Fundamentals of market response models Kathrin Gruber Assistant - - PowerPoint PPT Presentation

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fundamentals of market response models

Fundamentals of market response models Kathrin Gruber Assistant - - PowerPoint PPT Presentation

Feb 23, 2023 221 likes •434 views

DataCamp Building Response Models in R BUILDING RESPONSE MODELS IN R Fundamentals of market response models Kathrin Gruber Assistant Professor of Econometrics Erasmus University Rotterdam DataCamp Building Response Models in R Marketing mix

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DataCamp Building Response Models in R

Fundamentals of market response models

BUILDING RESPONSE MODELS IN R

Kathrin Gruber

Assistant Professor of Econometrics Erasmus University Rotterdam

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DataCamp Building Response Models in R

Marketing mix

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DataCamp Building Response Models in R

Market response models

Leverage information based on past data to: Adjust product prices. Optimize marketing tactics and strategies. Test the effectiveness of marketing plans. Plan future marketing activities.

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DataCamp Building Response Models in R

Retail sales

OBS: observation week SALES: volume sales PRICE: average unit price DISPLAY, COUPON, DISPLAYCOUPON: advertising, promotion activities

str(sales.data) 'data.frame': 124 obs. of 6 variables: $ OBS : int 1 2 3 4 5 6 7 8 9 10 ... $ SALES : num 22.6 22.9 80.6 85.1 81.9 ... $ PRICE : num 1.09 1.27 1.27 1.27 1.27 ... $ DISPLAY : int 0 0 0 0 0 0 0 0 0 0 ... $ COUPON : int 0 0 0 0 0 0 0 0 0 1 ... $ DISPLAYCOUPON: int 0 0 0 0 0 0 0 0 0 0 ...

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DataCamp Building Response Models in R

Understanding sales

mean(sales.data$SALES) 119.7319 min(sales.data$SALES) 11.66749 max(sales.data$SALES) 752.7219

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DataCamp Building Response Models in R

Let's practice!

BUILDING RESPONSE MODELS IN R

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DataCamp Building Response Models in R

Linear response models

BUILDING RESPONSE MODELS IN R

Kathrin Gruber

Assistant Professor of Econometrics Erasmus University Rotterdam

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DataCamp Building Response Models in R

Model elements

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DataCamp Building Response Models in R

Linear response function

SALES = f(PRICE)

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DataCamp Building Response Models in R

Building a linear response model for sales

linear.model <- lm(SALES ~ PRICE , data = sales.data) coef(linear.model) (Intercept) PRICE 274.2486 -134.3097

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DataCamp Building Response Models in R

Making predictions

coef(linear.model)[1] + 0.95 * coef(linear.model)[2] 146.6544 coef(linear.model)[1] + 1.05 * coef(linear.model)[2] 133.2234

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DataCamp Building Response Models in R

Predictive performance

plot(SALES ~ PRICE, data = sales.data) abline(linear.model)

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DataCamp Building Response Models in R

Let's practice!

BUILDING RESPONSE MODELS IN R

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DataCamp Building Response Models in R

Nonlinear response models

BUILDING RESPONSE MODELS IN R

Kathrin Gruber

Assistant Professor of Econometrics Erasmus University Rotterdam

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DataCamp Building Response Models in R

Linear response function

What happens when PRICE is zero or very large? What about threshold effects?

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DataCamp Building Response Models in R

Exponential response function

Assumes a constant percentage change (growth rate).

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DataCamp Building Response Models in R

Linearizing

Nonlinear model: Sales = β ⋆ exp(β ⋆ Price) Linearized model: log(Sales) = log(β ) + (β ⋆ Price) percentage change: β ⋆ 100 = (-) 66 %

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log.model <- lm(log(SALES) ~ PRICE, data = sales.data) coef(log.model) (Intercept) PRICE 5.0843983 -0.6622516

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DataCamp Building Response Models in R

What's the value added?

plot(log(SALES) ~ PRICE, data = sales.data) log.model <- lm(log(SALES) ~ PRICE, data = sales.data) abline(log.model)

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DataCamp Building Response Models in R

Let's practice!

BUILDING RESPONSE MODELS IN R