E il prezzo?

Fermo! Hai dimenticato di includere l'effetto delle variazioni temporanee di PRICE nel tuo modello. Puoi correggere questo errore usando la funzione update(). La funzione update() ri-stima il modello di risposta con variabili fittizie aggiornando e valutando la relazione log(SALES) ~ DISPLAY + COUPON + DISPLAYCOUPON memorizzata nell'oggetto dummy.model.

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Creare modelli di risposta in R

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Istruzioni dell'esercizio

Aggiorna l'oggetto dummy.model per PRICE usando la funzione update().

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Update the model for PRICE
___(dummy.model, . ~ . + ___)

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Questo esercizio fa parte del corso

Creare modelli di risposta in R

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The first chapter introduces you to the basic principles and concepts of market response models. Here, you will learn how to build simple response models for product sales. In addition, you will learn about the theoretical and practical differences between linear and non-linear models for sales responses.

Exercise 1: Fundamentals of market response models Exercise 2: Retail sales Exercise 3: Understanding sales Exercise 4: Linear response models Exercise 5: A linear response model for sales Exercise 6: Making predictions Exercise 7: Predictive performance Exercise 8: Nonlinear response models Exercise 9: Linearizing nonlinear functions Exercise 10: What 's the value added?

An effective marketing strategy combines all the tools available to communicate the benefits of a product. The key is crafting the right mix of these tools to achieve sales increases and market share goals. In the second chapter, you will learn how to incorporate the effects of advertising and promotion in your sales-response model and how to identify the marketing strategy that is most likely to succeed.

Exercise 1: Estensione del modello parte 1: variabili dummy Exercise 2: Capire le variabili dummy Exercise 3: L'effetto dell'esposizione sulle vendite Exercise 4: L'effetto di più variabili dummy sulle vendite Exercise 5: E il prezzo?

Esercizio in corso

Exercise 6: Estensioni del modello parte 2: Variabili dinamiche Exercise 7: Come applicare un lag?Exercise 8: Aggiungere effetti di prezzo ritardati Exercise 9: Altri ritardi Exercise 10: Qual è il valore aggiunto?Exercise 11: Quante estensioni servono?Exercise 12: Riepilogare il modello Exercise 13: Predittori non necessari Exercise 14: Eliminare predittori Exercise 15: Eliminare i predittori

A company can only be successful in the market if its products have a competitive advantage over those of its rivals. To develop an effective marketing strategy in a competitive environment, it is essential to understand the interrelationship between marketing activity and customer behavior. In this chapter, you will learn how to explain the effects of temporary price changes on customer brand choice by employing logistic and probit response models.

Exercise 1: Models for individual demand Exercise 2: Customer purchases Exercise 3: Summarizing the data Exercise 4: Competition Exercise 5: A linear probability model for beer demand Exercise 6: Logistic response models Exercise 7: A logistic model for beer demand Exercise 8: Bounded predictions Exercise 9: Average marginal effects Exercise 10: Effect plots Exercise 11: Probit response models Exercise 12: A probit model for beer demand Exercise 13: Logistic vs. probit Exercise 14: Model comparison

The main goal of response modeling is to enable marketers to not only see a payoff for their actions today, but also tomorrow. In order to view this future payoff, a simple but reliable statistical model is required. In this last chapter, you will learn how to evaluate the predictive performance of logistic response models.

Exercise 1: Model selection Exercise 2: Extending the logistic response model Exercise 3: Summarizing the model Exercise 4: The deviance principle Exercise 5: Eliminating predictors Exercise 6: Predictive performance Exercise 7: Classifications Exercise 8: Model confusion Exercise 9: ROC curves Exercise 10: Model validation Exercise 11: Subsetting Exercise 12: Model training Exercise 13: Out-of-sample testing Exercise 14: Wrapping it up