RangeMe supplier brand directory data: the two-sided CPG shelf, delivered

It is the audition tape for the American grocery shelf, plus its counterparts in six other markets. RangeMe - part of ECRM, and the largest business-to-business product discovery platform in the industry - is where retail and foodservice buyers search for what to stock next and where suppliers maintain structured digital sell sheets instead of emailing PDFs into the void. The company reports more than 200,000 CPG supplier brands facing over 15,000 retail buyer accounts.

The buy side is the part worth reading slowly: Walmart, Publix and Whole Foods Market on the grocery side, CVS in pharmacy, Petco in pet, Holland & Barrett in health, Do It Best in hardware. Two-sided structures like that are rare as delivered data - most CPG sources give you one side and make you guess at the other.

Rows arrive as entity records keyed to the platform's own identifiers, and the shape demonstrates the join logic of the whole dataset:

brand_name       : Mush Foods
title            : RangeMe – B2B Food & Grocery Product Discovery
record_type      : supplier_brand_profile
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retailer_name    : Whole Foods Market
record_type      : success_story / buyer_account_entity
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brand_name       : Barrelhouse Brewing
order_availability: accepting orders

A retailer_name anchors the buy side, a brand_name anchors the supply side, and every commercial record in between - product pages, pricing posture, sourcing calls - hangs off one of those two keys. These three come straight from the platform's public success-story layer; at full scale the same two-sided structure covers 200,000-plus brands and 15,000-plus buyer accounts rather than three illustrative rows. Get a sample and the rows land in exactly this schema, cut to whichever categories, regions or retailer types you name.

Nine fields define the confirmed core, and they stack into three jobs. Commercial identity: brand_name, product_name, certifications. Price and physical posture: msrp, price_margin, packaging_dimensions, order_availability - the "accepting orders" flag is live commercial state, not a category label. Demand side: buyer_opportunity_title and retailer_name, the pair behind every Immediate Opportunity posting where a buyer names what they are actively sourcing right now.

Honesty note, because it changes how you should treat the deeper schema: these definitions were assembled from the platform's own supplier-facing descriptions rather than observed page markup, so the field-definition confidence is marked inferred. The attributes past the confirmed core - keyword tags, brand-story copy, graphics assets, Verified-status flags, the internal category taxonomy - confirm with your sample instead of appearing here speculatively.

Two mechanics give the certification field teeth. Products must be uploaded and approved before searching buyers can see them, and RangeMe Verified status requires insurance documentation plus barcode documentation - operational weight, not marketing gloss.

Geography - seven markets: the United States, Australia, Canada, Europe, Mexico, New Zealand and the United Kingdom. The visible deal flow concentrates on North American grocery and drug chains, but the footprint reaches British high-street health retail and trans-Tasman grocery, so cross-market assortment comparisons do not stop at one coastline.

Temporal - current platform state. This is a living directory, not an archive: records describe what is on the platform now, and there is no published backfile of past listings or closed sourcing calls. Trend work runs forward from your first delivery rather than backward through history.

Granularity - one record per brand and per product inside supplier profiles, with buyer-side activity captured at the level of individual sourcing calls. That is the grain assortment planners actually need: not "the granola category grew" but "this brand lists this SKU at this price, accepting orders, today."

API, files, or your warehouse. Daily, weekly, or hourly.

That last point is the difference between a feed and a folder of screenshots. A brand's price margin means something different next to an open sourcing call from a national grocer than beside silence - and that join only works if both sides arrive as typed keys rather than prose.

• Assortment and category planning - see which challenger brands are positioning into your categories, at what price points and margins, before they reach a shelf review near you.
• CPG prospecting and competitive intelligence - sales teams build brand lists from the 200,000-plus profiles and watch Immediate Opportunities to learn what retailers are actively hunting; rivals' product counts, pricing posture and certification claims read as a live scoreboard.
• Distributor and broker territory lists - brands by category and region, prioritised by order availability and Verified status.
• Retail trend research and investment screening - new-brand listing volume per category functions as a leading indicator of where grocery innovation heads, months before scanner data confirms it, and sizes an emerging-brand pipeline without waiting for a target to publish a deck.
• Model features for a CPG product graph - a two-sided entity graph of brands, products, retailers and sourcing events is rare raw material for assortment and launch models.

The fuller workflow lives on the sales growth teams use cases and e-commerce operators use cases pages.

Nothing replicates buyer intent - stated demand from the buyer's own keyboard is the directory's differentiator - but each surrounding layer has a delivered counterpart, and the honest way to use all of them is as join keys rather than substitutes:

The trade-off is naming. The statistical layers aggregate; they never tell you which challenger salsa brand is pitching which buyer this week. That is exactly the row the directory supplies.

Because the interesting asset is famously awkward to work with. Profiles arrive as sell sheets built for humans - story copy, graphics, keyword soup - when analysis needs typed keys; pricing sits beside marketing language; the demand side lives in a separate surface from the supply side. Datadory ships the rows already shaped: brand and retailer entities typed separately, price and availability normalised, sourcing calls attached to their categories, definitions documented down to which ones are confirmed versus inferred.

Files, feeds, or straight into your warehouse. Daily, weekly, or hourly - your call. The sample comes first either way: name the categories, regions and retailer types, get real rows in the schema above, then decide.

Start with the anchor record, RangeMe – B2B Food & Grocery Product Discovery, sampled to your categories before anything is committed - its dataset page holds the full field dictionary, coverage chips and sample rows. Pair it with the IFDA Research Library for channel economics, then extend into the open product layers as enrichment rather than substitute.

This page is one thread of a wider map. The best food distributors datasets ranking places the anchor record against its neighbors, the environmental-grade detail on Open Food Facts bulk exports covers the enrichment layer, and the food distributors data hub indexes every record with its coverage statement.