How SimpleTire Increased Market Coverage by 50% with DataWeave’s AI-Powered Pricing Intelligence
22nd Oct, 2026
By DataWeave Marketing
SimpleTire prices tens of thousands of tire SKUs against several major competitor and marketplace sites, and every one of those decisions depends on competitive data its teams trust. When crawler failures kept knocking SKUs out of its feed and internal teams began dismissing the numbers, SimpleTire went looking for a new provider. It chose DataWeave’s AI-powered Pricing Intelligence. Today, matched pricing data lands in SimpleTire’s Snowflake environment every day, covering about 50% more of the market at a lower cost per unit. Here’s the full story.
Executive Summary
- Problem: Achieving pricing parity across competitors and maintaining a strong competitive position while navigating challenging market conditions. SimpleTire was looking to address several challenges with its competitive intelligence data such as crawlers breaking constantly, leading to reduced trust in the data
- Solution: DataWeave AI-Powered Pricing Intelligence, tracking of tens of thousands of SKUs across several major competitor and marketplace sites
- Delivery: Snowflake Secure Data Sharing directly into SimpleTire’s Snowflake environment, refreshed daily.
Impact:
- Against a challenging tire-industry backdrop, SimpleTire has continued to outperform broader market trends. Expanded competitive coverage and reliable market intelligence from DataWeave have helped SimpleTire navigate changing market conditions and make more informed pricing decisions across a larger portion of its assortment.
- 100% uptime of competitive pricing data feed, resulting in increased trust in the pricing data and optimized downstream operations.
- Increased market coverage by about 50% at a lower cost per unit.
- Issue resolution running two to three times faster than before.
About SimpleTire
SimpleTire is one of the fastest growing companies in the tire industry. The company’s business model links tire manufacturers, tire distributors, national retailers, and installation partners across the country into one seamless e-commerce network, providing consumers access to an unparalleled inventory of tires from hundreds of brands and an all-in-one tire replacement experience. SimpleTire’s network stretches across the country with 10,000+ installers, 20,000+ installation partners, and 3,000+ independent supply points.
Founded in 2012 and headquartered in Philadelphia, Pennsylvania, the company has operated as part of Dealer Tire since 2018 and has grown more than threefold since. Inc. 5000 has named it the fastest-growing automotive brand in America.
The Challenge
SimpleTire’s pricing operation runs on reliable competitive data. They normalize sources and integrate the competitive pricing feed into their internal pricing workflows to help teams respond quickly to a dynamic market while balancing competitiveness, margin, and manufacturer requirements.
SimpleTire was looking to address several challenges with its competitive intelligence data.
- Crawler Errors: Retailer and marketplace sites change constantly, and each change can result in SKUs being removed from the feed.
- Reactive Model: Marketplace changes could result in crawler failures that required time to identify and resolve. SimpleTire was looking for a solution that could identify coverage issues more proactively, communicate them quickly, and adapt more rapidly to marketplace changes.
- Eroding internal trust: With no proof of what a price was at the moment of capture, internal teams could dismiss any inconvenient data point. Someone would open a flagged URL hours later, see a different price, and conclude the crawler was broken.
SimpleTire Evaluated Alternatives
SimpleTire initiated an RFQ and designed a selection process to identify a new provider. It sent a sample data set to five vendors, with identical instructions: match all SKUs, hit a deadline, return a CSV with relevant fields in a particular order.
DataWeave returned the completed set roughly a day ahead of the deadline, with the highest match rate and quality of any submission.
“You were all given the same instruction. You guys had the highest match rate, highest quality, and the fastest turnaround, and you submitted a day before the deadline we provided. It was kind of a no-brainer at that point.”
– James Saldaña, Head of Data & Analytics Infrastructure, SimpleTire
The DataWeave Solution
Price Parity and Market Competitiveness
DataWeave matched tens of thousands of SKUs across several major competitor and marketplace sites, to deliver competitive pricing insights via Snowflake Secure Data Sharing directly into SimpleTire’s Snowflake environment each day. The data simply lands where SimpleTire’s analysts and algorithms already work, demonstrating DataWeave’s ability to integrate natively with a modern enterprise data platform. Every record carries a screenshot taken when the crawler ran, including in-cart pricing.
SimpleTire normalizes and integrates DataWeave’s data feed into its proprietary pricing workflows, helping the business respond dynamically to market conditions while balancing competitiveness, margin, and manufacturer requirements.
SimpleTire stores the pricing data over time and trends it. Four functions inside the business draw on it across very different time horizons.
- Pricing: Sets and adjusts SKU-level prices; identifies unexpected pricing movement across channels
- Manufacturer relations: Monitors MAP compliance across the market and frames evidence for brand conversations
- Sales: Uses historical price trend to support commercial planning and marketplace strategy.
- Data: Integrates competitive intelligence into SimpleTire’s data platform and enables downstream pricing, analytics, and business workflows.
MAP compliance as a partnership function
Most tire brands set a minimum advertised price. The daily feed shows the manufacturer relations team where those prices are being respected across the market, which lets them record and show SimpleTire’s compliance record relative to the market. This evidence gives SimpleTire a stronger foundation for conversations with manufacturer partners.
“It truly is critical for SimpleTire to create those partnerships with manufacturers and other suppliers. In order to be good partners, we need to understand what the environment looks like and how we can prevent perpetuation of these violations.”
Why matching tires is harder than it looks
No universal part number exists across the industry. Manufacturers, suppliers, and retailers each anchor on a different field, whether UPC, SKU, or internal part number, and pad or trim leading zeros inconsistently. Near-identical model names denote genuinely different products: a tire line and the same line with a “Plus” suffix are separate SKUs carrying different prices. Marketplaces compound it by creating duplicate listings for the same tire, one on rollback and one not.
SimpleTire does this work itself on every inbound supplier feed, reconciling inventory declarations across 3,000+ sources.
How DataWeave gets to 99%+ match accuracy
DataWeave has built the industry’s most sophisticated AI-powered product matching engine. Models read every listing DataWeave collects and pull structure out of unstructured text: the size buried in a product title, the load index, the speed rating, the model name and whatever suffix is attached to it. Computer vision handles the listings where the text runs thin and the detail sits in the image. Each candidate pair then carries a confidence score for how closely a marketplace listing corresponds to a known SKU, and the great majority clear the threshold with no human involved.
Below that threshold sit the cases models find hard. Two listings look nearly identical, and one character in a model name points to a separate product with its own price floor. Those go to DataWeave’s category specialists.
The specialists are also how the AI gets better. Every ruling becomes labeled training data, the models learn the distinction that tripped them, and the same class of error resolves on its own the next time it appears.
“AI is increasingly embedded across SimpleTire’s data and development workflows. Our team uses it to accelerate development, build better data products, and surface new insights for the business. But AI is only as valuable as the data behind it, which makes reliable, high-quality market intelligence increasingly important to how we operate.”
Results and Impact
- 50% more coverage, better unit economics: Actively crawled SKUs rose by roughly 50%, materially expanding the portion of the assortment supported by competitive visibility while lowering effective cost per unit.
- Easier navigation of a challenging market: Broader and more reliable competitive intelligence has helped SimpleTire navigate challenging industry conditions, make faster, more informed decisions, and outpace broader market trends.
- Stronger manufacturer partnerships: Continuous automated MAP monitoring, giving manufacturer relations a defensible evidence base for brand conversations.
- 3x faster resolution of problems: SimpleTire has seen issue resolution times improve by roughly 2-3x with DataWeave, alongside more proactive identification and communication of coverage issues.
- 99%+ feed uptime: Reliable daily delivery directly into SimpleTire’s Snowflake environment through Snowflake Secure Data Sharing.
“You guys are a breath of fresh air in the competitive pricing space. It’s really hard to nitpick you on anything, because every time we interact with you, it’s just way better.”
– James Saldaña, Head of Data & Analytics Infrastructure, SimpleTire
Download the full SimpleTire case study. To learn more, visit www.dataweave.com or email us at contact@dataweave.com.
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