Shopify Sales by Vendor, Category, Type, and Tag

Product attributes turn a catalog into useful Shopify analytics groups. Mixtable can compare sales by vendor, product type, standardized product category, and product tag without requiring you to export line items and group them manually.

Why group products instead of ranking them

A product-by-product ranking is useful right up until you have a few hundred products. After that it tells you which individual items sell and almost nothing about your business, because the pattern lives one level up.

Grouping by vendor, product type, category, or tag is where the useful questions get answered:

Which suppliers are worth better terms, and which are not worth reordering from
Whether a whole category is growing or one product inside it is carrying everything
How the products you tag as new, sale, or seasonal perform as a group
Where discounting is concentrated, which is usually a category habit rather than a product decision
Which parts of the catalog deserve more range and which are overstocked

The same product appears in each of these groupings, so you are not looking at different data. You are looking at the same sales through a supplier lens, a merchandising lens, or a campaign lens, and each one supports a different decision.

Each breakdown answers a different merchandising question, and the totals do not all behave the same way.

Choose the right Shopify product breakdown

Breakdown	What it uses	Best for
Vendor	The product vendor in Shopify	Supplier and brand performance
Product type	Your store’s custom product-type value	Internal assortment groupings
Product category	Shopify’s standardized taxonomy	Consistent category reporting
Product tag	Any product tag assigned in Shopify	Campaign, feature, season, or merchandising labels

Each of these fills the whole report in one step, giving you a row for every vendor, type, category, or tag in your store.

Group products your own way

The four breakdowns above cover the groups Shopify already knows about. When the group you care about does not match a single Shopify field, build the row yourself instead.

In any Pivot Table worksheet, right-click an empty row’s number and click Choose what to analyze…, or choose Row > Choose what to analyze… from the menu bar. In the Analytics Row Settings window, choose the Shopify object to analyze under What do you want to analyze?, select the Only matching option (for example, Only matching products), and write the conditions. The conditions start as Include items that match all of them. Switch to any, or to Exclude, when your group needs it. Click Save Row, and the row sits beside your generated rows in the same report.

This is what lets a report answer a question no single attribute describes:

Combine two attributes. One row for a vendor’s products that also carry your sale tag, beside a row for the same vendor’s untagged products
Split by cost. One row for variants with a cost above a threshold and another for those below, to compare how your price tiers really perform
Compare new against established. One row for products created this year, another for everything older
Isolate a data gap. One row for products with no product category assigned, or variants with no cost recorded, to see how much revenue is sitting in records you cannot report on properly
Segment customers yourself. Rows for customers above a lifetime spend threshold, above an order count, or in a particular country

Conditions use the fields Mixtable stores for analytics. For products that is vendor, product type, product category, product tag, and created or updated dates. For variants it is SKU, cost, and dates. For collections it is the collection and its published or updated dates. For customers it is total spent, order count, and default address country or city.

Give each custom row a clear label, since the label is what tells anyone reading the report which group they are looking at. For the full walkthrough, see add a custom reporting row.

Compare sales by vendor

Use vendor analytics for supplier, brand, and purchasing decisions. The ready-made Vendor sales leaderboard compares Net Quantity Sold, Gross Sales, discounts, refunds, and Net Sales.

For trends, use Vendor net sales by year, or switch it to By month, By week, or By day columns in the builder before you create the worksheet.

Useful questions include:

Which vendors carry the most Net Sales?
Which rely heavily on discounts?
Which produce high refund value?
Which are growing or declining over time?

See the Sales by Vendor template for a complete new spreadsheet.

Compare sales by product type

Product type is a custom Shopify field controlled by the store. It is useful when your internal merchandising structure does not match Shopify’s standardized category taxonomy.

Mixtable supports product type in Pivot Table worksheets and Time Series worksheets. Use Product type net sales by month to compare seasonality and recent momentum.

Product-type reporting is only as consistent as the values in Shopify. T-Shirt, T-Shirts, and Tees become separate groups. Clean the underlying product types before using the report for an important comparison.

See the Sales by Product Type template.

Compare sales by Shopify product category

Product category uses Shopify’s standardized taxonomy. Products without a category are grouped into a clear Uncategorized row.

The ready-made Category sales breakdown includes Net Sales, Gross Sales, Net Quantity Sold, discounts, and refunds.

Use category reporting for:

Comparing major merchandise families
Finding categories with high refunds or discount dependence
Checking whether uncategorized products represent meaningful sales
Planning category-level assortment or content work

See the Sales by Product Category template.

Compare sales by product tag

Tags are flexible and can represent season, material, audience, campaign, supplier status, margin tier, or any other system your team maintains.

The ready-made Product tag scorecard includes Net Sales, Gross Sales, Net Quantity Sold, Orders Count, and refunds.

One product can have several tags, so the same Shopify order activity can contribute to several tag rows. Do not add every tag row together and treat the result as store sales.

Tag analytics is best for comparing tag-defined groups, not reconciling a mutually exclusive total.

See the Sales by Product Tag template.

Compare sales by collection

Collections are not a breakdown in Pivot Table or Time Series worksheets. They represent Shopify storefront merchandising groups, and like tags, they can overlap because one product can belong to several collections.

To compare curated groups, navigation structures, seasonal collections, or campaigns, use the Collection sales & refund rate report under Shopify data worksheet with analytics columns. It creates a Collections worksheet with one row per collection and analytics columns for sales, quantities, orders, and refund rate. See Shopify Collection Analytics.

Create a product-attribute Pivot Table worksheet

Click the + at the end of the worksheet tabs, or choose Worksheet > Add worksheet…
Select Reporting worksheet, then click Continue
Choose a ready-made Pivot Table report or Blank pivot table
Click Continue
Under Break down by, choose Vendor, Product type, Product category, or Product tag
Select the metrics under Metrics
Choose the timeframe under Time period
Click Create Worksheet

Build a better scorecard

Use a combination rather than ranking by one number:

Gross Sales for original demand
Total Discount Amount for promotion dependence
Total Refund Amount Excluding Tax for money returned
Net Sales for retained merchandise revenue
Net Quantity Sold for unit movement
Orders Count for order frequency

Add formula columns for discount share, refund share, or growth. Then sort by the metric tied to the decision.

Add stock to the scorecard

The four breakdowns can also show inventory metrics, which answers the overstock question directly. In the builder, the Inventory group under Metrics includes Closing Stock, Closing Value at Cost, Sell-Through Rate, and Dead Stock Units, so a vendor report can show how much stock and cash each supplier ties up next to what it sells. Inventory metrics don’t offer All time, because Shopify’s stock history starts on October 1, 2023. See the Shopify inventory analytics guide.

Keep Shopify product data clean

These reports inherit their row labels from Shopify. Improve report quality by:

Standardizing vendor spelling
Consolidating duplicate product types
Assigning standardized product categories
Removing obsolete or redundant tags
Using clear collection names

Mixtable analytics can reveal inconsistent groups, but the correct fix is usually to clean the source product fields and intentionally sync those changes to Shopify.