Hoe vertraag je (lag)?

Mogelijk reageren klanten niet meteen op prijsverlagingen van Hoppiness. Daarom is het belangrijk om te checken of het effect van een prijspromotie kan doorlopen naar de week erna. Dat kun je onderzoeken door lags aan je model toe te voegen.

Een variabele laggen betekent dat je de tijdsbasis een bepaald aantal observaties terugschuift. Dit kan met de functie lag(). De functie lag() neemt maar één argument; standaard is n = 1 om het aantal te verschuiven perioden te bepalen.

Je past lag() toe op PRICE en vergelijkt het resultaat met de oorspronkelijke PRICE met behulp van cbind(). Om alleen de eerste zes elementen van de datakolommen te tonen, kun je de functie head() gebruiken.

Deze oefening maakt deel uit van de cursus

Responsmodellen bouwen in R

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Oefeninstructies

Bereken een vertragende PRICE-variabele met de functie lag().
Vergelijk de vertragende PRICE-variabele met de oorspronkelijke PRICE-variabele met de functies cbind() en head().

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Compare lagged PRICE to original PRICE
___(___(sales.data$PRICE, ___(sales.data$PRICE)))

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Responsmodellen bouwen in R

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus 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: 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: Uitreiding van het model deel 1: dummyvariabelen Exercise 2: Dummyvariabelen begrijpen Exercise 3: Het effect van display op sales Exercise 4: Het effect van meerdere dummies op verkoop Exercise 5: En de prijs dan?Exercise 6: Ubreidingen van het model deel 2: Dynamische variabelen Exercise 7: Hoe vertraag je (lag)?

Huidige oefening

Exercise 8: Vertraagde prijseffecten toevoegen Exercise 9: Meer lags Exercise 10: Wat is de toegevoegde waarde?Exercise 11: Hoeveel verlengingen zijn nodig?Exercise 12: Het model samenvatten Exercise 13: Onnodige voorspellers Exercise 14: Predictoren verwijderen Exercise 15: Predictors elimineren

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