Vendite retail

Lavori come analista per un'azienda di ricerche di marketing. Supporti il marketing manager di un microbirrificio locale chiamato the 3 MuskeBeers nell'analisi e valutazione del suo prodotto principale, Hoppiness. Il dataset a tua disposizione, chiamato sales.data, è già stato caricato in R.

Il primo compito è capire la struttura dei tuoi dati. Puoi usare la funzione str() per visualizzare in modo compatto la struttura e le prime osservazioni di sales.data. In alternativa, puoi usare la funzione tail() per visualizzare solo la parte finale dei dati.

Questo esercizio fa parte del corso

Creare modelli di risposta in R

Visualizza il corso

Istruzioni dell'esercizio

Visualizza la struttura di sales.data usando la funzione str().
Visualizza le ultime 6 osservazioni di sales.data usando la funzione tail().

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Structure of sales.data


# Tail of sales.data

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Creare modelli di risposta in R

IntermediárioNível de habilidade

4.8+

Inizia il corso gratis

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: Fondamenti dei modelli di risposta del mercato Exercise 2: Vendite retail

Esercizio in corso

Exercise 3: Capire le vendite Exercise 4: Modelli di risposta lineari Exercise 5: Un modello di risposta lineare per le vendite Exercise 6: Fare previsioni Exercise 7: Prestazioni predittive Exercise 8: Modelli di risposta non lineari Exercise 9: Linearizzare funzioni non lineari Exercise 10: Qual è il valore aggiunto?

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: Model extension part 1: Dummy variables Exercise 2: Understanding dummy variables Exercise 3: The effect of display on sales Exercise 4: The effect of multiple dummies on sales Exercise 5: What about price?Exercise 6: Model extensions part 2: Dynamic variables Exercise 7: How to lag?Exercise 8: Adding lagged price effects Exercise 9: More lags Exercise 10: What's the value added?Exercise 11: How many extensions are needed?Exercise 12: Summarizing the model Exercise 13: Unnecessary predictors Exercise 14: Dropping predictors Exercise 15: Eliminating predictors

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