A grocery price-comparison / meal-planning platform needing ZIP- and store-specific pricing, availability, and product data across 30+ US chains — regular and weekly promo prices — via a fresh, commercial-use API.

Use Case

Grocery Price Comparison / Meal-Planning

Region

United States

Coverage

30+ national & regional chains

30+

US Grocery Chains

ZIP / Store

Level Pricing

Regular + Promo

Circular Pricing

API / JSON

Commercial-Use Feed

Contact Sales

Key Takeaways

Actowiz delivered ZIP- and store-level grocery pricing across 30+ major US chains — including Kroger, Albertsons, Safeway, Publix, H-E-B, Meijer, Aldi, Lidl, Costco, Sam's Club, Target, Walmart, Whole Foods, Wegmans and more — covering both current shelf prices and weekly promotional/circular prices. For any ZIP code, the API resolves nearby stores and returns store-specific product prices, availability, and product URLs, refreshed on a defined cadence (daily or weekly). Delivered as a commercial-use API/JSON feed with store details and provenance, it gave price-comparison and meal-planning platforms a reliable data backbone that scales from a top-10-chain pilot to 50+ chains.

What did the client need?

Multiple clients — grocery price-comparison startups, a meal-planning app, and a City-supported affordability initiative — needed store- or ZIP-specific pricing across major US grocery chains, with fresh data, store details, product information, pricing, stock status, and product URLs.

Requirements spanned current shelf prices and weekly promotional (circular) prices, a common set of ingredients or full assortment, ZIP-to-store resolution, commercial-use rights, and a defined refresh frequency — delivered as an API or structured feed, with a path to scale the chain list over time.

What made this hard?

ZIP-to-store resolution. Prices are store-specific, so each ZIP had to resolve to the correct nearby stores before pricing.
30+ chain coverage. Many national and regional chains, each structured differently, had to feed one normalized schema.
Regular vs promo prices. Both everyday shelf prices and weekly circular/promo prices had to be captured and labelled distinctly.
Freshness at scale. Daily or weekly refresh across many stores and SKUs required a robust, maintained pipeline.
Commercial-use delivery. Data had to be delivered API-ready with commercial-use rights and store-level provenance.

How did Actowiz solve it?

Actowiz built a ZIP-aware, multi-chain grocery pricing pipeline delivering normalized store-level data via a commercial-use API, with a pilot-to-scale path.

Approach

ZIP-to-store resolution. Each ZIP resolves to nearby stores; pricing captured per store.
30+ chain pipelines. National and regional chains normalized into one schema.
Regular + promo capture. Everyday shelf prices and weekly circular/promo prices captured and labelled.
Defined refresh. Daily or weekly refresh per the client's use case.
Pilot-to-scale. A top-10-chain pilot (with free sample) validated coverage before scaling to 50+ chains.
Commercial API delivery. Normalized API/JSON feed with store details, product URLs, and provenance.

Data Attributes / Fields

Field	Description
zip / store	ZIP and resolved store
retailer	Chain / banner
product_name / brand	Product and brand
size / unit_price	Package size and unit price
regular_price	Everyday shelf price
promo_price	Weekly circular / promotional price, when applicable
availability	In-stock / out-of-stock
product_url / updated_at	Product link and last-updated timestamp

What were the results?

Store-level accuracy. Prices matched the shopper's actual nearby store, not a national average.
Regular + promo view. Everyday and weekly-deal prices both available and distinct.
Fresh, reliable feed. Defined-cadence refresh kept the comparison current.
Pilot-validated, scalable. A pilot proved coverage before scaling the chain list.

Project at a Glance

Metric	Value
Use Case	Grocery price comparison / meal-planning
Region	United States
Chains	30+ national & regional (Kroger, Albertsons, Publix, H-E-B, Aldi, Costco, Target, Walmart, …)
Granularity	ZIP → store → product
Prices	Regular shelf + weekly promo/circular
Refresh	Daily or weekly
Delivery	Commercial-use API / JSON feed

Client Feedback

“We needed real store-level prices tied to a shopper's ZIP — regular and weekly deals — not national averages. Actowiz gave us a clean API across dozens of chains, and a pilot to prove coverage before we scaled.”

— Founder, Grocery Price-Comparison Platform

Frequently Asked Questions

Q: Which retailers are covered?

A: 30+ US chains including Kroger, Albertsons, Safeway, Publix, H-E-B, Meijer, Aldi, Lidl, Costco, Sam's Club, Target, Walmart, Whole Foods, Wegmans and more.

Q: Is pricing store- or ZIP-specific?

A: Yes — each ZIP resolves to nearby stores and pricing is captured per store.

Q: Do you cover weekly promotional/circular prices?

A: Yes — both everyday shelf prices and weekly circular/promo prices are captured and labelled distinctly.

Q: How fresh is the data?

A: Refresh is on a defined cadence — daily or weekly — per the use case.

Q: Is there a sample/pilot?

A: Yes — a free sample and a top-chain pilot validate coverage before scaling to more chains, with commercial-use rights.

Need ZIP-level US grocery pricing data?

Actowiz Solutions delivers store-level, multi-chain grocery pricing via a commercial-use API with regular and promo prices. Visit actowizsolutions.com to discuss your data requirement.

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