--- title: "Auto parts and tire data: pricing, fitment, product matching, and market coverage" slug: auto-parts-and-tire-data date: 2026-10-05T10:19:33+00:00 modified: 2026-10-05T11:35:19+00:00 permalink: https://brightdata.com/blog/web-data/auto-parts-and-tire-data type: blog --- [ Blog ](https://brightdata.com/blog "Blog") / [Web Data](https://brightdata.com/blog/web-data) [Web Data](https://brightdata.com/blog/web-data) # Auto parts and tire data: pricing, fitment, product matching, and market coverage Why one part shows different prices across retailers, how to match listings by part number, and how Bright Data delivers auto parts and tire data. 16 min read [ ](https://brightdata.com/blog/authors/satyam-tripathi) [Satyam Tripathi Technical Writer ](https://brightdata.com/blog/authors/satyam-tripathi) ![Auto parts and tire data](https://media.brightdata.com/2026/10/Auto-parts-and-tire-data.png) Auto parts and tire data is the pricing, listing, fitment, and identifier information in a parts catalog. On several marketplaces it also includes product descriptions, images, and reviews. It’s hard to use because those fields vary across retailers and marketplaces. The same number can show different prices, and two listings that look alike can be different parts, since identifying a single part can take several attributes: brand, model, year, side, and color. So if you price against a competitor on the wrong assumption, you lose the margin. Ecommerce is [only 7% of US auto and parts sales](https://www.shopify.com/enterprise/blog/selling-auto-parts-online), against 15.4% of retail overall, so the online share has room to grow. Bright Data offers four ways to get this data: pre-collected datasets, scraper APIs, Scraper Studio, and Bright Insights as a managed data feed. The right one depends on how much of the workflow you want to run yourself. ## TL;DR Auto parts data, also called car parts data, comes from retail sites and marketplaces, not from connected-car telematics or autonomous-vehicle systems. - You collect the competitive data. Brand and part number do most of the matching, and licensed fitment vocabulary can help as complexity grows. - Sellers put cross-reference numbers in titles, so a part-number match returns listings that only mention the number. - Four retailers listed one Moog part, and the highest price was 2.3 times the lowest.\* No licensed catalog includes competitor pricing, and the [ACES and PIES reference databases](https://www.autocare.org/data-standards/subscriptions) start at $2,500 for members. - Retailer sites often use anti-bot protections. Data collection needs unblocking, retries, and monitoring, not a one-time script. - Tires need a separate schema. Size, load markings, and OE codes don’t fit a shared parts model. *\*Prices, listings, and search results were checked in September 2026.* ## Why the same part looks different on every site FRAM PH3593A is a current spin-on oil filter. When you search Amazon for that exact number, the top result is an oil filter for a Cummins Onan RV generator. *The number in the query appears in the title of a filter for a different engine. That’s the matching problem in one screenshot.* The first result is not an ad. Sellers put cross-reference numbers in titles because buyers search by whatever number they have. Nobody is lying, because no single brand owns that number. ### Four retailers list the same part at four prices Moog K80261 is a front sway bar link for the 2000s Lincoln LS and Ford Thunderbird. Four retailers list it at these prices: **Retailer****Listed as****Price**WalmartMOOG K80261$17.65PartsGeekMoog K80261$31.97CARiDMOOG K80261$33.16, plus a $1.70 couponSummit RacingMOG-K80261$39.99 All four retailers listed the same part on one day, and the highest price was 2.3 times the lowest. Summit Racing writes the number as MOG-K80261, and CARiD shows the discount as a coupon rather than inside the price. Both details make a direct comparison wrong. A universal identifier doesn’t fix this either, because retailer pages rarely show a GTIN (Global Trade Item Number). ## What to match on Brand and manufacturer part number (MPN) do most of the matching. For price monitoring on known SKUs, where you already know exactly which part you are tracking, they are often enough. But when you are matching across retailers or the fitment is complex, a single part can take several attributes to pin down: brand, model, part, year, side, and color. A side mirror can differ by model year, by side of the car, and by color, so brand and part number alone will merge products that are not the same. The anonymized examples that follow come from real auto parts data teams. **An aftermarket brake parts manufacturer** provided its full catalog for competitive matching across Amazon and Walmart. One of its sub-brands uses part number prefixes on those marketplaces that do not appear in the internal catalog. Matching on the internal part number alone returned zero results for hundreds of SKUs that competitors did list. The fix was to collect the marketplace-specific identifier separately and map it back to the internal SKU. **A brake parts manufacturer’s analytics team** tracks three identifiers for every product. The internal MPN is the primary key. The retailer’s part number or ASIN comes from the listing, and a technical SKU may come from the URL. Treating them as interchangeable caused reporting errors and broken joins, and keeping them in separate fields from the start resolves most later reconciliation issues. **A performance parts retailer** simplified its matching after a complex multi-field approach produced fewer results than a plain brand plus part number match. The team preferred a broader match with manual review over a high-confidence match that returned too few records to be useful. Match quality and match coverage are a tradeoff, and the right balance depends on how the data is used later. Across these teams, matching starts with brand and part number. Marketplace identifiers are stored beside them, and a broad match is reviewed before a strict one. Terminology, position, and vehicle application come from the Auto Care Association’s [ACES 5.0 and PIES 8.0](https://www.autocare.org/news/latest-news/details/2026/04/02/auto-care-association-releases-aces--5.0-and-pies--8.0), through the PCdb, VCdb, and Qdb reference databases. ACES is the standard for fitment, meaning which part fits which vehicle, and PIES for product attributes. With that vocabulary, you map both “Stabilizer bar link” and “Sway bar link” to a single PCdb term. The [reference databases](https://www.autocare.org/data-standards) need a paid 12-month subscription, and access takes several days after you order. When fitment matters, license the vocabulary, because rebuilding it means maintaining your own terminology tables as vehicles and part types change. ## What a licensed automotive parts database can’t tell you A licensed automotive parts database contains what a manufacturer publishes about its own products. Your pricing team needs data that no ACES or PIES file includes. - **Competitor pricing.** No licensed catalog has it, and one price column is not enough. - **Assortment and availability.** A competitor’s assortment and availability can change often. - **Third-party sellers.** More than 60% of worldwide paid units at Amazon have come from third-party sellers for several years, according to its own [reported metrics](https://www.sec.gov/Archives/edgar/data/1018724/000101872426000024/amzn-20260630xex991.htm). A brand usually has no contract with that inventory. - **Advertised price compliance.** Teams can collect public advertised pricing signals and apply their own [MAP policy](/blog/how-tos/what-is-map-monitoring) rules, or use [Bright Insights MAP monitoring](/products/insights/map-monitoring) where it is part of their plan. - **Location-level pricing.** Prices differ by store and by country, and a licensed catalog has neither. **A major auto parts retailer’s pricing team** found that its data feed was collecting promotional prices rather than everyday low prices (EDLP). Its pricing models were built on EDLP, so every decision from the feed was based on a price the competitor only offered temporarily. The fix was to store everyday low price, promotional price, discount, coupon, core charge, and freight in separate fields. **A major auto parts retailer** needed one competitor’s pricing from five specific store locations in the US and five in Mexico, plus one in Canada. A national average or a single-location sample would have invalidated its price-match policy, which applies at the local level. Location and country belong in the primary key on every price record, not in a filter applied after collection. All of this data is collected, not licensed. Bright Data can collect public pricing fields, promotional signals, seller details, product attributes, ratings and reviews, and availability-related page signals across supported sources. Data update cadence can be quarterly, monthly, weekly, daily, or intraday, depending on the use case, source, and project scope. Collect it yourself with scraper APIs or Scraper Studio, take it pre-collected as a dataset, or have Bright Insights run it as a managed feed. *The retailer’s SKU, the UPC, and the manufacturer part number are separate fields in one row, next to price, stock, and rating signals.* ## What collecting auto parts data requires Auto parts retailers and marketplaces often have anti-bot protections, dynamic pages, and inconsistent markup. A reliable collection layer needs unblocking, retries, monitoring, and source-specific handling. That work grows with every retailer and marketplace that you add. Not every site allows collection, so check robots.txt and the site’s terms before you build a target list. **A performance parts retailer** relied on one competitor’s site for daily pricing signals. When that site added new anti-bot protections, collection stopped for two weeks. The team had no prices for one of its most important benchmarks, with a direct impact on marketing and pricing decisions. Collection is continuous maintenance, not a one-time build, and the work is the same whoever does it. Most of it is engineering time, which is why some teams buy a managed feed and get the data with no engineering lift. The managed feed also includes a formal SLA, proactive monitoring and maintenance, and a client success team, which together resolve blocking faster than a self-service pipeline. The [scraper API free tier](/pricing/web-scraper) gives you 5,000 requests a month, with no credit card required. That is usually enough to try your own retailer list first. ## Market coverage: knowing what a competitor doesn’t list You cannot check share of shelf by hand across a full catalog, so you have to trust the coverage report instead. A missing SKU has 3 common causes, and each one needs a different fix. **What you see****What it can mean****What to do**No listing foundThe competitor doesn’t list itCoverage gap, record as not listedNo listing foundThe page was never retrievedCollection failure, retryNo listing foundListed under a number that you didn’t matchMatching failure, re-query by attribute **Multiple auto parts teams** have had this problem. Both a brake parts brand and a performance parts retailer had to audit their pipelines to separate the 3 causes. Only then did their assortment reports become reliable inputs for merchandising decisions. To separate them, the collection layer has to log what happened. When a retailer returns “Sorry, we found 0 results” inside its own markup, log it as a real gap rather than a block. On a self-service pipeline your team builds that separation, and Bright Insights can include it in the assortment coverage report. *The coverage side of the same product within the Sales and Market Share dashboard: active selling SKUs, in-stock rate, and share, measured against the previous period.* ## Tires need their own schema Tires don’t fit a parts schema. Size is one code built from several values, and the XL and HL load markings appear in different positions on the same string. OE codes such as MO or N0 make identical-looking sizes different products. A feed that merges them can create price gaps that don’t exist. Once the schema is separate, the pricing, matching, and coverage work above applies to tires as well. Bright Data collects tire data from UTires and giga-tires.com, among other tire retailers and marketplaces. See how [1001Pneus uses Bright Data for competitive tire pricing](/customer-stories/e-commerce/1001pneus). ## Build versus buy for auto parts data The decision involves two inputs, collected data and any licensed vocabulary, plus everything that happens after they meet. *Collection and any licensed fitment data meet at matching, and every later step depends on it.* Data providers offer the same kinds of data in every retail category. The Bright Data [beauty and cosmetics comparison](/blog/web-data/best-beauty-cosmetics-data-providers) sorts them into 6 layers. Above catalog and pricing come variant availability, estimated sales and share, reviews and shelf visibility, and trend. In that comparison, most providers supply only the first 2, and Bright Insights can deliver the upper layers as a managed feed. Ask each vendor which layers above catalog and pricing it supplies. For speed, cost, and accuracy, an [independent benchmark from AIMultiple](https://aimultiple.com/ecommerce-scraper) ran 1,700 URLs across 10 scraper APIs and measured all three separately. Three of these cost lines have no published price. ### Cost and pricing considerations **Cost line****Published cost**[Reference databases](https://www.autocare.org/data-standards/subscriptions), when fitment matters$2,500 entry package at member rates, under $1M revenue; up to $16,400 for full ACES and PIESCommercial parts catalogsNot published, quote onlySelf-collected competitive dataNo list price, varies with volumeManaged data collectionBright Insights from $2,000 per month, quoted; can include several sites depending on scopeEngineering to maintain collectionWhat the engineers cost you, including overhead Count listing pages when you estimate volume, because one page can show many prices. Then decide the delivery format. Bright Data can deliver bulk data through cloud storage, warehouse integrations, APIs, or file exports (S3, Snowflake, Databricks, SFTP, and more). An auto parts catalog API is for on-demand lookups, not bulk delivery. A Model Context Protocol endpoint, such as the [MCP server for AutoZone](/ai/mcp-server/autozone), works for AI agents. Confirm which of these a vendor supports before you commit. ### Which sources are already covered The first question is whether you need to collect at all. Bright Data publishes more than 40 [pre-collected datasets](/products/datasets) related to auto parts and tires, including [PartsGeek](/products/datasets/parts-geek), [CARiD](/products/datasets/carid), and [AutoZone](/products/datasets/autozone). Datasets and scraper APIs are available for dozens of parts sites, including Ford and Toyota OEM Parts Online, Summit Racing, and FCP Euro. There are marketplace datasets for eBay, Amazon, Walmart, and AliExpress, among others. Bright Insights adds European retailers such as atp-autoteile.de and gsfcarparts.com. ### What 3 auto parts datasets include Pre-collected datasets are a strong starting point for source-level product data. Comparing one part across retailers usually takes a matching layer, which your team builds or Bright Insights runs. Bright Data offers a [free 1,000-record sample](/products/datasets/free). Download one and check which fields your own SKU list needs. **Dataset****What its catalog puts first**PartsGeekVehicle, in the row and the category pathCARiDBrand, in the category pathAutoZonePart type, in the category path The category path shows this difference most clearly. *PartsGeek puts the vehicle in the category path, down to year, make, and model.* The fields differ by source, because each retailer publishes what its own catalog contains. The same manufacturers appear across all 3 datasets under different identifiers. Bright Insights can run that matching as part of a managed workflow and return the results. Do you want to run the collection yourself, or have it delivered? A [15-minute consultation](/contact) compares both for your own SKU list. ## Next steps You have 3 decisions: the vocabulary, the matching, and the collection. Licensed vocabulary is not always the starting point. For simpler use cases, such as price monitoring on known SKUs or direct retailer URLs, brand and manufacturer part number are often sufficient matching anchors. Licensed vocabulary from ACES and PIES becomes more important as matching complexity grows, particularly for fitment-heavy catalogs or cross-retailer comparison at scale. Start with what you have, and add vocabulary as your requirements expand. Cross-retailer matching is built rather than licensed, because PIES includes only the manufacturer’s own cross-references. Auto parts data is complex, so the right entry point for collection depends on how much of the workflow you want to run yourself. - [Pre-collected datasets](https://brightdata.com/products/datasets) for fast access to structured source-level data, with a free sample for each. - [Scraper APIs](/products/web-scraper) for teams that want control over collection, with unblocking included. - [Scraper Studio](/products/web-scraper/studio) for custom workflows on sources that nobody has pre-collected. - [Bright Insights](/products/insights) for teams that want collection, matching, normalization, QA, and delivery handled, with dashboards and an [AI Assistant](https://brightdata.com/products/insights/ai-assistant-playground) to query the data in plain language. The first three options are best for teams that want to control collection and matching themselves and have resources to maintain it. Bright Insights is better when teams want matching, normalization, QA, delivery, dashboards, and AI-assisted analysis handled as a managed workflow. Three steps come before the first run, whether you collect the data yourself or a vendor does. ### If you collect it yourself 1. Define a successful request as one that returns a product element, not just a 200 OK status or a page title. 2. Store the raw response beside the parsed record, and keep MPN, retailer part number or ASIN, and any technical SKU in separate fields. 3. Record a timestamp and source URL on every price. ### If a vendor collects it for you 1. Name the 20 SKUs and the competitor domains that you want tracked first. 2. Set the data update cadence, from quarterly to intraday depending on scope. 3. Name who uses the data, through a dashboard or directly, and which decision it feeds. To apply these steps to your own catalog, [book a 15-minute Bright Insights demo](https://brightdata.com/products/insights). Bring 20 SKUs and the competitor domains that you care about. ## FAQ ### Is there an auto parts catalog API? For retailer catalogs, yes. Bright Data runs auto parts catalog APIs for AutoZone, Advance Auto Parts, CARiD, and Ford and Toyota OEM Parts Online, among others. For ACES and PIES fitment, no, because that vocabulary is licensed as reference databases rather than offered as an API. ### Why do part number searches return wrong parts? Sellers put cross-reference and supersession numbers in listing titles because buyers search by any number that they have. So a partial match returns listings that only mention the number. Brand and part number narrow the results, and terminology, position, and vehicle application matter for fitment-heavy catalogs. ### Can I download a car parts database as a CSV? Yes. Bright Data’s pre-collected auto parts datasets export as a CSV, JSON, or Parquet, among other formats, and each includes a free data sample. A CSV is a snapshot from the dataset’s last refresh, so for near-live pricing you either set a refresh schedule or use a scraper API that returns data on demand. Note that a licensed catalog export contains manufacturer data only, not competitor pricing. ### What does a superseded part number mean? It means the manufacturer replaced that number with a newer one. A part can replace an older number and be replaced by a newer one at the same time. If you watch only one of the two numbers, you can follow the wrong part while the page still takes orders. ### What do I get if Bright Data handles the collection? Bright Insights can run collection, matching, normalization, QA, and delivery, then return the result in a dashboard or send it directly into your data stack. It starts at $2,000 per month and can track several sites, depending on scope. An [AI Assistant](https://brightdata.com/products/insights/ai-assistant-playground) answers questions in plain language. Bright Insights customers see a 3% to 5% margin uplift. The managed feed also includes a formal SLA, proactive monitoring and maintenance, and a client success team, which together resolve blocking faster than a self-service pipeline. ### Do I need a data or engineering team to use this? Not for the managed option. Bright Insights can handle collection, matching, and the quality checks, and deliver the result to a dashboard and to the AI Assistant. For the self-service options you control the matching, so a data team helps. ### Is there a tire database? Several vendors sell one, but tire data needs its own schema. Size is one code built from several values, and the XL and HL markings appear in different positions. Some tires are built to a specific automaker’s spec, marked by OE codes such as MO or N0, so identical-looking sizes can be different products. A feed that merges those can create price gaps that don’t exist. ### Do I need to license ACES and PIES? Not always. Brand and manufacturer part number are enough for most price monitoring on known SKUs. ACES and PIES, from [$2,500 at member rates](https://www.autocare.org/data-standards/subscriptions), matter more for fitment-heavy catalogs and cross-retailer comparison at scale. The [NHTSA vPIC API](https://vpic.nhtsa.dot.gov/api/) is free, and it lists vehicles but no fitment. Contact usStart for free No credit card required [ ](https://www.linkedin.com/in/triposat/) Satyam Tripathi Technical Writer 5 years experience Satyam Tripathi helps SaaS and data startups turn complex tech into actionable content, boosting developer adoption and user understanding. Expertise Python Developer Education Technical Writing [ View all articles ](https://brightdata.com/blog/authors/satyam-tripathi) Table of Contents Dedicated Scraper APIs & No-Code Scrapers Over 1000 scrapers for all popular domains. Simplify your web scraping. [See pricing](/pricing/web-scraper "See pricing") Just want data? Skip scraping. 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