Editorial Policy
Signal & Convert serves a professional audience of working marketers who rely on this publication for practical guidance they can apply in their work and cite in internal advocacy. These standards document how we maintain the accuracy, honesty, and credibility they require.
Last-Reviewed Date Practice
Every platform-specific article (Google Ads, Meta Ads, Salesforce, HubSpot, etc.) and every tool profile in the AI Tools library carries a last-reviewed date. This date records when the article was last verified against current platform features, tool pricing, and product capabilities.
AI advertising platforms, SEO tools, and marketing software update on monthly cycles. An article that was accurate six months ago may describe deprecated features, outdated pricing, or changed workflows. The last-reviewed date gives readers a concrete signal to assess whether the guidance is likely still current before investing time in reading it.
When reading platform-specific or tool-specific content, always check the last-reviewed date and verify critical details — especially pricing, feature availability, and UI workflows — against official platform documentation before acting on them.
Case Study Sourcing Standards
The case study library includes only real-brand AI marketing implementations with independently verifiable outcomes. Each entry requires:
- A named brand or company (no anonymized "a major retailer" entries)
- A specific, measurable outcome (not a vague "improved results" claim)
- A primary source URL — an original document from the brand, a credible third-party publication, or an official press release — that readers can verify independently
- An outcome that can be assessed without relying solely on vendor-produced marketing materials
Vendor-supplied case studies that exist only in vendor marketing materials, without independent verification or a direct primary source, are excluded. This standard is not a reflection on the quality of those implementations — it is an editorial boundary that maintains the library's credibility for managers who will cite these examples in internal presentations.
If you are aware of a real-brand AI marketing implementation with a verifiable outcome that should be included, contact us with the primary source.
Data Citation and No-Hype Commitment
Statistics and research data
When we cite statistics, market sizing figures, or research findings, we include the original source organization, the year the data was published, and a link to the original report or page where possible. We do not present statistical claims without attribution.
Market research in the AI marketing space can vary widely in methodology and recency. Where we cite multiple conflicting figures, we note the source and acknowledge the variance rather than selecting the most favorable number.
No overpromising of outcomes
We do not claim specific revenue lifts, conversion improvements, or cost reductions from AI tools or approaches unless those figures are sourced from a verifiable, specific implementation with a named brand and primary source.
General statements like “AI can improve conversion rates by X%” are not used without specific attribution and context. AI tool outcomes depend heavily on implementation quality, team expertise, data quality, and business context — outcomes that work for one organization may not transfer to another.
This commitment is not just a style preference. The FTC has taken enforcement action against deceptive AI performance claims. Professional marketers have developed acute sensitivity to overclaiming. Credibility with this audience requires honest, caveat-appropriate framing of what AI can and cannot reliably deliver.
Honest tool limitations
Tool profiles include a Notable Limitations field that documents known gaps, pricing considerations at scale, and workflow problems that practitioners have encountered. This field is not promotional. It is the trust-building element that differentiates structured tool profiles from vendor marketing copy.