About the Client

Our client is one of the largest independent tire and automotive-service retailers in the United States, operating a dense store network across multiple states. Tire retail is a price-led category: shoppers compare the exact same SKU (brand, model, size) across retailers before booking an installation. For the client's Revenue, Pricing & Margin team, knowing a key competitor's shelf price in each market — every week, at SKU level — is a direct input into pricing decisions worth millions of dollars annually.

The Challenge

The client already had a data vendor supplying weekly price scrapes from its primary national competitor. But the relationship had three growing problems:

Data gaps after site changes. Whenever the competitor's website shipped an update, feeds broke — and stayed broken for days. In a category where prices move weekly, a missed cycle meant pricing decisions made on stale data.
Match-quality doubts. Tire SKUs are unforgiving: the same tire model exists in dozens of size/load/speed-rating variants. Mismatched variants were producing false "price gaps" that the pricing team wasted hours investigating.
Cost vs. coverage. The team wanted to expand from ~15 markets toward full network coverage — but not at the incumbent's price curve.

The mandate to Actowiz was simple: match or beat the incumbent on accuracy, guarantee the refresh, and make expansion affordable — with zero disruption to the downstream pricing models already consuming the feed.

The Actowiz Solution

1. Market-Accurate Store Simulation

Tire prices vary by location. Each weekly crawl anchors to the specific competitor stores serving the client's 15 priority markets, so every price point reflects what a shopper in that market actually sees — including market-specific promotions and rebates.

2. Variant-Exact SKU Matching

We built a tire-specific matching layer keyed on brand + model + full size code (e.g., 225/65R17) + load index + speed rating, validated against the client's own catalog. Ambiguous matches are flagged for review instead of silently guessed — eliminating the false price-gap noise that plagued the previous feed.

3. Self-Healing Collection with a Recovery SLA

The competitor's site is actively maintained and changes frequently. Actowiz's agentic, LLM-assisted extraction detects layout changes and re-maps fields automatically; where human review is needed, our contractual SLA guarantees feed recovery within 24 hours. The client has not missed a weekly cycle since go-live.

4. Zero-Friction Migration

We replicated the incumbent vendor's exact file schema, column order and naming — so the client's existing pricing models, dashboards and Excel workflows consumed the new feed on day one with no re-engineering.

Data Fields Delivered

Field Group	Fields
Product Identity	Brand, model, size code, load index, speed rating, competitor SKU/part number
Pricing	Shelf price, promotional price, rebate/offer text, installation package price
Availability	In stock / out of stock, ship-to-store vs. same-day flags
Context	Market/store identifier, capture timestamp, product URL

The Results

100% weekly refresh reliability since go-live — no missed cycles, including through multiple competitor site updates
98.4% variant-exact SKU match accuracy, audited against the client's catalog — false price-gap investigations reduced to near zero
~35% lower annual cost than the incumbent quote at equivalent coverage — freeing budget for market expansion
15 → 40+ markets on the expansion roadmap now economically viable under the new pricing model

Why It Worked

Category expertise matters. Tire data punishes lazy matching. A variant-aware matching layer was the single biggest accuracy driver.
SLAs beat promises. A contractual recovery window turned data reliability from a hope into a guarantee.
Meet clients where they are. Schema-identical delivery meant zero switching cost — the most underrated feature of any vendor migration.

Evaluating a Switch from Your Current Data Vendor?

Send us one week of your current feed's spec. We'll run a free parallel pilot — same SKUs, same markets — so you can audit our accuracy against your incumbent before you decide.

Request a Free Parallel Pilot

Frequently Asked Questions

Can you track more than one competitor?

Yes. Programmes commonly cover 2–6 competitors per market — national chains, regional players and marketplaces like Amazon, Walmart and Tire Rack — in a single normalized feed.

Do you cover other automotive categories?

Yes — parts, accessories, batteries, lubricants and service-package pricing, plus marketplace data from platforms like Shopee/Lazada for international tyre programmes.

What refresh frequencies are available?

Weekly is standard for tire retail; daily and intra-day refreshes are available for promo-heavy periods or dynamic pricing programmes.

Is competitor price scraping compliant?

We collect only publicly available pricing and product information — the same data any shopper sees — under a compliance framework aligned with applicable US regulations and responsible-scraping standards.

Hear It Directly from Our Clients

Watch how businesses like yours are using Actowiz data to drive growth.

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1 min

★★★★★

"Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing!"

Thomas Galido

Co-Founder / Head of Product at Upright Data Inc.

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2 min

★★★★★

"Actowiz delivered impeccable results for our company. Their team ensured data accuracy and on-time delivery. The competitive intelligence completely transformed our pricing strategy."

Iulen Ibanez

CEO / Datacy.es

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1:30

★★★★★

"What impressed me most was the speed — we went from requirement to production data in under 48 hours. The API integration was seamless and the support team is always responsive."

Febbin Chacko

-Fin, Small Business Owner