definition. The month-to-month comparison is therefore about the store and not about how somebody built a pivot. Once the questions are saved you can reuse them as a monthly report; see how to write a monthly business report for the structure around it.

Writing the review so someone reads it

The analysis is only half the routine. A monthly review nobody reads is a monthly chore. Keep it to one page with a fixed shape: the headline number and its three parts, one finding about customers, one about products, one about discounts, and a short list of what you will test next month. Use the same order every time, so a reader can compare months by eye. Resist adding a chart for every sentence. Two charts, net sales by month and new versus returning orders, usually carry the story.

Save the questions you used so next month's version is the same set run again. The value of a routine is comparability, and comparability comes from asking the identical question each time.

Honest limits

• Without full order history approved, only the last 60 days are available. Large stores import up to an order cap, covering the newest whole months.
• Product cost is the variant's current unit cost, so margin on older orders is approximate.
• Customer records carry no names, emails or addresses by design, so this is behavior analysis, not mailing-list work.
• Shopify does not report ad spend or sessions to this connector. Marketing efficiency needs ad data joined in, as in how to calculate ROAS by channel.

More store workflows are collected at Quiriz for ecommerce, and a first look at questions to ask is in Shopify AI: Ask Your Store Data Questions.

Frequently asked questions