How is assortment tracking different from price monitoring?

Price monitoring looks at what competitors charge. Assortment tracking looks at what they stock and how their range changes across marketplaces, Shopping and web shops. You need both for good decisions.

How many competitors should we track?

Start with three to five per category. Add more once matching and taxonomy are stable.

Is web shop tracking allowed?

Use publicly available information, respect robots.txt, site terms and local laws. NetRivals aggregates compliant signals to help you decide where to play.

Competitor catalogue assortment tracking: methods, metrics, 10 day plan

Competitor catalogue assortment tracking shows which products rivals sell, when they add or remove SKUs, and where your range is thin or strong. It covers marketplaces, Google Shopping and direct web shops (retailers and D2C brand sites). Used well, it reveals overlap, white space and new-in momentum so you can act early and grow profitably.

What is assortment tracking in plain terms?

You monitor the exact products that named competitors sell, SKU by SKU, across channels. You compare their range to yours and look for three things: overlap you must defend, white space you can enter, and trend shifts such as rapid new launches or quiet exits. Repeat this weekly or monthly so you do not miss turning points.

Channels to include

Marketplaces: Amazon, Zalando, Cdiscount, OTTO, eBay, Bol, Allegro and others.
Google Shopping: product ads and free listings that expose catalogue breadth by country.
Web shops: retailer and brand D2C sites. Track category (PLP) and product (PDP) pages.

When should you use it?

Category expansion: identify subcategories, brands or price bands to add next.
Range rationalisation: retire slow lines and double down on proven winners.
Seasonal planning: see which variants competitors launch for peak and how fast they scale.
Supplier negotiations: evidence gaps or exclusivity opportunities with facts.

Data you need

Accurate product matching: prefer GTIN/MPN or identical attributes. Avoid near matches.
Normalised taxonomy: shared brand and category trees across channels.
Channel signals: availability and price band by marketplace, Shopping and web shop.
Web shop structure: PDP/PLP URLs, canonical tags, and schema.org/Product JSON-LD (title, brand, GTIN, offers, availability) to improve identification.
Timestamps: when SKUs appear, disappear or return by channel and country.

Metrics that matter

Metric	What it means	How to calculate
Assortment overlap rate	Share of your SKUs that at least one key competitor also sells	Overlapping SKUs ÷ Your active SKUs
White space count	Competitor SKUs you do not carry	Competitor active SKUs − Your active SKUs (matched)
Channel presence matrix	Where competitors list: marketplace only, web shop only, both	Count SKUs by channel type and compare shares
Depth on hero lines	Variant coverage versus market (size, colour, capacity)	Your variants ÷ Variants offered in market
New-in rate	How fast a competitor adds range	SKUs first seen in period ÷ Active SKUs
Exit rate	How fast SKUs disappear from a competitor’s range	SKUs that went inactive ÷ Active SKUs
Price ladder coverage	Presence across entry, mid and premium bands	SKUs per band ÷ Total SKUs

Methods that work across channels

Exact matches first: use GTIN or identical attributes before any fuzzy logic.
Normalise names: unify brand and category naming to avoid false gaps.
Web shop parsing: extract PDP JSON-LD (schema.org/Product) and respect canonical URLs to reduce duplicates.
PLP sampling: crawl paginated category pages to reveal full range, then reconcile with PDPs.
Change detection: track new in, back in stock, out of stock and discontinued events by channel.
Price context: map each SKU to a band so you do not chase gaps with weak margin.
Compliance: use publicly available data, follow robots.txt and site terms, and apply sensible request rates.

Alert rules you can use from day one

Trigger	Condition	Action	Why
Competitor launches 5+ SKUs on their web shop	You lack those SKUs on any channel	Evaluate supplier shortlist and add 1 starter line	Enter emerging demand early
SKU listed on web shop but not on Shopping/marketplaces	D2C only for 14 days	Prepare content and request distribution or exclusivity	Secure access before rivals
Overlap above 80% in entry band	Thin margins	Rationalise tail variants and shift budget to mid band	Avoid me-too range with poor ROI
Depth below 50% on a hero line	Competitors cover sizes/colours you lack	Add top missing variants first	Win incremental units without full relaunch

Use cases by team

Buying and merchandising: build a white space roadmap by subcategory and brand across channels.
Pricing and revenue: align price bands and promo depth with true market coverage.
Performance marketing: push Shopping and retargeting to SKUs with unique coverage or deeper variants.
Marketplace and web shop ops: prioritise attribute enrichment and images for the next 100 SKUs to launch.

10-day plan to operationalise assortment tracking

Day 1: pick one country and two channels (for example Amazon plus top competitor web shop). Choose 200 SKUs with clear competitor overlap.
Day 2: confirm product matching at GTIN level and normalise brands and categories.
Day 3: compute overlap rate, white space count and channel presence matrix.
Day 4: measure depth for your top 10 hero lines and list missing variants.
Day 5: tag price bands and identify mid-band gaps with healthy margin.
Day 6: set alert rules for new-in bursts on web shops and exits on marketplaces.
Day 7: shortlist two brands or ranges to add and one tail to retire.
Day 8: prepare content and attributes for the first 50 new SKUs.
Day 9: launch and monitor diagnostics. Fix rejections quickly.
Day 10: review results and update the roadmap for the next subcategory.

Common pitfalls to avoid

Comparing near matches: different packs or bundles distort overlap and pricing.
Ignoring web shop variants: regional variants and exclusive colours can hide gaps.
One-off audits: run a monthly or weekly cadence, not a single snapshot.
Messy taxonomy: inconsistent brand and category names hide real gaps.