Competitor markdown tracking is the regular collection of competitors' public prices, reference prices, clearance badges and stock messages so you can see when they cut prices, how deep the cuts go and how often they repeat. It helps retailers and brands time their own markdowns, protect margin and avoid matching cuts that are really clearance.
Discounting is now a core part of peak season. Adobe reported that US shoppers spent $257.8 billion online from 1 November to 31 December 2025, with discounts peaking at 30.9% off list price in electronics and 25.1% in apparel Adobe 2025 holiday season report. NRF counted a record 202.9 million shoppers over the 2025 Thanksgiving-to-Cyber-Monday weekend NRF, 2 December 2025.
This guide explains which signals to collect, how to measure markdown depth and cadence, how to read stock signals as sell-through proxies, and when to watch most closely in 2026.
Competitor markdown tracking tells you more than a price change. It hints at a competitor's stock position, season plan and margin pressure, which helps you decide whether to follow, hold or wait.
Interest in competitor markdown tracking is growing. Pricing and competitor intelligence inquiries to Actowiz rose this week (four genuine requests versus one the week before), and one US retail-analytics client asked for store-level online price, availability and clearance flags by item and ZIP code.
For competitor markdown tracking, online markdowns show up as a handful of visible page elements. Each one should become a separate field so you can tell a temporary deal from a permanent clearance.
| Signal | What it looks like | What it usually means | Field to store |
|---|---|---|---|
| Strike-through price | Crossed-out price above the current price | Price cut against a stated reference | price_reference, price_current |
| Was/now text | "Was $49.99, now $29.99" | Explicit markdown with stated depth | was_price, now_price |
| Clearance badge | "Clearance", "Final sale", "Reduced to clear" | Permanent markdown to exit stock | clearance_flag, badge_text |
| Rollback or price-drop label | Retailer-specific price-cut label | Temporary or longer-term cut | promo_label |
| Coupon | Clip coupon, promo code, cart discount | Discount not shown in the shelf price | coupon_value, coupon_type |
| Bundle or multi-buy | "2 for $30", "Buy 2 save 15%" | Per-unit markdown hidden in a deal | bundle_terms, unit_price_effective |
| Sale or outlet page | End-of-season or outlet category | Range-wide markdown event | listing_source, sale_page_url |
| Low-stock message | "Only 3 left", limited sizes | Stock running down (proxy only) | stock_message, sizes_available |
In the EU, price-reduction announcements must state the lowest price of the prior 30 days, and the UK has its own pricing-practices guidance. So the reference price itself is regulated, and it is worth storing exactly as shown.
Markdown depth is the percentage cut from a stable reference price; cadence is how often and how far apart the cuts come. Competitor markdown tracking needs a clean price history per product to measure either one.
Use a consistent baseline. The displayed reference price is easy but can be inflated, so also compute depth against the product's own regular price, for example the most common price over the previous 30 to 60 days.
A big gap between displayed and observed depth suggests an inflated reference price. Our Holiday 2026 US retail price war report looks at that question for peak season.
Cadence shows the competitor's playbook. Record the date of each step-down, the depth of each step and the days between them.
You cannot see a competitor's sales, but public availability signals give a rough, indirect read. Treat them as proxies, never as sell-through figures.
In competitor markdown tracking, pair these signals with markdown steps. A deep cut followed quickly by size breaks suggests the cut worked; a second cut with full sizes still available suggests it did not. Both are inferences, so label them as such in reports.
Markdown activity clusters around a few predictable windows. Increase competitor markdown tracking frequency before each one and keep it high until clearance ends.
| Window | 2026 dates | What to watch |
|---|---|---|
| Autumn/winter build-up | October to mid-November | Early "Black Friday" pricing, reference-price changes |
| Thanksgiving (US) | Thursday 26 November 2026 | Early-access deals and doorbusters |
| Black Friday | Friday 27 November 2026 | Peak depth across electronics, toys, apparel |
| Cyber Monday | Monday 30 November 2026 | Online-only cuts, coupons, bundle deals |
| December price drops | 1 to 24 December 2026 | Second markdowns and last-shipping-day deals |
| Boxing Day (UK) | Saturday 26 December 2026 (bank holiday on Monday 28 December) | Clearance badges and end-of-season sale pages |
| January clearance | Late December to January 2027 | Final-sale cuts on winter ranges |
| Summer end-of-season | Typically late June to August | Apparel, garden and outdoor clearance |
For December specifically, see our December price-drop deal mapping across Amazon, Walmart and Target.
Each category marks down for different reasons and at a different pace, so your competitor markdown tracking setup should differ too. The table combines Adobe's 2025 peak-discount figures with the signals we see most often.
| Category | Typical markdown driver | Key signals | Peak 2025 US online discount (Adobe) | Suggested cadence |
|---|---|---|---|---|
| Apparel | Season end, size breaks | Clearance badges, sizes left, outlet pages | 25.1% | Daily; twice daily in peak weeks |
| Electronics | New models, event pricing | Was/now, coupons, bundles, open-box | 30.9% (TVs 24.3%, computers 23.4%) | Several times a day around events |
| Grocery | Short shelf life, promotions | Rollback-style labels, multi-buys, unit price | Not reported | Daily, by store or ZIP where available |
| Home and furniture | Range resets, bulky stock | Clearance pages, delivery offers | Furniture 18.8%, appliances 20.2% | Daily; weekly for long-tail items |
Source for discount figures: Adobe 2025 holiday season report. Grocery markdowns on short-dated stock often happen in store and may not appear online.
Retailers and brands use competitor markdown tracking data from different sides. Retailers want to price against rivals; brands want to see how their products are treated.
A good markdown dataset is one row per product, per retailer, per location, per crawl. Keep raw values as shown and add derived fields separately.
| Field | Description |
|---|---|
| retailer / country | Retailer name and market (e.g. Walmart US, a UK fashion retailer) |
| product_id / upc_gtin | Retailer item ID and UPC/GTIN where shown, for matching |
| title / brand / category | Product identity and category path |
| store_id / zip | Location for store-level online prices, where published |
| price_current | Price shown to the shopper at crawl time |
| price_reference | Strike-through or "was" price as shown |
| promo_label / clearance_flag | Badge text and a yes/no clearance flag |
| coupon_value / bundle_terms | Extra discounts not in the shelf price |
| availability / stock_message | In stock, out of stock, low-stock text |
| sizes_available | Variants still available (apparel, footwear) |
| crawl_timestamp | Date and time of collection |
| depth_displayed / depth_observed | Derived markdown depth fields |
Public data only: collect what any shopper can see without logging in, respect each site's terms and rate limits, avoid personal data, and get legal review before you start.
Build if you track a small, stable list of products on a few sites. Buy if you need many retailers, store-level data or peak-season frequency, because maintenance grows fastest exactly when markdowns peak.
| Factor | Build in-house | Managed service |
|---|---|---|
| Speed to start | Weeks of engineering per retailer | Scope, sample, then schedule |
| Peak-season load | You scale crawls and fix breakages | Provider scales and monitors |
| Product matching | Your own UPC and attribute rules | Matching with QA checks |
| Store-level data | Complex location handling | Store or ZIP logic set up per retailer |
| Output | Raw files | CSV, JSON, API or dashboard |
Actowiz provides price monitoring services and retailer-specific feeds such as Walmart search results data.
It is the regular collection of competitors' public prices, reference prices, clearance labels and stock messages to see when they cut prices, how deep and how often.
A promotion is temporary and the price returns afterwards. A markdown stays down, and clearance is a markdown meant to sell out the remaining stock.
Subtract the current price from a stable regular price and divide by the regular price. Compare that with the displayed reference price to spot inflated "was" prices.
Not directly. Size breaks, low-stock messages, out-of-stock duration and listing removals are proxies that suggest how stock is moving, and they should be labelled as such.
Daily competitor markdown tracking is enough for most categories. Move to several times a day for electronics and apparel around Black Friday, Cyber Monday and Boxing Day.
Where a retailer publishes online prices and availability by store or ZIP code, those public values can be collected. In-store shelf tags that are not online cannot.
You can also reach us for all your mobile app scraping, data collection, web scraping, and instant data scraper service requirements!
Our web scraping expertise is relied on by 4,000+ global enterprises including Zomato, Tata Consumer, Subway, and Expedia — helping them turn web data into growth.
Watch how businesses like yours are using Actowiz data to drive growth.
From Zomato to Expedia — see why global leaders trust us with their data.
Backed by automation, data volume, and enterprise-grade scale — we help businesses from startups to Fortune 500s extract competitive insights across the USA, UK, UAE, and beyond.
We partner with agencies, system integrators, and technology platforms to deliver end-to-end solutions across the retail and digital shelf ecosystem.
Use this web data RFP checklist to define scope, sources, fields, SLA, QA and pricing models, then score vendors fairly.
Sensitive Skin Skincare Product Data API helps brands track cleansers, serums, moisturizers, and sunscreens with structured product data.
German marketplace data report 2026: online retail size, Amazon.de, Otto, Kaufland and Zalando price signals, PAngV rules and peak seasons.
Whether you're a startup or a Fortune 500 — we have the right plan for your data needs.