Why AffiliatePicker exists
Search "best affiliate programs" and you'll find hundreds of near-identical listicles. Most share three problems: the data is unverified (often copied from other listicles, errors included), the ordering is commercial (programs that pay the author more rank higher), and the numbers that actually predict your income — payout thresholds, NET terms, validation windows, attribution models — are missing entirely.
Ask experienced affiliates how they choose programs and they'll tell you the headline commission rate is the least important field. They want the cookie window, the payout ops, the reversal behavior, the churn profile. That information exists — scattered across official program pages, network dashboards and forum threads — but nobody had assembled it into one verifiable, comparable, honestly-ranked resource.
So we built it.
What we do
- Verify: every field in the database is checked against the official program page and stamped with a verification month.
- Standardize: the same fields for every program, so a hosting bounty and a SaaS recurring deal are genuinely comparable.
- Score transparently: a public rubric (read it here) computes every 0–100 score at build time. No editorial thumb on the scale, no sponsored positions.
- Model honestly: where we estimate (like first-referral value), we state the assumptions and label it an estimate.
Who runs this
Elliot Marsh
Founder & Lead Analyst · Auckland, New Zealand
I'm Elliot — an affiliate marketer from New Zealand, at this for a bit over twelve years. In that time I've built and run a string of niche sites: some that quietly paid the mortgage for years, a couple I sold, and a few that failed outright. I've promoted programs through Amazon Associates, ShareASale, CJ, Awin, Impact and PartnerStack, plus more in-house programs than I can count.
The failures taught me more than the wins, and most of them traced back to the same mistake: picking the program before checking the terms. I've built six months of content on a program that cut its rates with a week's notice. I've sent thousands of clicks into a 24-hour cookie for a product people take three weeks to buy. I've waited out NET 60 validation windows only to watch commissions reverse, and I've chased an in-house program for a four-figure balance it simply never paid. Every one of those lessons is now a field in this database — the cookie windows, the payout thresholds, the NET terms, the network-versus-in-house risk — because they're the details that actually decided whether my sites made money, and nobody was publishing them straight.
AffiliatePicker is the tool I needed on day one and couldn't find: verified terms, dated checks, and a scoring rubric anyone can read and challenge. If a figure here is wrong, that's on me — tell me and I'll fix it with the source linked.
Elliot maintains the scoring rubric, performs the term verifications against official program pages, and signs off every published score — when a profile says "analyst-assigned subscore," this is the analyst. Corrections are handled personally, and the correction thread becomes part of that record's verification history. Accountability for a rating means a name attached to it.
Editorial policy
Rankings are produced by the scoring rubric, not by commercial relationships. Some outbound links may become affiliate links as the site grows — that's how we intend to keep the database free — but per our conflict-of-interest policy, monetization never touches scores, ordering or inclusion. Programs we could monetize and programs we can't sit in the same table, ranked by the same math.
Corrections
Affiliate terms change constantly. If you spot an outdated field, we want to know — point us to the official source and we'll correct the record and refresh its verification date. A database like this stays useful only if it stays honest about what it knows and when it knew it.
Where this is going
The current database covers 45+ flagship programs across 12 verticals — the programs people actually search for. The roadmap: broader coverage per vertical, historical terms tracking (so you can see when a program cuts its rates — looking at you, Amazon), and opt-in anonymized EPC data from real affiliates, published with sample sizes. The goal is simple: make this the reference layer for an industry that runs on unverifiable claims.