Pages tagged "methodology"
14 pages tagged with methodology.
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- The Brand Email Design System — The Durable Anchor for AI Email Production A brand email design system is the human-authored asset that makes AI email production output your brand, not generic slop. The seven layers a usable one needs, and why each earns its place.
- Evidence-Graded Audience Research: Units Instead of Avatars A research methodology that replaces persona theater with evidence-graded units — every pain, audience, and angle labeled provided, researched, or hypothesis.
- AI Product Video Without Wrecking the Product — The Composite + Keyframe Method How to produce high-fidelity AI product video for reflective, fine-detail products (jewelry, watches, packaging): composite the real product photo, let AI generate only the environment, and animate with first-last-frame keyframing instead of single-frame image-to-video. The two moves that stop the model hallucinating the one thing that has to stay exact.
- Reference-Image Conditioning — Show, Don't Tell, the AI Aesthetic Controlling AI image/video aesthetics by feeding reference images (composition, palette, structure, style) instead of writing descriptive prose. Covers what each reference slot controls, the main tools (Midjourney sref/cref/cw, Flux Kontext, Soul guided generation), when reference beats prose and when it doesn't — and the brand-coherence move: feeding the client's own assets so the output looks like them.
- Win-Loss Analysis — The Highest-Leverage CI Layer Win-loss analysis interviews recent prospects (both won and lost deals) to understand the actual reasons behind decisions. The CI layer practitioners credit most with moving win rate — the mechanism (structured retrospective review) is anchored in tier-1 AAR/debrief meta-analyses, though the exact win-rate magnitude is a practitioner figure. Klue/Crayon convergence on 8 best practices: organizational buy-in, defined goals, senior decision-makers as respondents, deals competed to the bitter end, direct buyer interviews (not sales reps), 30–90 day timing, multi-source triangulation, quantitative + qualitative analysis.
- Continuous Competitive Monitoring — The 2026 CI Discipline Always-on tracking of competitor signals across web, pricing, product, hiring, ads, and reviews — replacing the stale quarterly competitive-landscape deck. The 2026 bottleneck is no longer data collection (Crayon tracks 100+ signal types per competitor); it's signal-to-noise triage and routing intelligence to the people who decide. AI compresses synthesis; decision-linkage discipline stays human.
- Creative Reverse Engineering — Extracting Structural Patterns from Competitor Ads The systematic discipline of deconstructing competitor ad creative to extract reusable structural patterns — composition, lighting, focal hierarchy, copy skeleton — without copying the brand-specific skin. The 2026 vision-LLM stack (Claude + GPT-4o + Foreplay / Atria / Motion + Meta Ad Library) compressed what used to be an art-director consulting engagement into an afternoon. Pattern extraction across many examples is the load-bearing move; single-ad deconstruction underperforms.
- Competitor Analysis in 2026 — The Operational Approach The biggest competitor-analysis failure mode in 2026 isn't analytical — it's operational. Most teams treat CI as a quarterly slide-generation exercise instead of a continuous system. This page covers the methodology that actually drives decisions: five operational layers (win-loss analysis, continuous monitoring, battlecards, share of model, creative reverse engineering) tied to specific decision triggers, with AI compressing the execution work while the strategy work stays human-leveraged.
- The Strategist Pattern — Turn a Wiki Into a Thinking Partner Wiki + Claude + ~8 small config files = a domain-specific strategist. Knowledge in the wiki, capabilities in skills, persona in priors. Sessions end with better thinking, not always an artifact.
- How This Wiki Is Built The methodology behind an AI-maintained knowledge base that compounds over time
- Experiments — Testing What Actually Works Real tests with real results — our laboratory for validating AI approaches before recommending them
- Substance Ranking — Content Quality Over Popularity A content evaluation method that scores comments and posts on evidence quality rather than engagement metrics like upvotes or likes
- LLM Wiki Pattern — What It Means A pattern for building compounding knowledge bases using LLMs
- Zettelkasten — What It Means A note-taking methodology of interconnected atomic notes, perfect for LLM-maintained knowledge bases