# Marketing Analytics

> Marketing analytics e modelli quantitativi data-driven. Regressione, choice modeling, CLV, brand equity, advertising effectiveness, research methodology, digital/social analytics. AI-applied: neuroscience copy testing 8 scores, storytelling 12-step, dynamic pricing PID, Big Five brand personality, celebrity selection, segmentazione AI PCA+clustering metaphor/ facet-based, promotion journey 7-stage, loyalty card switching algorithms. Bocconi BEMACS + Pradeep/Appel/Sthanunathan. Usa SEMPRE per: dummy clustering RFM, logistic regression, BAV, Interbrand, CLV, GRP, ROI, conjoint, A/B testing, churn, NPS, CAC, LTV/CAC, CTR, CPC, Tobin's Q, causalità IV 2SLS, neuroscience copy testing, Big Five brand, PID pricing, programmatic ads, PCA brand perception, factor analysis, Bayes classification, data sources AI, data cleanup, product naming 30 categorie. Attiva per: regressione marketing, brand equity, efficacia campagna, segmentazione AI, ROI, GRP, RFM, copy testing, dynamic pricing, brand personality Big Five.

- Skill: `gigik2a/marketing-analytics` (Agent Skill, multi-file: 6 files)
- Install (CLI): `npx skillmds@latest add gigik2a/marketing-analytics`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gigik2a/marketing-analytics/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: gigik2a (https://skillmd.com/u/gigik2a)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/gigik2a/marketing-analytics

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# Marketing Analytics e Modelli Quantitativi

Guida operativa ai modelli quantitativi per decisioni di marketing data-driven: dalla regressione alla misurazione del brand equity, dal CLV all'analisi dell'efficacia pubblicitaria, alla ricerca di mercato strutturata. Ogni sezione include formule, interpretazione e applicazione pratica.

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## Routing Table: Quale Reference File Usare

| Tema | Reference File | Sezioni |
|------|---|---|
| **Regressione lineare, dummy variables, interazioni, effetti non lineari** | `modelli-regressione-segmentazione.md` | 1.1-1.4 |
| **Cluster analysis, RFM segmentation** | `modelli-regressione-segmentazione.md` | 2.1-2.2 |
| **Logistic regression, MNL, choice modeling** | `choice-modeling-brand-equity.md` | 3.1-3.3 |
| **Brand equity: BAV, Interbrand, Keller CBBE** | `choice-modeling-brand-equity.md` | 4.1-4.3 |
| **GRP, CPM, CPRP, curve di risposta, media planning** | `advertising-clv-performance.md` | 5.1-5.3 |
| **Misurazione efficacia pubblicitaria, MMM, attribution, incrementality** | `advertising-clv-performance.md` | 5.4 |
| **CLV formula base, varianti, CAC, LTV/CAC, Customer Equity** | `advertising-clv-performance.md` | 6.1-6.4 |
| **Dashboard KPI, funnel analysis, attribution incrementality** | `advertising-clv-performance.md` | 7.1-7.3 |
| **Metriche social, engagement, virality, SOV** | `social-media-digital-analytics.md` | 8.1 |
| **Influencer marketing: selezione, tipologie, ROI** | `social-media-digital-analytics.md` | 8.2 |
| **Connected consumers, tassonomia digitale** | `social-media-digital-analytics.md` | 11.2 |
| **Social tagging, brand familiarity/favorability** | `social-media-digital-analytics.md` | 11.3 |
| **Crowdsourcing, owned/paid/earned media** | `social-media-digital-analytics.md` | 11.4-11.5 |
| **Adozione innovazione, opinion leaders, market mavens** | `social-media-digital-analytics.md` | 11.6 |
| **Digital advertising, search keywords, CTR, CPC, display ads** | `social-media-digital-analytics.md` | 11.7 |
| **Influencer tassonomia micro/macro/mega** | `social-media-digital-analytics.md` | 11.8 |
| **Firm value, Tobin's Q, stock returns, myopic management** | `social-media-digital-analytics.md` | 10.1-10.5 |
| **Causalità, omitted variable, FE, RE, IV/2SLS** | `social-media-digital-analytics.md` | 10.5 |
| **Processo ricerca (6 step), secondary data** | `research-methodology.md` | 9.1-9.2 |
| **Qualitative research: focus group, depth interviews, projective techniques** | `research-methodology.md` | 9.3 |
| **Survey methods, observation, causal research, experimental designs** | `research-methodology.md` | 9.4-9.5 |
| **Measurement & scaling, Likert, semantic differential, Stapel** | `research-methodology.md` | 9.6 |
| **Questionnaire design, wording rules, pretesting** | `research-methodology.md` | 9.7 |
| **Sampling: probability e non-probability, sample size** | `research-methodology.md` | 9.8 |
| **Data preparation: editing, coding, consistency checks** | `research-methodology.md` | 9.9 |
| **Analisi descrittive, bivariata, cross-tabulation, chi-square** | `research-methodology.md` | 9.9 |
| **Hypothesis testing, z/t/F-test, Type I/II, power** | `research-methodology.md` | 9.9 |
| **Correlazione, regressione bivariata e multipla** | `research-methodology.md` | 9.9 |
| **Segmentazione AI-driven, PCA+clustering, metaphor/facet-based, segment fusion** | `modelli-regressione-segmentazione.md` | 3.1-3.5 |
| **Five-Factor Model (OCEAN), personality extraction, inverse hierarchy of needs** | `modelli-regressione-segmentazione.md` | 3.4-3.5 |
| **Brand personality Big Five, 5 archetipi, brand tracking AI** | `choice-modeling-brand-equity.md` | 4.4-4.6 |
| **Celebrity spokesperson selection, M&A brand portfolio, product naming AI** | `choice-modeling-brand-equity.md` | 4.7-4.8 |
| **Neuroscience copy testing 15 scores (8 core + 7 extended: music/lyric/optical/slow motion/context/metaphor/brand semiotics), factor analysis creative** | `advertising-clv-performance.md` | 8.1 |
| **Algorithmic storytelling 12 step** | `advertising-clv-performance.md` | 8.2 |
| **Ad templates per formato, programmatic ad purchase logic** | `advertising-clv-performance.md` | 8.3-8.4 |
| **Dynamic pricing PID controller, ~15 euristiche pricing** | `advertising-clv-performance.md` | 8.5 |
| **Promotions AI: 7-stage journey, 5-part template, loyalty card analytics** | `advertising-clv-performance.md` | 8.6 |
| **AI Data Sources taxonomy (18+ fonti), data ethics, dati consci vs non-consci** | `research-methodology.md` | 9.10 |
| **Data cleanup AI: missing data, normalizzazione, destagionalizzazione, anomaly detection** | `research-methodology.md` | 9.11 |
| **Metriche di distanza per segmentazione (Euclidea, Manhattan, Chebyshev)** | `modelli-regressione-segmentazione.md` | 4.1 |
| **K-Centers clustering, celle di Voronoi, 3 approcci clustering** | `modelli-regressione-segmentazione.md` | 4.2 |
| **Classificazione Bayesiana per consumer profiling** | `modelli-regressione-segmentazione.md` | 4.3 |
| **PCA per brand perception, matrice covarianza, autovettori, perceptual maps** | `modelli-regressione-segmentazione.md` | 4.4 |
| **Factor Analysis vs PCA, dimensioni latenti brand** | `modelli-regressione-segmentazione.md` | 4.5 |
| **Pipeline PCA→Clustering completa per segmentazione AI-driven** | `modelli-regressione-segmentazione.md` | 4.6 |
| **Product naming AI: 5-step process, 30 categorie naming** | `choice-modeling-brand-equity.md` | 4.8 |

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## Operative Instructions

1. **Chiedi i dati disponibili**: prima di suggerire un modello, verifica quali dati il cliente ha (CRM, vendite, web analytics, survey, studi qualitativi)
2. **Parti dal problema di business**: non dalla tecnica. Il modello serve a rispondere a una domanda manageriale
3. **Spiega i risultati in linguaggio business**: "un incremento di 1€ nel budget ADV genera 3,50€ di vendite incrementali" è meglio di "β = 3.50, p < 0.01"
4. **Usa le formule**: questa skill è quantitativa — calcola CLV, GRP, elasticità, ROI con numeri concreti
5. **Valida sempre**: R², significatività, overfitting, multicollinearità (VIF), eteroschedasticità

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## Quick Reference: Key Formulas

- **Regressione**: Y = β₀ + β₁X₁ + ... + βₖXₖ + ε
- **Logit**: P(Y=1) = 1 / (1 + e^(-(β₀ + β₁X₁ + ...)))
- **MNL**: P(scelta j) = e^(Vⱼ) / Σₖ e^(Vₖ)
- **GRP**: Reach (%) × Frequency
- **CPM**: (Costo totale / Impressions) × 1.000
- **CPRP**: Costo totale / GRP
- **CLV Base**: M × (r / (1+i-r)) - AC
- **CAC**: Spesa marketing e vendite / Nuovi clienti
- **Elasticità (log-log)**: β₁ = % variazione Y / % variazione X
- **Chi-square**: χ² = Σ ((Osservato - Atteso)² / Atteso)
- **Tobin's Q**: Valore mercato / Costo sostituzione asset
- **PID Pricing**: ΔP = Kp×(D_target−D_actual) + Ki×Σerror + Kd×d(error)/dt
- **Neuroscience Copy Score**: 8 dimensioni (motion, novelty, error, ambiguity, implicit humanity, no cortisol, voice-over, sound) scala 1-10
- **Big Five Brand Personality**: Explorer (O), Director (C), Connector (E), Caregiver (A), Sentinel (N inv.)
- **Distanze segmentazione**: Euclidea √(Σ(aᵢ-bᵢ)²), Manhattan Σ|aᵢ-bᵢ|, Max max|aᵢ-bᵢ|
- **Bayes consumer profiling**: P(Seg|Data) = P(Data|Seg)×P(Seg)/P(Data)

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## Cross-Skill References

- Combina con `marketing-strategico` per framework strategico (STP, 4P, Porter)
- Usa questa skill per i modelli quantitativi; marketing-strategico per la strategia