Assortment intelligence is the use of external market data, mainly competitor catalogs, to guide assortment planning decisions: which products and brands to add, where to offer more sizes or colors, which price tiers you are missing and which items you can drop. It shows what the market offers that your own sales data cannot.
Most retailers plan their range from internal numbers: what sold last season, what earned margin, what sat on the shelf. Those numbers are essential, but they have a built-in blind spot. They only describe the products you already carry. Demand for a product you never stocked shows up as zero.
This guide covers how to close that gap with competitor data: which metrics to compare, how to run a competitor assortment analysis step by step, how to turn the findings into concrete decisions, and how to keep your assortment plan current.
What Is Assortment Intelligence (and Where It Fits in Assortment Planning)
So what is assortment planning? Assortment planning is the process of deciding which products a store offers, in what variety, at which prices and for which season or channel. A retail assortment plan typically sets the number of products per category, the brands, the size and color range, the price points and the stock depth for each.
Assortment intelligence is one of the inputs to that process: the external view. It answers questions that internal reports cannot, such as which brands every major competitor stocks or which price tier is empty in your category.
It also helps to separate assortment planning from category management. Category management in retail treats each category as a business unit with its own targets for sales, margin, promotions and supplier terms. Assortment decisions are one part of it, alongside pricing, space and marketing.
Internal data | External competitor data | |
|---|---|---|
Answers | What sold, at what margin, how fast | What the market offers that you don't |
Sources | ERP, POS, store analytics | Public catalogs of competitors and marketplaces |
Blind spot | Demand for products you don't carry | Competitors' actual sales |
Typical owner | Merchandise planning, finance | Category managers, buyers |
A good merchandise assortment planning process uses both layers. Internal data tells you how your current range performs. Competitor data tells you what your range is missing. The rest of this article is about the second layer.
Why Your Own Sales Data Can't Show Assortment Gaps
Three structural problems make internal data alone unreliable for assortment planning in retail:
Missing products have no history. If shoppers want orthopedic dog beds and you don't sell them, your reports show nothing. They do not show low demand; they show no data at all. The sale happens elsewhere.
You only see what you once chose. Your product assortment is the result of past buying decisions. Analyzing it tells you which of your bets worked, not which bets you never made.
The market moves before your numbers do. A new material, a new brand or a new pack size can spread across competitor catalogs in a few months. Your sales data reflects it only after shoppers have already gone to the stores that adopted it.
Competitor catalogs fill exactly these gaps. They are public, they change continuously, and when you look at several competitors together they show what product assortment the market has collectively settled on for each category.
Competitor Assortment Metrics That Feed an Assortment Plan
Every metric below compares your catalog against the market in one category. The goal is not to describe a single competitor but to see where your range differs from what shoppers find elsewhere.
Metric | How to calculate | Data fields | Planning question |
|---|---|---|---|
Breadth | Number of unique products (or subcategories) per category, for you and each competitor | Category or breadcrumb, product URL | Is our range too narrow or too wide for this category? |
Depth | Average number of variants per product (sizes, colors, configurations) | Variants | Do we offer enough choice within each product? |
Brand coverage | Brands stocked by two or more competitors; your share of private label vs. third-party brands | Brand | Which brands are shoppers likely to expect from us? |
Price tier distribution | Share of products in entry, mid and premium tiers per category | Price | Is a whole price tier missing or overcrowded? |
Attribute coverage | Share of products with a given attribute (material, feature, pack size) | Attributes, filters, specifications | Are we missing a feature the market has adopted? |
Overlap rate | Share of your products that at least one competitor also sells, as identical or comparable items | Identifiers, brand, title, attributes | How exposed are we to direct comparison? |
Demand proxies | Competitor stockouts, review counts and ratings on products you don't carry | Availability, rating, review count | Is there visible demand behind a gap? |
A few notes on using these numbers:
Breadth and depth work together. Assortment breadth and depth describe two different choices: how many different products you offer and how many options each product has. A store can match the market on breadth and still lose shoppers because it stocks one size where competitors stock five.
Price tiers reveal positioning. Your price architecture is the way products are spread across entry, mid and premium price points. Comparing it with competitors shows whether you are the store shoppers visit for value, for choice or for premium items, and whether that matches your intent.
Demand proxies are directional only. Repeated stockouts at several competitors and a fast-growing review count suggest demand. They are not sales figures: review rates differ across stores and platforms, and a stockout can also mean a supply problem. For how to collect and read availability data, see our guide on competitor stockouts and variant-level availability.
All of these fields are visible on public product and category pages. Our documentation lists the standard and extended product fields that can be collected, including variants, attributes and brand-level collection.
These metrics describe a snapshot. How a competitor's range changes week by week is a separate question; we cover it in our guide on what to track in competitor monitoring.
How to Run a Competitor Assortment Analysis
A competitor assortment analysis turns raw catalogs into a clear list of gaps and overlaps. The six steps below work for a single category or a whole store. Start with one category you know well, then extend.
Step 1. Define the category and the competitor set for it
Competitors differ by category. For dog beds, the stores shoppers compare you with might be a large pet chain, a specialist orthopedic brand and a marketplace. For dog food, the set might be completely different. Define the category boundaries first (what counts as a "dog bed"), then pick the competitors that actually appear next to you in search results and marketplace listings for that category.
Step 2. Collect full category and brand listings
Assortment gap analysis needs complete category listings, not a list of matched SKUs. If you only collect products you already sell, you cannot find the ones you don't. Collect every product in the chosen categories (or for the chosen brands) at each competitor, with the fields from the table above: title, brand, category path, price, variants, attributes, identifiers, availability and rating data.
At this scale, manual copying stops being realistic. A dedicated service can collect full competitor category listings and return them as one structured table per store.
Step 3. Normalize categories and attributes
Every store organizes its catalog differently. One retailer files dog beds under "Dog Beds", another under "Pet Furniture > Beds & Mats". One lists "Memory foam", another "Visco-elastic foam", a third mentions it only in the description. Sizes may be S/M/L at one store and centimeters at another.
Before you compare anything, map each competitor's categories to your own taxonomy and bring key attributes to a common format: the same attribute names, the same units, the same value lists. Where competitor pages lack attributes you need, you may have to fill and standardize missing attributes from descriptions and specifications.
Step 4. Match identical and comparable products
For pricing, you need identical products. For assortment, you need two levels:
Identical: the same product sold by several stores. Match by GTIN, EAN or MPN where available, then by brand, model and key attributes. See how to match identical products across stores.
Comparable: a different product that serves the same need, such as another brand's memory-foam bed in the same size range and price tier. Group these by subcategory, price tier and key attributes.
The comparable level matters most for planning. A gap is rarely "we don't sell this exact SKU". It is usually "we have nothing in this segment".
Step 5. Build the overlap matrix
Sort every product, or every comparable group, into three buckets:
Bucket | What it means | What to do with it |
|---|---|---|
In your catalog and at competitors | Direct comparison; shoppers can choose | Check price position and depth |
At competitors only | A potential assortment gap | Score and prioritize (step 6) |
In your catalog only | Exclusive or niche products | Protect; decide whether they are a strength or dead stock |
Then cut the matrix by brand, price tier and attribute. A simple pivot table is enough: rows for subcategories or price tiers, columns for each store, product counts in the cells. Gaps become visible immediately.
Step 6. Score and prioritize the gaps
Not every gap deserves action. A product carried by one competitor is noise. A segment carried by three of four competitors is a pattern. Score each gap on a few simple criteria:
Market coverage: how many competitors carry it
Demand signals: stockouts, review counts and ratings at competitors
Fit: whether it matches your positioning and price strategy
Feasibility: supplier access, margin, minimum order quantities
The result is a short, ranked list of opportunities that your buying team can evaluate with internal data, rather than a long spreadsheet nobody reads.
From Assortment Gaps to Assortment Decisions
Finding gaps is half the work. The value comes from turning them into decisions in your assortment plan. The table below links typical findings to typical actions and to what you should verify in your own data first.
Finding | Likely decision | Check in your own data |
|---|---|---|
A product or brand that most competitors carry and you don't | Add it, or test it with a small order | Supplier terms, expected margin |
Your variant range is much shallower than the market's | Add sizes or colors on your best-selling products | Sales and stockouts by size and color |
A whole price tier is empty in your category | Add an entry or premium line | Fit with your store's positioning |
Many near-identical products in one tier | Candidates for SKU rationalization | Sales, margin and turnover per product |
Products only you carry | Keep them and protect their margin | Their sales trend and return rate |
Competitors are expanding a subcategory quickly | Decide whether to enter now or wait | Search and category demand trends |
Two points matter here. First, competitor data proposes; internal data decides. A gap that looks attractive can still fail on margin or supplier terms. Second, assortment optimization goes both ways. Removing weak duplicates frees budget, shelf space and attention for the products that fill real gaps. A clear assortment strategy usually involves both adding and cutting.
Exclusive products deserve a separate note. When shoppers cannot compare an item elsewhere, you have more pricing freedom. Our guide on competitive pricing explains how to price the products only you carry without giving away margin.
Once you decide to add products, speed matters. If the products come from a supplier's online catalog, there is no need to enter them by hand: you can import the supplier's data and test new products from supplier catalogs before committing to a full range. The same product data can also be delivered as import-ready catalog files for Shopify or WooCommerce.
Example: Assortment Analysis for a Pet Supplies Category
The scenario below is illustrative.
An online pet store sells 48 dog beds. Its buyer runs a competitor assortment analysis before the spring buying round, comparing the category with three competitors: a large pet chain (210 dog beds), a specialist store (120) and a marketplace seller with a strong position in the category (95).
After normalizing categories and grouping comparable products, the matrix shows:
Metric | Our store | Market (3 competitors) |
|---|---|---|
Products in category | 48 | 95–210 |
Average sizes per product | 2.1 | 3.8 |
Orthopedic / memory-foam beds | 2% of range | 18–25% of range at each competitor |
Premium tier (above $120) | 1 product | 14–31 products |
Near-identical budget beds under $40 | 9 | 3–5 per store |
Brands only we carry | 2 | — |
The findings translate into four decisions:
Add an orthopedic segment. All three competitors carry it, it is a large share of their ranges, and several of those products show frequent stockouts and high review counts. The buyer shortlists eight products from two suppliers for a test order.
Deepen sizes on the top sellers. The store's five best-selling beds come in two sizes; competitors offer four or five. Adding XL and XS sizes is cheaper than adding new products.
Cut four of the nine budget beds. Internal data shows they cannibalize each other. Their budget moves to the orthopedic test.
Keep the two exclusive brands. They have no direct comparison in the market, so the store keeps their prices and features them more prominently.
Without the competitor view, the internal reports would have shown a healthy budget segment and no demand for orthopedic beds. Both conclusions would have been wrong.
Keeping Your Assortment Plan Current
A competitor assortment analysis is not a one-off project. Ranges change with seasons, new brands and supplier changes, so the analysis should follow your planning cycle:
Before each buying or planning round, take a fresh snapshot of the relevant categories.
Monthly or quarterly for stable categories, to see direction rather than noise.
More often in fast categories such as fashion, seasonal goods or electronics accessories.
Keep every snapshot. A single snapshot shows gaps; a series shows trends, such as a competitor steadily building a premium tier or a brand quietly disappearing from the market. When you need to see which products appeared or disappeared between two dates, the simplest method is to compare two catalog exports in a spreadsheet.
After you act on a gap, check it again in the next cycle. Did competitors keep the segment? Did your new products sell? Retail assortment planning improves when every decision is reviewed against fresh market data.
Assortment Planning Software vs Competitor Data: What Each Covers
Three types of tools come up when retailers look for assortment intelligence, and they solve different problems:
Assortment planning software. Retail assortment planning software works with your internal data: sales history, forecasts, open-to-buy budgets, store clusters and planograms. It is strong on "how much of what we sell", but it does not know what competitors offer unless you feed it that data.
Catalog and digital shelf analytics platforms. These collect competitor data and present it in their own dashboards and reports. Convenient, but the analysis follows their model and their categories.
Product data services. These deliver competitor catalogs as structured data in your format, ready to go into your spreadsheets, BI tools or assortment planning tools.
The first and third types complement each other: planning software handles internal performance, and a data service supplies the external market view that planning software lacks. Some teams also consider collecting competitor data themselves; before you decide to build the data collection in-house, weigh the ongoing maintenance cost.
Getting Competitor Assortment Data with ShopScraping
ShopScraping collects product data from public e-commerce websites and marketplaces and delivers it as clean, structured tables. For assortment intelligence, that means:
Full competitor catalogs, or selected categories and brands
Product name, brand, category and breadcrumb path
Prices, sale prices and labels such as "Sale" and "New"
Variants (size, color, configuration) and stock status
Attributes, specifications and identifiers such as SKU, EAN and GTIN
Ratings and review counts where the source website shows them
You choose the fields and the schedule: one-off, daily, weekly or monthly. You can export to CSV, Excel, JSON or Google Sheets.
What we don't do: estimate competitors' sales, build your assortment plan, compare runs or send alerts, or analyze your internal sales data. ShopScraping is the data layer for your competitor assortment analysis. Your team draws the conclusions in the spreadsheets, BI tools or planning software it already uses.
See plans and pricing, or start collecting competitor catalog data.
FAQ
What is assortment intelligence?
Assortment intelligence is the use of external data, mainly competitor catalogs, to inform assortment decisions. It shows which products, brands, variants and price tiers the market offers that your range lacks, and where your range overlaps with competitors.
What is the difference between assortment planning and category management?
Assortment planning decides which products a store carries, in what variety and at what price points. Category management is broader: it runs each category as a business, covering assortment along with pricing, promotions, space and supplier relationships.
How do you find assortment gaps?
Collect full category listings from the competitors your shoppers compare you with, normalize categories and attributes, and match identical and comparable products. Then sort products into three buckets: carried by both, carried only by competitors, carried only by you. Gaps carried by several competitors and backed by demand signals are the ones worth acting on.
How often should you analyze competitor assortments?
Align it with your planning cycle: a fresh snapshot before each buying round, plus a monthly or quarterly review for stable categories. Fast-moving categories such as fashion or seasonal goods need more frequent checks.
Can competitor data show which products sell best?
Not directly. Competitors' sales are not public. Stockouts, review counts and ratings give directional signals of demand, but they are not sales figures. Use them to prioritize gaps, then validate with your own test orders and sales data.
Is it legal to collect competitor catalog data?
Collecting publicly available product information is common practice in e-commerce, but the details matter: respect website terms, don't overload servers, don't bypass logins and don't collect personal data. Rules differ by country. Our guide Is Web Scraping Legal? covers the main points. This is general information, not legal advice.
