Voorspellingen maken

Om betere prijsbeslissingen te nemen, moet de marketingmanager weten hoeveel verkochte aantallen hij kan verwachten bij alternatieve stuksprijzen. Hij wil specifiek de verwachte verkoopaantallen weten voor stuksprijzen van 1,05 en 0,95.

Je kunt de coëfficiënten uit je eerder gefitte lineaire sales-responsmodel gebruiken om deze voorspellingen te doen. De functie coef() geeft een numerieke coëfficiëntenvector met twee elementen terug: het eerste element is het intercept van sales en het tweede element is de prijsslope van het object linear.model. Je berekent de verwachte sales door simpelweg de prijsslope, vermenigvuldigd met 1,05 en 0,95, op te tellen bij het sales-intercept.

Deze oefening maakt deel uit van de cursus

Responsmodellen bouwen in R

Cursus bekijken

Oefeninstructies

Haal de geschatte coëfficiënten op uit het object linear.model met de functie coef(). Extraheer het sales-intercept en de prijsslope afzonderlijk via numerieke indexering.
Bereken de verwachte verkoopaantallen voor de stuksprijzen 1,05 en 0,95.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Obtain the intercept coefficient
coef(linear.model)[___]

# Obtain the slope coefficient
coef(linear.model)[___]

# Calculate the volume sales for the unit price of 1.05 
___(linear.model)[___] + ___ * ___(linear.model)[___]

# Calculate the volume sales for the unit price of 0.95 
___(___)[___] + ___ * ___(___)[___]

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: Basisprincipes van markt-responscurvemodellen Exercise 2: Detailhandelsverkopen Exercise 3: Inzicht in sales Exercise 4: Lineaire responsemodellen Exercise 5: Een lineair responsmodel voor sales Exercise 6: Voorspellingen maken

Huidige oefening

Exercise 7: Voorspellende prestaties Exercise 8: Niet-lineaire responscurvemodellen Exercise 9: Niet-lineaire functies lineariseren Exercise 10: Wat is de meerwaarde?

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