Create a Time Series Worksheet for Shopify Data

A Time Series worksheet tracks your Shopify sales, orders, and stock across recent days, weeks, months, or years, such as Net Sales by month for the last three years. It turns a total into a trend, which helps you spot seasonality, changing customer behavior, vendor momentum, market growth, marketing-channel shifts, and changes in your stock.

In this worksheet type, time periods live in the columns. The rows define either the store metrics you want to follow or the Shopify groups you want to compare.

Choose the Time Series layout

The layout changes depending on the breakdown.

Store breakdown

When you choose Store, each selected metric becomes a row. Recent days, weeks, months, or years become the columns. Store metrics available in the builder include:

• Net Sales
• Gross Sales
• Orders Count
• Average Order Value
• Net Quantity Sold
• Total Refund Amount Excluding Tax
• Refund Count
• Total Discount Amount

Below them, an Inventory heading lists 14 stock metrics, such as Closing Stock, In-Stock Rate, and Sell-Through Rate. See track inventory over time.

This is the best layout for a monthly or yearly Shopify performance summary. Each row keeps its metric across every column, so reading down a single month gives you the whole picture for that month.

Business-dimension breakdown

When you choose a business dimension, each group becomes a row and one selected metric is measured across the time-period columns. Available breakdowns include:

• Vendor
• Product type
• Shopify Market
• Customer segment
• Marketing channel
• Location (one row per stock location, for inventory metrics)

The builder offers Net Sales, Gross Sales, Orders Count, Net Quantity Sold, Total Refund Amount Excluding Tax, and Average Order Value as the single measure for these grouped reports, but not every measure fits every breakdown:

• Vendor and Product type rows also offer the 14 Inventory metrics. They can’t calculate Average Order Value, so its cells show “N/A”
• Market, Customer segment, and Marketing channel rows offer the six sales measures and no inventory metrics
• Location rows offer only inventory metrics: 11 of them, leaving out Estimated Lost Sales, Sell-Through Rate, and Inventory Turnover, because sales aren’t split by stock location

The Marketing channel breakdown uses the last-touch marketing source associated with the Shopify customer journey.

These breakdowns generate the rows for you, but they are not the only rows available. You can add your own rows with conditions you define, which is covered under add a row after creation below.

Start with a ready-made Time Series report

Mixtable includes:

• Vendor net sales by year
• Store sales and orders by month
• Product type net sales by month
• Customer segment net sales by month
• Marketing channel net sales by month

These are starting points. Before creating the worksheet, you can change the breakdown, the measure, the time periods (days, weeks, months, or years), and how many periods to include.

Create a Time Series worksheet

1. Click the + button beside the worksheet tabs, or choose Worksheet > Add worksheet…
2. Select Reporting worksheet, then click Continue
3. Under Time series, select a ready-made report, or choose Blank time series under Start from scratch
4. Click Continue
5. Under Break down by, choose Store or a business dimension
6. Under Metrics or Measure, choose what the rows should calculate
7. Under Time periods, choose By day, By week, By month, or By year
8. Next to “for the last”, choose how many recent periods to include
9. Review the sample preview
10. Click Create Worksheet

Mixtable can create up to 90 daily columns (30 by default), 52 weekly columns (12 by default), 36 monthly columns, or 10 yearly columns. Choose enough history to reveal the pattern without making the worksheet harder to scan.

Pick days, weeks, months, or years

Use daily columns when you need to see:

• The effect of a sale, launch, or email campaign day by day
• Stock levels as they rise and fall between deliveries
• Recent changes you want to catch quickly

Use weekly columns when you need to see:

• Recent trends without day-to-day noise
• Whether a promotion kept working after its first week
• Weekly stock and sales movement

Use monthly columns when you need to see:

• Seasonal peaks and slow periods
• Campaign or promotion effects
• Recent changes in refunds or discounts
• Whether a vendor or Market is gaining momentum

Use yearly columns when you need to see:

• Long-term growth
• Structural changes in the product mix
• Vendor relationships over several buying cycles
• Whether customer segments are becoming more or less valuable

Daily and weekly columns stop at yesterday and last week (weeks run Sunday to Saturday), so every column covers a complete period. Monthly and yearly columns start with the current month or year, which is still in progress, so the newest column covers only the period so far.

A yearly view can hide short spikes. A monthly view can make normal seasonality look alarming. When the decision matters, create both views or add both monthly and yearly worksheets.

Add a row after creation

In a Time Series worksheet, each row defines the metric, the Shopify objects it measures, and any order filters.

Like every Reporting worksheet, its linked column letters and row numbers are blue rather than the green used on Shopify data worksheets.

1. Right-click an empty row’s number and click Choose what to analyze… at the top of the menu. You can also select an empty row and choose Row > Choose what to analyze… from the menu bar. The Analytics Row Settings window opens
2. Under What do you want to analyze?, choose Products, Product Variants, Collections, Customers, or Store
3. Under Which metric?, select the metric for the row
4. Under Which products should this row include?, keep All products for every record, or choose Only matching products and add the conditions. The step adapts to the object you picked
5. Under Refine which orders count, add optional order filters
6. Under Row label, keep the automatic name or type your own
7. Click Save Row

Step 5 is where a row stops being a store-wide total. The filter menu offers the order filters supported by the metric, such as customer segment, customer country, discounts, repeat customers, and fulfillment status.

Because each row carries its own metric, object group, and filters, a single worksheet can track things a breakdown could never produce side by side:

• Net Sales for one vendor’s tagged products, beside that vendor’s other products
• Order Count for customers above a given lifetime spend
• Net Sales for variants above a cost threshold, beside those below it
• Refund totals for one collection, beside store-wide refunds

Give each row a clear label, since the labels are the only thing distinguishing rows that use the same metric.

Add a time-period column

Each Time Series column applies its date range to every configured row.

1. Click ＋ Analytics data in the header row of an empty column, or choose Column > Add Shopify data… from the menu bar. The time period settings open right away
2. Under Time period for this column, select All time, Fixed date range, Calendar year, or Calendar month
3. Set the dates for the option you chose
4. Click Save Column at the bottom of the window

A Time Series column carries only the dates. The metric and the Shopify records come from each row, which is why these columns offer calendar periods rather than the rolling periods available on a Pivot Table or analytics column.

The columns the builder creates are headed by their period: 2026-09-30 for a day, Week of 2026-09-20 for a week, 2026-09 for a month, and 2026 for a year. Use clear labels when adding custom periods, especially when a column does not follow the same sequence as the original report.

Track inventory over time

Stock changes every day, so a Time Series worksheet is a natural place to follow it:

• Choose Store and pick metrics from the Inventory heading, such as Closing Stock and In-Stock Rate, to follow your whole store
• Choose Vendor or Product type with an inventory measure to compare groups of products
• Choose Location to give each stock location its own row
• Pick By day or By week columns to watch stock move between deliveries

Inventory history runs from October 1, 2023 through yesterday. Until your store’s history has finished downloading, the cells read “Waiting for inventory history”, and a column that starts before October 2023 can show “No inventory history”.

A custom row lists inventory metrics under Which metric? only when it analyzes Products or Store. On such a row, Refine which orders count offers the Stock location filter instead of order filters. To learn what each metric tells you, see the Shopify inventory analytics guide.

Compare trends responsibly

Before acting on a change, check the metric beside at least one supporting measure:

• Net Sales rising while Orders Count stays flat usually means larger baskets
• Gross Sales rising while Net Sales stays flat can mean heavier discounts or refunds
• Net Quantity Sold rising faster than sales can mean lower-priced products are taking a larger share
• Refund Amount rising with sales may be normal, while a faster refund increase deserves investigation
• Marketing-channel sales can move because attribution coverage changed, not only because the channel changed

Add formula columns for month-over-month or year-over-year growth, or copy the report into an exported Excel file for additional modeling.

For a ranked comparison over one timeframe, use a Pivot Table worksheet.