Weekly SKU-Level Competitor Tire Price Intelligence Across 15 US Markets

Industry: Automotive Retail (Tires & Service)

Region: United States — 15 metro markets

Competitors monitored: Discount Tire, Tire Rack, Walmart Auto Care, Costco Tire Center, regional chains

Services used: Automobile Data Scraping, Competitor Price Monitoring, Weekly Data Feeds

The Client

A leading US tire and automotive service retailer with 100+ stores, whose Revenue, Pricing & Margin (RPM) team sets localized pricing on thousands of tire SKUs. The team had relied on an incumbent vendor for weekly competitor price scraping but faced rising costs, slow turnaround on coverage changes, and gaps in fitment-level accuracy.

The Challenge

Tire pricing is uniquely difficult to benchmark:

1. Price is local and bundled. The same SKU is priced differently by market, and competitors fold installation, warranty, and disposal fees into checkout differently — making sticker-price comparison misleading.
2. Fitment complexity. Tires are sold against size/spec combinations (e.g., 225/65R17 102H), and competitors structure catalogs around vehicle fitment rather than flat SKU lists. Matching "the same tire" across retailers requires brand + model + spec + load/speed rating normalization.
3. SKU-level, market-level, weekly. The RPM team needed 4,000+ SKUs priced across 15 markets every week, on a fixed schedule the pricing cycle depends on.
4. Vendor switch with zero disruption. The team was mid-contract with an existing provider and could not tolerate a coverage gap or a schema change that broke downstream pricing models.

The Solution

Actowiz Solutions ran a structured migration and took over the weekly competitor pricing feed.

1. Parallel-run validation.

For the first 4 weeks, we delivered our feed alongside the incumbent's, matched to the client's existing schema field-for-field. The RPM team compared both against manual spot checks — our feed showed 99.2% price accuracy and caught 340 SKU-market combinations the legacy feed was silently missing.

2. Fitment-aware product matching.

We built a tire master catalog normalizing brand, model line, size, load index, and speed rating across all monitored competitors, so every comparison is true like-for-like — including flagging when a competitor stocks an equivalent-spec alternative rather than the identical model.

3. Market-true, fee-complete pricing.

Crawlers query each competitor with store/ZIP context per market and capture the full price stack: unit price, installation, fees, and promotional bundles (e.g., "buy 3 get 1") — normalized into a comparable out-the-door price per tire.

4. Fixed-schedule weekly delivery.

Every Monday by 6:00 AM ET, a complete feed lands in the client's S3 bucket in their existing schema, with a delta file highlighting week-over-week price moves beyond 2%. Mid-week refresh is triggered automatically when a competitor launches a major promotion.

5. Transparent annual pricing.

A flat annual contract covering all 15 markets, with pre-agreed per-market pricing for expansion — replacing the incumbent's opaque per-change billing.

The Results

• 22% lower annual cost than the incumbent vendor for broader coverage (15 markets plus two competitors the previous feed didn't include).
• 99%+ price accuracy sustained across 12 months of weekly deliveries, validated by the client's ongoing spot-check program.
• 340+ previously missing SKU-market data points recovered, closing blind spots in exactly the high-margin spec ranges where mispricing hurts most.
• Out-the-door price normalization changed decisions: the RPM team found it was perceived as 4–6% more expensive than a key competitor in three markets purely due to fee structure, not unit price — and restructured fees accordingly.
• Zero missed Monday deliveries in the first year, including through two competitor website redesigns (feeds restored within 30 hours, before the next scheduled delivery).
• Migration completed with no disruption to downstream pricing models — same schema, same cadence, better data.

"We switched vendors for cost and stayed for accuracy. The fee-normalized pricing alone paid for the contract." — Director of Revenue, Pricing & Margin, Client

Why It Worked

• Parallel-run de-risked the switch. Pricing teams can't gamble on data quality; four weeks of side-by-side proof made the decision easy.
• Fitment-level matching. In tires, naive title matching produces garbage; spec-normalized matching produces pricing decisions.
• Out-the-door pricing. Competitors compete on the checkout total, not the sticker — so that's what we benchmark.

FAQs

Which automotive retailers can Actowiz monitor?

Discount Tire, Tire Rack, Walmart, Costco, Pep Boys, Mavis, regional chains, and OEM/dealer sites — plus parts marketplaces and used-car platforms.

Can pricing be captured at local market level?

Yes — crawls run with store/ZIP context so prices, fees, and promotions reflect each metro market.

Can you match products across retailers with different catalog structures?

Yes — spec-normalized matching (brand, model, size, load/speed rating) ensures like-for-like comparison, with equivalent-spec alternatives flagged separately.

Can you take over from an existing data vendor?

Yes — we routinely run schema-matched parallel deliveries during migration so downstream models continue uninterrupted.