Tahminler yapmak

Fiyatlandırma kararlarını iyileştirmek için, pazarlama müdürünün alternatif birim fiyatlar altında beklenen satış hacmini bilmesi gerekiyor. Özellikle, 1.05 ve 0.95 birim fiyatları için beklenen satış hacmini öğrenmek istiyor.

Bu tahminleri yapmak için, daha önce kurduğun doğrusal satış tepki modelinin katsayılarını kullanabilirsin. coef() fonksiyonu, iki öğeden oluşan sayısal bir katsayı vektörü döndürür: İlk öğe satış kesişimini (intercept), ikinci öğe ise linear.model nesnesinin fiyat eğimini (slope) verir. Beklenen satışları, satış kesişimine 1.05 ve 0.95 ile çarpılmış fiyat eğimini ekleyerek basitçe hesaplarsın.

Bu egzersiz

R ile Tepki Modelleri Oluşturma

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

coef() fonksiyonunu kullanarak linear.model nesnesinden tahmini katsayıları elde et. Sayısal indeksleme ile satış kesişimi ve fiyat eğimi katsayılarını ayrı ayrı çıkar.
1.05 ve 0.95 birim fiyatları için beklenen satış hacmini hesapla.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# 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 
___(___)[___] + ___ * ___(___)[___]

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Tepki Modelleri Oluşturma

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Pazar tepki modellerinin temelleri Exercise 2: Perakende satışlar Exercise 3: Satışları anlama Exercise 4: Doğrusal tepki modelleri Exercise 5: Satışlar için doğrusal bir tepki modeli Exercise 6: Tahminler yapmak

Geçerli Egzersiz

Exercise 7: Öngörü performansı Exercise 8: Doğrusal olmayan tepki modelleri Exercise 9: Doğrusal olmayan fonksiyonları doğrusal hâle getirme Exercise 10: Eklenen değer ne?

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