Methodology

How verdicts get built

Every recommendation is grounded in named sources, weighed against each other, and traceable back to the evidence. No black box, no gut feel, no paid placement.

For the list of sources we draw from, see the Sources we monitor page. This page is about what we do with them.

Pipelines

Three pipelines, three roles.

Three content pipelines, each pulling from a different mix of sources depending on the question.

01

Settings Checker

Per-setting verdicts. You ask whether to enable or disable a specific ad-platform setting (Performance Max, broad match, auto-applied recommendations, attribution windows). The engine triangulates the paid-media trade press, practitioner communities, and first-party tests submitted by the Shared Learnings community into a Recommended, Not Recommended, or Context-Dependent verdict with cited sources.

02

Insights

On-demand research. You ask a broader question, a topic, a tactic, a tradeoff. The engine returns a personalised answer tied to your account type, monthly spend, and active business context. Same triangulation as Settings Checker, wider scope and more personalisation.

03

Platform News

Daily ingestion. A daily refresh built primarily on the six official platform newsrooms, with trade publications filling in where platform coverage is sparse. Every article is classified by AI against a six-platform whitelist before insertion, so the feed reflects what the platforms themselves announced.

Evidence

What counts as evidence.

Not every source carries the same weight. The engine sorts evidence into four categories and treats each differently.

01

First-party tests from the community

Practitioner-submitted A/B tests with stated platform, setting, hypothesis, result direction, magnitude, and where available a verifying screenshot. The highest-trust class because they are dated, single-variable, and attached to a real account on a known platform. The submitter's own tests never influence their own verdict, your data informs the community, not yourself.

Highest weight when present
02

Platform docs and newsrooms

Authoritative on what the platform itself supports, deprecates, or rolls out. We treat these as the source of truth for platform behaviour ("does Performance Max support search exclusions?") but not for "is it worth using?", because platforms are not impartial about their own products.

Authoritative for platform behaviour
03

Paid-media trade press

Established publications with editorial standards (Search Engine Land, Search Engine Journal, PPC Hero, Wordstream, Optmyzr, AdExchanger, Digiday, Adweek, Social Media Today, Marketing Dive, and others). Strong for industry-wide context and large-N benchmark studies, weaker on "what should I do tomorrow" because the advice is generalised.

Strong for context, broad on specifics
04

Practitioner communities

Reddit subreddits where working practitioners discuss what is actually happening in their accounts right now (r/PPC, r/googleads, r/FacebookAds, r/PPCMarketing, and others). Weaker on rigour than first-party tests, stronger on currency than trade press. Useful for the lived reality of a platform change, and for catching shifts before they hit the trade press.

Currency over rigour

Exclusions

What we deliberately don't count.

What the engine filters out of the evidence stream, because including it would weaken every verdict.

Filtered out

Content that never reaches the synthesis layer.

  • Undated tips-and-tricks posts. If we can't establish when the advice was written, it can't be weighted against newer evidence.
  • Evergreen guides and listicles. Useful as background reading, not as primary evidence for a verdict on a specific setting.
  • Content-farm and SEO-spam pages, including AI-generated thin content. The classifier rejects on source rather than content alone.
  • Opinion-without-data pieces. A practitioner saying "I think X is bad" without test data is weighted at zero for the verdict.
  • Paid placements, sponsorships, and advertorials. We don't accept them and we filter detected ones out.
  • Posts older than the platform's relevance window. Ad platforms change rules quarterly; a 2023 post about bid strategies often describes a system that no longer exists.
  • The submitting user's own tests, when generating their own verdict. Self-influence would distort the signal back at them.

Synthesis

How a verdict reaches its category.

The synthesis layer weighs the evidence into one of three outcomes. The category is the answer; the cited sources are the receipt.

Outcome A
Recommended
The weighted evidence agrees that enabling this setting is the right default for most accounts in your context. First-party tests, trade press, and practitioner discussion all point the same way. The verdict still names every source so you can disagree with the underlying data if you have a reason to.
Outcome B
Context-Dependent
The evidence agrees that the answer depends on something specific (account type, spend bracket, vertical, attribution model, campaign goal). The verdict names the dimension and what each side of it should do. The most common outcome, because different account types genuinely need different defaults.
Outcome C
Not Recommended
The weighted evidence agrees that disabling or avoiding this setting is the right default. Often because the setting auto-applies platform changes the practitioner can't reverse, or because measurement loss outweighs the surface-level gains. The verdict cites the evidence that ties out to this conclusion.

Recency

Why old advice loses weight.

Ad platforms change behaviour on a quarterly cadence. Advice that was correct in 2024 is often wrong in 2026 because the feature it described no longer exists, or now defaults to the opposite behaviour. The engine treats time as a weighting input, not just a sort order. In practice that means three things.

  • Older sources count less. A trade-press article from twelve months ago carries less weight in the synthesis than one from the last quarter, because the platform itself has likely changed since.
  • Platform announcements reset the clock. When the platform publishes a change to a feature, older third-party content about that feature is effectively demoted automatically.
  • Verdicts are generated fresh on demand. There is no cache of stale verdicts. Every check pulls the current evidence at the moment you ask.

Platform News

How the news feed stays accurate.

The daily news pipeline pulls candidate articles from across the open web, prioritising the six official platform newsrooms with the trade publications filling in where coverage is sparse, and runs every candidate through a Claude-based classifier before insertion. The classifier rejects on three independent axes.

  • Scope. Is this article about one of the six tracked ad platforms (Google Ads, Meta Ads, TikTok Ads, Microsoft Advertising including LinkedIn, Snapchat Ads, Pinterest Ads)? Adjacent topics (Google antitrust, YouTube creator economy, Meta earnings, Amazon DSP) get filtered out even when they look close.
  • Source. Is this from a publisher we trust? Content farms, low-quality aggregators, and AI-spam domains get rejected regardless of how reasonable the content looks.
  • Content. Is this displayable news? How-tos, opinion pieces, case studies, benchmark reports, recap posts, and predictions are useful background reading but they are not news, and get rejected by this axis.

A labelled fixture set runs through the classifier on every change to validate that all three rejection axes still hold at 100 percent recall. If a future classifier change drops below that threshold on any axis, the change doesn't ship.

Discipline

What we don't do.

Worth stating out loud, so the guardrails don't stay invisible.

  • No editorial bias by hand. Verdicts are the output of a defined synthesis process. No one at Shared Learnings hand-edits a verdict to make a platform look better or worse than the evidence supports.
  • No paid placement. Platforms and vendors cannot pay to be recommended, cannot pay to suppress a Not Recommended verdict, and cannot pay to alter source weighting. Not a future commitment, a current one. If it ever changes, the change goes here first.
  • No self-influence loop. When you query a verdict for a setting you've submitted a test on, your own test is excluded from the aggregate. You see the community's view of your contribution, not your own contribution echoed back at you.
  • No surfaced scoring math. How much each piece of evidence counts is decided internally and intentionally not displayed. Surfacing it would tempt gaming, force every screen to defend a formula, and distract from what matters: the cited sources behind the verdict. To verify a verdict, read the sources.
  • No verdict caching. Verdicts are not pre-computed or pre-warmed. Every run is fresh against the current evidence. Slower than caching, and we accept that tradeoff because a 2024 verdict served to a 2026 user is worse than a short wait.
  • No fake confidence. When the evidence is genuinely thin or contradictory, the verdict says so. A Context-Dependent outcome with three first-party tests behind it is not the same as one with three hundred, and we don't pretend it is.

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