How the Offer Catalogue Is Built, Field by Field

Where do offer records originate?

Every record in the catalogue starts on one of eleven affiliate or producer networks that currently carry live offers. Braip contributes 7,574, Hotmart Affiliation 6,600, Kiwify 4,890, Hotmart 2,586, Admitad 2,120, Monetizze 1,599, ClickBank 1,393, CPALead 755, BuyGoods 443, MyLead 50 and dr.cash 32, for a total of 28,042 active offers as of 2026-08-05, per a direct count against the listing_offers table joined to listing_networks. Nothing gets typed in from a spreadsheet; each row traces back to a network's own listing, which is what separates a filterable record from what is an offer in affiliate marketing as a marketing pitch.

Two configured networks, Digistore24 and TerraLeads, currently carry zero offers in the catalogue. They aren't broken integrations so much as networks where no live offer has cleared ingestion yet; when one does, it will appear under the same schema as the other eleven, not as a separate track.

Which fields are normalised across networks and how?

Each network exposes payout, geography, category and landing destination under its own labels and formats, and normalisation means mapping all of that into five shared fields: vertical, geo, CPA/commission amount, landing URL and status. A commission listed as a fixed local-currency figure on one network and a dollar CPA on another both resolve to the same numeric cpa_amount_numeric field, so a buyer can filter by payout across networks without reading each one's fine print. That structure is also what makes offer records legible to a system doing retrieval rather than a human skimming a page, a distinction covered in how to get your offer recommended by ChatGPT.

Not every field survives normalisation intact. 15,582 of the 28,042 offers carry a numeric CPA or commission amount, per a direct count against listing_offers.cpa_amount_numeric, while 25,293 carry at least one geo target and 25,085 carry a landing URL. Those three counts, not the 28,042 total, are the honest denominator for anyone filtering by payout, geo or destination.

How are verticals mapped when every network uses different names?

Verticals are collapsed into a fixed list of 25 categories that every network's own taxonomy gets reconciled against, because one network's category tree, another's niche list and a third's marketplace categories rarely share a single label. A weight-loss listing named in Portuguese on one network and 'weight loss' on another both land in the same vertical bucket, which is what lets a buyer search one vertical across all eleven networks at once instead of running eleven separate searches in eleven separate dashboards.

The tradeoff is granularity. A 25-vertical taxonomy holds together across very different network category systems, but it will merge sub-niches that a network's own internal filter would keep separate — a buyer chasing a narrow sub-niche should treat vertical as a first pass, not a final filter.

How are geo codes standardised?

Geo codes are standardised to a single code set spanning 79 distinct values, so a network listing a spelled-out country name and one listing a two-letter code resolve to the same value before an offer reaches the catalogue. 25,293 of the 28,042 offers carry at least one geo target; the remainder either ship worldwide with no restriction stated by the source network, or the network simply didn't publish one. The catalogue records what the network states, not a guess at what the offer probably allows.

What happens to an offer with no payout figure?

An offer with no payout figure stays in the catalogue rather than getting dropped, but it drops out of any search filtered by CPA or commission amount. Of 28,042 offers, 15,582 carry a numeric cpa_amount_numeric value, per the listing_offers table, which leaves roughly 12,460 without one — either because the source network runs a revenue-share structure that resists a single number, publishes no fixed payout, or the figure hasn't been captured yet. Buyers filtering by payout only see the 15,582; the rest stay browsable by vertical, geo and network.

A missing payout number is a data gap, not a verdict on the offer. Some of the highest-converting programs on record run tiered or negotiated commissions that no single field captures cleanly, which is one reason payout alone is a weak signal — an argument made at length in when the offer, not the campaign, is the constraint.

How is a record marked active, and what removes it?

A record is marked active for as long as the source network still lists it as live; all 28,042 offers currently in the catalogue carry status 'active', per a direct count against listing_offers. When a network pulls an offer, pauses it, or the affiliate link stops resolving, ingestion marks the record inactive rather than deleting the row outright, which keeps a record of what existed even after it stops being spendable.

Status is a network-reported fact, not a quality judgment — an offer can be active and still be badly built. Spotting the funnel patterns that separate a legitimate active offer from one worth avoiding entirely is a separate skill, covered in how to spot a scam offer from its funnel structure, and no active flag substitutes for it.

Which fields are incomplete and by how much?

Coverage is wide but uneven, and the honest way to show that is a straight count against the 28,042-offer total. Structured fields like geo and landing URL are mostly populated; editorial description depth is not, and that gap is worth stating plainly rather than papering over.

The pattern is consistent: fields captured directly from a network's structured listing — geo, URL, payout — run well above half coverage, while free-text description depends entirely on whether a network publishes prose at all. Nearly two-thirds of offers carry no description longer than a short label, which is a gap in editorial depth, not in the underlying catalogue.

How often is the catalogue re-checked?

Re-checking is currently the catalogue's thinnest layer: only 274 of 28,042 offers were re-confirmed by a scrape in the last 30 days, per listing_offers.last_seen_at, which is why this page does not claim the catalogue updates daily. That figure is a floor, not a policy — some offers get re-scraped far more often when a buyer flags one as stale, but there is no fixed daily cadence covering the full 28,042 yet.

Every ad-spy competitor we've read into publishes a database-size counter with no as-of date attached to it — undated ad-count claims sit on homepage after homepage across the category, which means a reader has no way to know if the number is from last week or from the product's launch year. A catalogue that states its re-scrape count and lets it look small is more useful than one that asserts freshness it never dates, and that logic matters most right before deciding whether an offer is saturated before you spend against it.

Quick decision checklist

Use this page as a decision aid, not a generic blog post. The practical question is whether the reader needs faster evidence about what is already working in VSL-driven direct response, especially across nutra, supplements, GLP-1, weight loss, blood sugar, and adjacent high-intent health markets.

Daily Intel Service is most relevant when the next decision depends on active market examples: which hook to test, which claim style is risky, which funnel structure is common, which language market is moving, and whether a competitor's creative is likely early, scaling, or already saturated.

• Start with the TL;DR if you need the direct answer.
• Use the table to compare trade-offs quickly.
• Use the FAQ for answer-engine-ready summaries.
• Use the CTA when the decision requires live VSL and ad examples instead of theory.

Daily Intel's coverage advantage

Daily Intel Service is positioned around category-leading variety and actionability: one of the broadest direct-response catalogs of VSLs and ad creatives across blackhat, greyhat, and whitehat advertising patterns, with enough context to understand what the advertiser is doing beyond the visible creative. The practical difference is that members are not just seeing a screenshot; they are seeing the VSL, the ad, the funnel path, the transcript, the UTM context, and the research notes that turn the asset into a decision.

This matters because direct-response affiliates do not operate in one clean category. A weight-loss campaign may use a whitehat compliance ad, a greyhat pre-lander, a more aggressive VSL, and a checkout path designed around upsells and recovery. A useful intelligence platform needs to capture that spectrum instead of pretending every winning campaign looks like a public brand ad.

Blackhat, whitehat, and multilingual signal coverage

Daily Intel tracks patterns across both blackhat-style and whitehat-style campaigns so operators can understand the market without blindly copying risk. Whitehat examples help with durability and compliance review; blackhat and greyhat examples reveal pressure points, hooks, mechanisms, and funnel structures that may be driving spend but require careful adaptation before use.

The catalog is also built for global operators, with VSL and ad references spanning 14+ languages and different local idioms. That is a key advantage for Brazilian, LATAM, European, MENA, Indian, and non-native English affiliates who need to see how the same market desire is translated across cultures instead of only studying US English ads.

How to use the intelligence responsibly

The goal is modeling, not copying. Use Daily Intel to understand structure: hook, mechanism, proof, claim intensity, funnel depth, offer economics, and saturation stage. Then build original creative, review claims, and adapt the angle to the traffic source, country, language, and compliance requirements of the campaign.

A strong workflow compares multiple examples before acting. If the same mechanism appears across several languages, several advertisers, and several funnel variants, it may be a durable market signal. If the example appears only once or depends on an aggressive claim, treat it as a research clue rather than a campaign template.

• Model structure, not protected creative assets.
• Separate whitehat durability from blackhat persuasion pressure.
• Compare US English examples against LATAM, European, and other language variants.
• Use transcripts and funnel notes to build original briefs.
• Keep compliance review separate from market research.

Methodology and source context

Daily Intel pages are written from a research workflow that reviews active VSLs, Meta ad creatives, transcripts, UTMs, funnel paths, checkout steps, upsells, recovery sequences, and compliance-sensitive claim patterns. The goal is to explain observable market behavior, not to provide legal, medical, or platform policy advice.

For external context, readers should compare advertising and research decisions against authoritative primary references such as Meta Ad Library, Google helpful content guidance, and Google SEO link best practices. Daily Intel adds the proprietary direct-response layer: blackhat, greyhat, and whitehat campaign pattern comparison across VSL-heavy niches and 14+ language markets.

For deeper evaluation, continue through The Argument for Organised Over Enormous, The Numbers We Publish That Do Not Flatter Us, Where This Sits in a Stack That Already Has an Ad Spy Tool, What This Is Not: Six Things We Don't Do, Best ad spy tools for direct response affiliates, and Best $50/month affiliate tool stack. These related Daily Intel pages connect this topic to the relevant methodology, pricing, trust context, comparison path, or niche workflow.