AI advisors modelled on practitioners
@adamriemer
AI interpretation, not the person. This advisor is modelled on Adam Riemer's publicly available work and documented frameworks. It is not Adam Riemer. It does not speak as Adam Riemer, it does not fabricate quotes attributed to Adam Riemer, and it will not reproduce copyrighted material. Every response cites the public source it draws from.
Affiliate as a quality-first performance channel: recruit selectively, track attribution precisely
This is an AI interpretation of Adam Riemer's public work. Adam Riemer is a performance marketing consultant whose published blog and industry writing focuses on affiliate program management, partner recruitment quality, and affiliate attribution. He is known for emphasising compliance, fraud prevention, and long-term partner relationships over volume-based affiliate recruitment.
Summary of Adam Riemer's published affiliate recruitment framework: recruit affiliates based on audience fit, not follower count. The quality matrix scores affiliates on: (1) Audience overlap with target ICP (0-10), (2) Content quality and trust score (0-10), (3) Disclosure compliance (binary), (4) Conversion history or niche proof (0-10). Only recruit affiliates who score above threshold on all four dimensions. High-volume, low-quality affiliate recruitment drives fraud, brand damage, and attribution inflation.
Adam Riemer's published writing on affiliate attribution: the default 30-day last-click attribution window in most affiliate platforms incentivises coupon and loyalty affiliates who intercept conversions at the last step rather than driving new customer discovery. Best practices: (1) Use 7-day click windows for bottom-funnel affiliates, longer for content and comparison sites, (2) Exclude coupon codes that are not partner-unique, (3) Audit for cookie stuffing and conversion pixel fraud monthly.
In domain
When a question falls within affiliate, performance-marketing, partnerships and related areas, this advisor answers grounded in Adam Riemer's documented frameworks, with citations to the public source.
Out of domain
Out of domain, this advisor explicitly flags low confidence and defers rather than guessing. It never answers authoritatively outside its declared specialisation.
Example
“How do I build a paid social campaign on Meta for DTC?”
This question is about paid-social which is outside this advisor's declared domains.
Capabilities
2Schedule
Source scraping
onWeb research
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