Pages tagged "marketing"
29 pages tagged with marketing.
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- Customer-Perception Moments — How Style, Timing, and Structure Shape Judgment A Primores framework consolidating the wiki's behavioral-evidence research on customer perception: at discrete moments of judgment — the decision moment, the review-writing moment, the failure-recovery moment — small choices about content style, timing, and display structure have outsized, peer-reviewed effects. The cross-cutting meta-pattern: every headline behavioral finding comes with a context-dependent moderator (hedonic-vs-functional, severity, focal-customer) that can flip it. The practitioner discipline is to identify the moment and its moderators before applying the headline.
- AI Video Marketing — Beyond Synthetic Content How to use AI video tools (Sora 2, Gemini Omni, Veo 3.1, Meta Vibes) to enhance authentic marketing storytelling and produce ad-scale variant creative. May 2026 update: Google's Gemini Omni shifts the publisher's-tool-vs-artist's-tool split — Omni for ad-scale variant generation and text-rendering reliability, Sora 2 for cinematic short-form.
- AI Human Voice for Social Posts and Outreach — Six Techniques + Platform Tactics In 2026, AI-detection at the platform layer is a distribution constraint, not just a stylistic concern. Six prompting techniques produce human voice; the 80/20 hybrid ratio is empirically universal; platform-specific tactics vary across LinkedIn 360Brew, X Grok, TikTok C2PA, and cold-email deliverability. The generation-side complement to the ai-tells editing-side discipline.
- Marketing Analytics in 2026 — The Cookieless Stack Cookie deprecation broke last-click attribution. MMM adoption surged 212% since 2023. Data clean rooms + AI-driven attribution + incrementality testing now constitute the operating stack. The 2026 reality is dual-model: multi-touch for tactics, MMM for strategy, AI reconciles. Plus LTV/CAC cohort analysis as the capital-efficiency layer underneath.
- AI Tells in Sales Copy — Operator-Grade Audit Checklist Sales-page work converges on two disciplines: don't sound like AI (eleven-pattern catalog), and model the reader's motivation before drafting (structural pain modeling, vertical-specific). The audience-mode review beat catches tells; proactive reader-motivation modeling catches argument-level mismatches that no amount of voice polish can recover from.
- Telegram as a Marketing Channel — What the Data Actually Says Telegram has 1B+ MAU and is the dominant marketing channel for iGaming and for fashion DTC in Russia/CIS/MENA — but it is not the Western Web3 fashion channel that hype claims it is. Channel-fit for new platforms is geographic before it is categorical. Entry requires a keyword × region scout phase before scale.
- AI for Marketing — Overview Comprehensive guide to using AI in marketing for business professionals
- AI Marketing Case Studies: Real Results with Specific Numbers Named companies, specific metrics, measurable outcomes. No fluff, just what actually worked.
- Influencer Marketing's Task Overload: 19 Jobs in One Role, and Where AI Has the Clearest Use Case Modash's 2026 salary survey (n=499) found influencer marketers handle ~19 distinct weekly tasks. Most are AI-automatable execution work. The strategy and leadership tasks that stay human-leveraged also pay the most.
- Behavioral-Profile Fingerprinting — The Ratio-Based Measurement Framework for Organic Content Volume-only metrics collapse important distinctions. Four behavioral ratios (save/like, share/like, comment/like, follower/profile-view) characterize content output by behavioral profile rather than just reach. The framework converts content measurement from view-counting into deliberate behavioral-profile design.
- Brand vs Content Layers — Reconciling Sharp's Broad Reach with Primores' Narrow Authority Brand-building (mental availability via broad reach) and content-marketing (AI citation via narrow authority) operate at different layers. Both matter. Neither replaces the other. Most apparent contradictions in marketing strategy come from conflating the two.
- Discovery-Before-Scale — The Two-Phase Operational Framework for Organic Content Most content operators skip discovery and scale random content, producing noise. Discovery-Before-Scale is a two-phase architecture: 2-4 weeks of pattern × niche validation, then scale only validated patterns. The math (Pirolli & Card's optimal-diet selection) makes it non-optional.
- Distinctive Assets — What They Are and Why They Beat Differentiation Brand-specific cues (colors, logos, fonts, tone, mascots, jingles) that trigger recognition without active thought. Sharp's argument: distinctiveness builds mental availability; differentiation usually doesn't matter empirically.
- Double Jeopardy Law — Why Smaller Brands Get Hit Twice Smaller brands have fewer buyers AND those buyers buy slightly less often. Loyalty doesn't vary much across competing brands. Penetration is the lever for growth, not loyalty.
- Mental Availability — What It Is and Why It Drives Brand Growth A brand's propensity to be thought of in buying situations. Sharp's central thesis: brands grow by maximizing mental availability across many light buyers, not by deepening loyalty in a small core.
- Organic Content Strategy in the AI-Era: Why It Compounds and How to Engineer It AI-era organic content compounds while paid traffic decays. Producing it isn't viral chasing — it's pattern × niche fit discovered before scaling, designed for the behavioral profile that produces brand recall, not just views. Six-source academic foundation; ratio-fingerprinted case studies.
- Slideshow Pattern Design — Mapping the Nine Common Patterns to Cialdini's Six Principles The nine recurring short-form slideshow patterns aren't arbitrary — each leverages specific Cialdini persuasion principles. The mapping reveals which patterns drive saves vs shares vs comments vs follows, and why behavioral-profile design beats viral chasing.
- Awareness Levels — Schwartz's Framework for Understanding Your Audience Eugene Schwartz's Five Levels of Awareness and Market Sophistication stages — classic copywriting frameworks with modern AI applications
- The AI Interface Layer — When Claude Becomes Your App's Front Door How AI assistants are becoming the primary interface between users and apps, and what this means for brand visibility and marketing strategy
- Building Brand Voice Skills for Claude: The Technical Foundation How to create Claude Skills that maintain consistent brand voice across your team, grounded in LLM learning mechanics and proper skill architecture.
- GEO/AEO (Generative/Answer Engine Optimization) — What It Means Optimizing content for AI search engines like Perplexity, ChatGPT, and Google AI Overviews
- Astroturfing — Fake Grassroots Marketing Coordinated promotion disguised as organic community engagement. How to detect it and why authentic marketing wins long-term.
- Reddit Authenticity Patterns: Detecting Shills and Building Trust How to spot fake Reddit promotion (the Three-Post Pattern) and what it teaches about authentic engagement. Based on documented 2026 specimens.
- Ad Alchemy — AI-Assisted Creative Reverse Engineering Case study: Building a Claude skill that reverse-engineers competitor ads into reusable creative formulas for your own brand
- Experiment: Piggybacking Competitor Ad Concepts with AI Testing whether AI can extract reusable formulas from competitor ads and apply them to a new brand — fitme.lt learning from Tastier
- Rumpelstiltskin Effect — Why Naming Problems Drives Sales The psychological principle that giving a customer's problem a specific name builds trust, reduces anxiety, and positions your brand as the solution
- E-Commerce Purchase Psychology — Emotional & Cognitive Triggers Complete guide to psychological factors driving online purchases: emotional triggers (S-O-R) and cognitive factors (TAM) with practical implementation
- Preparing Your Brand for Agentic AI Strategic guide for brands navigating the shift to AI agent-mediated commerce and customer relationships
- AI-SEO Content Strategy — How to Get Cited by AI Search Practical guide to creating content that AI search engines will find, understand, and cite