marketing-analytics

name

marketing-analytics

description

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.

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