Filter Shopify Analytics Reports

Filters let one Shopify analytics metric answer a focused question. Instead of calculating Net Sales from every order, you can calculate Net Sales from one customer segment, one discount campaign, B2B orders, a fulfillment status, a marketing source, or another supported slice.

Mixtable analytics uses two different kinds of filtering:

• Metric filters control which Shopify orders (or, for inventory metrics, which stock locations) contribute to a calculated metric
• Reporting-row conditions control which Shopify objects belong to a row in a Pivot Table worksheet or Time Series worksheet

These filters change the calculation itself. They are different from filtering visible worksheet rows after the values have been calculated.

Why filters are the most useful thing here

An unfiltered metric gives you one number for the whole store. That is a starting point, and almost every follow-up question needs a slice of it: not “how are sales”, but “how are sales to our wholesale accounts”, “how did the spring campaign do”, or “what do our repeat customers actually buy”.

Filters are also what lets a single worksheet compare things, because each column is filtered independently. Add Net Sales three times with three different filters and you have a side-by-side comparison that would otherwise take three exports and a pivot table.

A few things this makes easy:

• Put B2B and retail sales in adjacent columns and compare them directly
• Measure one discount campaign’s orders without hunting through individual codes
• See which products a specific customer segment buys, product by product
• Compare one sales channel or POS location against another over the same dates
• Separate a marketing channel’s revenue from everything else

The habit worth forming: change only one thing between columns. Keep the metric and the timeframe identical and vary the filter alone, or the difference you are looking at will not mean what you think.

Add a metric filter to an analytics column

1. Open a supported Shopify data worksheet or Pivot Table worksheet
2. Click the button in the header row of an empty column: ＋ Shopify data on a Shopify data worksheet, or ＋ Analytics data on a Pivot Table worksheet. You can also choose Column > Add Shopify data… from the menu bar
3. Choose the metric:
   • On a Shopify data worksheet, the field picker opens and asks What should column E show?, with your column’s letter in place of E. Click Sales & analytics in the list of sections on the left, or type the metric’s name in the search box, then click the metric. Its settings open with the metric already chosen
   • On a Pivot Table worksheet, the analytics settings open right away. Under Which metric?, select the metric
4. Under Over what time period?, choose a valid timeframe
5. Under Limit which orders count, click + Add filter
6. Select the filter type and value
7. Add more filters if needed
8. Click Save Column

The filter menu only shows filters supported by the selected metric. A fulfillment metric, for example, can offer fulfillment-specific filters that do not apply to an ordinary sales metric.

General Shopify order filters

Depending on the metric and target, general filters can include:

Filter availability varies by metric. Mixtable hides unsupported choices rather than applying a filter that the calculation cannot use.

To compare Shopify Markets, use a breakdown rather than a filter: choose Market when you create a Pivot Table worksheet or Time Series worksheet, and each Market gets its own row.

Filter by a Shopify discount campaign

The Discount code filter is designed for campaigns that may have many redeem codes. Its selector is searchable and presents the parent Shopify discount campaign, so you do not need to build a report code by code.

For a useful promotion comparison, add several analytics columns with the same timeframe and campaign filter:

• Gross Sales
• Total Discount Amount
• Net Sales
• Orders Count

This shows the order activity associated with the campaign. It does not prove that every order was caused by the discount, and it does not calculate incremental revenue.

See Shopify Total Discount Amount analytics.

Filter B2B Shopify analytics

To filter by Company or Company Location:

1. Add Order type (B2B / B2C)
2. Select B2B
3. Add Company or Company location
4. Select the company or location

The company filters depend on the B2B order type. They do not appear as valid dependent filters when the order type is B2C.

Use the same metric and timeframe across several company-filtered columns to compare B2B accounts directly. See Shopify B2B sales analytics.

Filter Customer Journey attribution

Attribution filters include:

• Has attribution
• First-touch source and source type
• First-touch landing page and referrer
• First-touch UTM source, medium, campaign, content, and term
• Last-touch source and source type
• Last-touch landing page and referrer
• Last-touch UTM source, medium, campaign, content, and term

Attribution values can vary across stores and campaigns, so many of these selectors let you search or type the value. Use consistent campaign naming in your UTM links to make the results easier to filter.

See Shopify Customer Journey and marketing attribution analytics.

Filter fulfillment analytics

Fulfillment metrics can offer:

• Fulfillment status
• Fulfillment service
• Fulfillment location
• Tracking company

Use these filters to compare warehouses, third-party fulfillment services, or carriers. Keep the metric and timeframe identical when comparing several filtered columns.

See Shopify fulfillment and delivery analytics.

Filter return analytics

Return metrics can offer:

• Return status
• Return reason

Return reason values come from returned line items. A return request containing several items can therefore contain more than one reason.

See Shopify return request analytics.

Filter inventory metrics by stock location

Inventory metrics, such as Closing Stock or Sell-Through Rate, count stock rather than orders, so they don’t take order filters. Their last step reads Which stock locations count (optional), and the only filter is Stock location:

1. Click + Add filter and choose Stock location
2. Pick a location
3. To count another location too, click + Add filter again and pick it. Each location shows as its own “Stock location is …” filter

Leave the step empty to count every location. Days of Stock Left, Estimated Lost Sales, Sell-Through Rate, and Inventory Turnover always count every location, because sales aren’t split by stock location.

To see your locations side by side instead, choose the Location breakdown when you create a Pivot Table worksheet or Time Series worksheet, which gives each stock location its own row. See the Shopify inventory analytics guide.

Add reporting-row conditions

Reporting-row conditions define which Shopify objects belong to a custom Pivot Table worksheet or Time Series worksheet row. This is useful for groups that are not represented by one standard vendor, category, or tag.

1. In a Pivot Table worksheet or Time Series worksheet, 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 the Shopify object type. In a Time Series worksheet, also choose the row’s metric under Which metric?
3. Select Only matching products. The label follows the object you chose, for example Only matching customers
4. Build the conditions
5. Enter a row label, or keep the suggested label
6. Click Save Row

The plain-language condition builder supports object-field comparisons that are valid for analytics reporting. Conditions such as Has a value and Has no value are useful for separating complete and incomplete Shopify data without guessing what blank value to enter. By default, the sentence above the conditions includes records that match all of them. You can switch it to any, or switch Include to Exclude to leave the matching records out instead.

Example custom rows include:

• Products from one vendor that carry a given tag
• Products with no product category
• Customers from one country
• Product variants where SKU has a value
• Collections published after a given date

In a Time Series worksheet, the row also stores the selected metric and its order filters. In a Pivot Table worksheet, the row defines the objects while the metric and timeframe come from the analytics columns.

Combine filters carefully

When you add several filters to one metric, an order must satisfy the combined filter setup to count. A Net Sales column filtered to B2B, one company, and one fulfillment status is much narrower than a column using any one of those filters alone.

If a value looks unexpectedly low:

• Remove the filters one at a time to find which condition is narrowing the result
• Check that the timeframe contains relevant Shopify orders
• Confirm that attribution, fulfillment, return, company, or location data exists on those orders
• Compare the filtered metric with an unfiltered version in the next column

Analytics filters and worksheet filters are different

An analytics filter changes the value calculated in the column. A worksheet filter only hides or shows rows after values have been calculated.

For example, filtering a Products worksheet to show vendor Northwind does not change how each product’s Net Sales was calculated. Adding a metric filter changes the Shopify orders included in that Net Sales value.

Use analytics filters to define the calculation. Use worksheet filters to focus the visible report.