What is fashion ecommerce analytics?
Fashion ecommerce analytics means collecting and reading the data an online clothing, shoe or accessories shop produces: what people look at, what they add to the basket, what they buy, what they send back and what it cost to sell it. The point is practical. It tells you which categories earn their place, which products need a price change and which discounts are giving margin away for no reason.
Fashion is harder to read than most retail. Ranges change every season, every style comes in several sizes and colours, and prices move a lot through the year as stock is marked down. That is why the useful reports in fashion are almost always split by category, by size and by discount band rather than shown as one store-wide average.
The dashboard above, built in Tableau by Irene Shi, is a good example of this kind of analysis. She made it to understand the women’s fashion market before choosing a product category for a small ecommerce business [1]. It looks at 29,575 products across seven categories, which is enough to show how differently each category behaves.
The metrics that matter most for an online fashion store
There are hundreds of things you could measure. These are the ones most fashion retailers come back to, because each one leads to a decision:
- Conversion rate: the share of sessions that end in an order. Track it by device and by category, as a low rate on one category usually points to a product page, price or size problem.
- Average order value (AOV): revenue divided by orders. Bundles, free delivery thresholds and cross-selling are the usual levers.
- Sell-through rate: units sold in a period divided by stock available, times 100 [3]. In fashion it shows which lines need reordering and which will need marking down before the season ends.
- Discount depth: how far below the full price products actually sell. A store can look busy while selling most of its range at a loss.
- Return rate: returns as a share of orders or revenue, ideally by product and by size.
- Gross margin by category: what is left after the cost of goods, discounts and returns. This is the figure that tells you which categories to grow.
- Customer lifetime value and repeat rate: how much a customer spends over time, which sets how much you can afford to pay to acquire one.
Why you should analyse a fashion catalogue by category
Store-wide averages hide most of what is going on. In Irene Shi’s dataset, Indianwear and Westernwear each have 9,975 products, so together they make up about two thirds (67%) of the catalogue. Lingerie and nightwear has 3,225, footwear 2,475, jewellery and watches 1,725 each, and fragrance just 475 [1].
Prices vary even more than range size. The average price is 8,657 for watches and 3,972 for fragrance, against 2,194 for footwear, 2,164 for jewellery, 1,484 for Indianwear, 1,318 for Westernwear and 918 for lingerie and nightwear [1]. A watch sells for more than nine times the price of the average lingerie item, so one watch order can be worth as much as a basket of clothes.
That matters when you choose what to sell. The two biggest categories are also among the cheapest, which usually means more competition, more sellers and thinner margins per order. The smaller, higher-priced categories (watches, fragrance) bring larger orders but need more trust from the buyer, better photography and often brand authorisation.
The dashboard doesn’t state a currency. The MRP label (maximum retail price, the list price printed on goods in India) and the Indianwear category suggest an Indian marketplace catalogue, so read the prices as relative to each other rather than as pounds or dollars.
How deep do fashion discounts go?
Discounting is the biggest single thing that separates fashion ecommerce from other retail, and it is where analytics pays for itself. Across the 29,575 products the average discount is 34.23% [1]. The most common band is 45 to 59% off, which covers 9,717 products, about a third of the catalogue. Almost half of all products (48%) are discounted by 45% or more, and about 15% by 60% or more. At the other end, 7,791 products (26%) have less than 15% off [1].
Again the averages hide big differences between categories:
| Category | Products | Under 15% off | 45% off or more |
|---|---|---|---|
| Indianwear | 9,975 | 17% | 67% |
| Westernwear | 9,975 | 19% | 47% |
| Jewellery | 1,725 | 53% | 37% |
| Lingerie and nightwear | 3,225 | 39% | 36% |
| Footwear | 2,475 | 35% | 32% |
| Watches | 1,725 | 49% | 16% |
| Fragrance | 475 | 68% | 2% |
Source: product counts by discount band from Irene Shi’s dashboard [1]; percentages are our arithmetic.
Two thirds of Indianwear is sold at 45% off or more, and a quarter of it at 60% or more. Fragrance is the opposite: two thirds of it carries less than 15% off and almost none is heavily discounted. Jewellery is split, with half barely discounted and almost a quarter at 60% or more.
For a seller this is the useful part. A category where most products sit at 45 to 60% off is one where the “full price” is largely notional and buyers expect a deal, so your margin plan has to start from the discounted price. A category where products rarely go below 15% off gives you more control over pricing.
Average selling price versus list price
In this catalogue the average list price (MRP) is 2,817 and the average selling price is 1,924, about 32% lower [1]. That is close to the 34% average discount, but not the same, because one figure averages prices and the other averages discount rates. When you report discounts, say which one you mean.
Prices within each category are also skewed. In every category except fragrance, more products sit below the category’s average price than above it: about 72% of jewellery, 66% of watches, 64% of lingerie and nightwear, 61% of Westernwear, 60% of footwear and 58% of Indianwear [1]. A few expensive items pull the average up. For pricing decisions the median price, or price bands, will usually tell you more than the mean.
Returns, fit and size data
Returns are where fashion analytics most often falls short. The National Retail Federation and Happy Returns projected total US retail returns of $890 billion for 2024 [4], and clothing and shoes are among the most returned categories because people order several sizes and send back what doesn’t fit.
The fixes start with data. Record the return reason for every item (too small, too big, not as pictured, faulty), track return rate by product and by size, and look for products where one size is returned far more than the others. That usually means the size guide or the product description is wrong, not that customers are being difficult. Count returns against revenue and margin too, or your best-selling product may turn out to be one of your least profitable.
How to set up fashion ecommerce analytics
You don’t need a data team to get started. Most of the useful reports come from three sources you already have:
- Website analytics. Google Analytics 4 has standard ecommerce events for viewing item lists and items, adding to and removing from the basket, starting checkout, purchases and refunds [5]. Set these up properly, with product category, size and colour passed as item parameters, and you can see where people drop out by category.
- Your shop platform. Shopify and similar platforms report sales, sell-through and returns by product [3]. Export these monthly.
- Your product catalogue. List price, selling price, cost, category and stock for every product. This is the data behind a dashboard like Irene Shi’s, and it is the part most small shops never join up with their sales data.
A free tool such as Tableau Public, Looker Studio or a spreadsheet is enough to bring them together. Start with sales, margin, discount and returns by category, then add size and channel once those are working.
Questions about fashion ecommerce analytics
What KPIs should a fashion ecommerce store track?
Conversion rate, average order value, sell-through rate, discount depth, return rate, gross margin by category and customer lifetime value. Split each one by category and, where you can, by size, because fashion averages hide most of the detail.
What is a good average discount for online fashion?
There isn’t one figure. It depends on the category and the market. In Irene Shi’s catalogue of 29,575 women’s fashion products the average discount is 34%, but it ranges from mostly under 15% for fragrance to 45% or more for two thirds of Indianwear [1].
How do you calculate sell-through rate?
Divide the units sold in a period by the stock available for sale in that period and multiply by 100 [3]. If you had 1,000 units and sold 800, your sell-through rate is 80%.
What is MRP in ecommerce?
MRP stands for maximum retail price, the highest price a product can be sold for in India, printed on the packaging. Online sellers in India usually show MRP alongside the selling price so the discount is visible.
Which tools are used for fashion ecommerce analytics?
Google Analytics 4 for site behaviour [5], the reports in your shop platform for sales, stock and returns, and a dashboard tool such as Tableau or Looker Studio to combine them. Larger retailers add dedicated merchandising, pricing and fit tools.
Sources
- Irene Shi, Women Fashion Ecommerce, Tableau Public, first published 12 August 2024 (the dashboard shown above).
- Irene Shi, Tableau Public profile.
- Shopify, Sell-through rate: what it is and how to calculate it.
- National Retail Federation and Happy Returns, 2024 Consumer Returns in the Retail Industry, 5 December 2024.
- Google for Developers, Measure ecommerce in Google Analytics 4.
Last reviewed 4 November 2026
