gigik2a Digital Marketing e Performance Marketing
Skill operativo per professionisti del digital marketing con esperienza BEMACS. Copertura completa da SEO/SEM fino a growth hacking, automation, e modelli di attribuzione multi-touch. Ogni modulo fornisce formule concrete, benchmark industriali certificati, e decision framework pronti all'uso.
Routing Table: Seleziona per Tema
| Tema | Reference File | Contenuti Chiave |
|---|---|---|
| SEO on-page & technical | references/seo-sem-fundamentals.md |
Title/meta/H1-H6, Core Web Vitals (LCP/FID/CLS), sitemaps, robots.txt, schema.org |
| SEM Google Ads | references/seo-sem-fundamentals.md |
Account structure, match types, Quality Score formula, bidding strategies |
| Keyword research | references/seo-sem-fundamentals.md |
Ahrefs/SEMrush, search volume, KD, CPC, intent classification, audit checklist |
| Email marketing lifecycle | references/email-marketing-automation.md |
Welcome series, abandoned cart, post-purchase, win-back, re-engagement |
| Deliverability email | references/email-marketing-automation.md |
SPF/DKIM/DMARC, sender reputation, spam triggers, inbox placement |
| Marketing automation | references/email-marketing-automation.md |
Lead scoring, drip campaigns trigger-based, segmentation, A/B testing email |
| Performance advertising | references/performance-advertising.md |
Google Ads (Search/Display/Shopping/Performance Max), Meta Ads, TikTok, LinkedIn B2B |
| Programmatic & RTB | references/performance-advertising.md |
DSP/SSP/DMP/CDP, header bidding, brand safety, viewability, fraud detection |
| Retargeting & attribution | references/performance-advertising.md |
Cart abandonment, product viewed, cross-device challenges, ROAS benchmark per industry |
| AARRR & North Star | references/growth-hacking-content.md |
Pirate Metrics, metriche per fase, North Star Metric framework, viral K-factor |
| Growth loops & experimentation | references/growth-hacking-content.md |
Content loops, paid loops, PLG (freemium, free trial), ICE/RICE scoring |
| Content strategy | references/growth-hacking-content.md |
Pillar+cluster, content mix (40/30/30), editorial calendar, SEO-driven planning |
| CRO & A/B testing | references/digital-funnel-conversion.md |
Hypothesis framework, statistical rigor (95% sig, 80% power), heatmaps, form analytics |
| Landing page optimization | references/digital-funnel-conversion.md |
Above-fold, social proof, trust signals, CTA placement, micro vs macro conversions |
| Funnel TOFU/MOFU/BOFU | references/digital-funnel-conversion.md |
Content/channel mix per fase, E-commerce (checkout, product page, upsell) |
| Attribution modeling | references/digital-funnel-conversion.md |
Last-click, first-click, linear, time decay, position-based U-shape, data-driven |
Operative Instructions
1. Quando inizi una nuova strategia digital:
- Parti dalla keyword research (volume, KD, intent) → mappa su TOFU/MOFU/BOFU
- Scegli canali primari (SEO, SEM, Email, Social Ads) in base a intent
- Defini il North Star Metric e AARRR sub-metriche per fase
2. Per campagne performance (SEM/Meta/LinkedIn):
- Valuta Quality Score (CTR + Relevance + LP Experience) → target >6/10
- Scegli bidding strategy in base a conversioni disponibili (tCPA/tROAS se dati sufficienti)
- A/B testa match types, copy, landing page con rigore statistico (95% sig, 80% power)
3. Per email marketing:
- Segmenta per comportamento (acquisti, engagement, tempo da ultimo contatto)
- Implementa lifecycle (welcome → post-purchase → win-back se >90d inattivo)
- Monitora deliverability (SPF/DKIM/DMARC), Open Rate (target 20-30%), CTR (2-5%)
4. Per growth hacking:
- Identifica growth loop primario (viral K-factor, content loop, paid loop)
- Priorizza con ICE score (Impact × Confidence × Ease)
- Misura retention (D1, D7, D30) e payback period
5. Per CRO:
- Mappa micro-conversioni (click, add-to-cart) + macro-conversioni (purchase)
- Formula ipotesi A/B test basate su heatmap/session recording
- Garantisci significance 95% e power 80% prima di rollout
6. Per attribuzione:
- Inizia con last-click (default), esplora first-click e linear
- Usa position-based U-shape (40/20/40) per comparare con data-driven se disponibile
- Riconcilia con CLV per validare model
Quick Reference: Key Formulas & Benchmarks
Performance Metrics
| Formula | Descrizione | Benchmark |
|---|---|---|
| CTR | Clicks / Impressions | 2-5% (SEM), 0.5-2% (Display), 3-8% (Email) |
| CPC | Spesa / Clicks | Varia per industria, ~€1-5 Search, ~€0.50-2 Display |
| CPA | Spesa / Conversioni | Target dipende da LTV |
| CPM | Spesa / (Impressions / 1000) | €5-20 Display, €10-50 Premium |
| ROAS | Revenue / Ad Spend | E-commerce 3-5x, SaaS 4-6x, Retail 4x |
| CAC Payback | CAC / (ARPU × Gross Margin) | <12 mesi ideale |
Google Ads Quality Score
Quality Score (1-10) = f(CTR, Ad Relevance, LP Experience)
Obiettivo: >6/10
CTR atteso per parola chiave / CTR effettivo = CTR component
Ad Relevance: Poor/Fair/Good/Excellent
LP Experience: Poor/Fair/Good/Excellent
Growth & Viral Metrics
| Metrica | Formula | Interpretazione |
|---|---|---|
| K-factor (viral) | (invites per user) × (conversion rate) | >1 = crescita esponenziale |
| D1 Retention | Active users day 1 / Cohort size | Target >30% consumer app |
| D7 Retention | Active users day 7 / Cohort size | Target >15-20% |
| Payback Period | CAC / (ARPU × Gross Margin %) | <6 mesi SaaS B2B |
Email Marketing
| KPI | Benchmark B2C | Benchmark B2B |
|---|---|---|
| Open Rate | 20-30% | 15-25% |
| CTR | 2-5% | 1.5-3% |
| Conversion Rate | 1-3% | 0.5-2% |
| Unsubscribe | <0.5% | <0.3% |
| Spam Complaint | <0.1% | <0.1% |
E-E-A-T Framework (Google Search Quality)
- Experience: Dimostra esperienza diretta
- Expertise: Qualifiche / credenziali autore
- Authoritativeness: Riconoscimento industria, backlink autorevoli, citazioni
- Trust: Trasparenza, GDPR compliance, SSL certificate, author bio, contact info
ICE Score per Priorizzazione Esperimenti
ICE = Impact (1-10) × Confidence (0-1) × Ease (1-10)
Esempio: [Impact:8 × Confidence:0.7 × Ease:6] = 33.6
Priorità: ICE >30 = alta, 15-30 = media, <15 = bassa
A/B Testing Rigor
- Significance level (α): 95% (p-value <0.05)
- Statistical power (1-β): 80% (β=0.20)
- Minimum Detectable Effect (MDE): Defini prima del test
- Sample size formula: n = 2[(Z_α/2 + Z_β)σ / MDE]²
Funnel Rates
| Fase | Metrica | Target Ragionevole |
|---|---|---|
| TOFU (Awareness) | Impressions → Click | CTR 0.5-2% (Display) |
| MOFU (Consideration) | Landing → Form submission | 5-15% |
| BOFU (Decision) | Cart add → Purchase | 2-4% (e-commerce) |
| Complessivo | Impression → Purchase | 0.1-0.5% (e-commerce) |
Cross-Skill References
- Combina con
marketing-analyticsper modelli quantitativi (CLV, attribution avanzata, cohort analysis) - Combina con
marketing-strategicoper STP (Segmentation/Targeting/Positioning), brand strategy, competitive positioning - Combina con
marketing-settori-creativiper contenuti (copy, visual, storytelling) in industrie creative
Come Usare Questo Skill
Scenario 1: Lancio campagna SEM
→ Consulta seo-sem-fundamentals.md → Crea account structure → Impostare Quality Score target (>6) → Configura bidding strategy (tCPA se conversion data sufficienti) → A/B testa copy e landing page
Scenario 2: Ottimizzare email marketing
→ Consulta email-marketing-automation.md → Definisci lifecycle stage (welcome, post-purchase, win-back) → Implementa segmentazione comportamentale → Monitora KPI (Open, CTR, spam complaint) → A/B testa subject line e CTA
Scenario 3: Accelerare growth con experimentation
→ Consulta growth-hacking-content.md → Identifica North Star Metric → Mappa AARRR per la tua fase → Formula ipotesi con ICE score → Esegui con rigore statistico 95% sig / 80% power
Scenario 4: Migliorare conversion rate landing page
→ Consulta digital-funnel-conversion.md → Esegui heatmap/session recording → Formula ipotesi per CRO A/B test → Calcola sample size per MDE → Monitora micro + macro conversions
Scenario 5: Modellare attribuzione digital
→ Consulta digital-funnel-conversion.md → Scegli modello iniziale (last-click, linear, position-based U) → Valida con CLV → Itera verso data-driven se dati sufficienti