Complete summary of all the lectures, tutorials and cases of the course Marketing Strategy Research given by Xi Chen in the master program Marketing Management.
Good luck with studying!
WEEK 1 | INTRODUCTION
Consumers data tools strategy
There are 9.932 analytical tools – this course covers 5
o Linear regression: market responses
o Conjoint analysis: new project design
o Bass model: new project diffusion
o Cluster analysis: segmentation
o Multi-dimensional scaling: positioning
Principles of data-driven marketing
1. Any statistical analysis is to reduce information loss
2. Causation cannot be learned directly from data
3. Prediction does not care about statistical significance
4. Practical usefulness triumphs statistical criteria
Case: pricing strategy for Jetstar
o Formulate strategies based on analytical results
WEEK 2 | LINEAR REGRESSION
LECTURE
What & why: intro to predictive modeling
o Market response model: how to predict market response
o E.g. Target knowing when someone is pregnant based on behavior
o Prediction machine: find functional relationship between input (data) and output (prediction)
o Linear regression = simplest form, straight line / : y = a + bx - with an intercept and b slope
Terminologies: with a toy example, using price to predict sales - sales = a + b*price
o X: price = independent variable - input
o Y: sales = dependent variable - output
o Principle: any statistical analysis is to reduce information loss
Prediction as close as possible to observations – choose line to minimize differences
How: 5 steps to perform a linear regression
o Examining the data: make sure data is clean, check for correlation, multi-collinearity, etc.
Multi-collinearity: VIF < 10 not an issue, VIF > 10 high collinearity
High correlation indicates trouble, get biased and misleading estimated coefficients
Use one variable in regression, transform correlated variables, collect more data
o Formulating the model: decide which variables to use as input: IV's, DV, and residual
Translate conceptual model to a R formula
o Estimating the model: any statistical analysis to minimize information loss (residuals)
Choose coefficients so differences (residuals) between actual & predicted are minimized
Least squares criterion: minimize residual sum of squares (RSS)
o Validating the model: look at model's significance
Naïve prediction: prediction with only intercepts, but no other IV's - assumption
Null hypothesis using F statistics and check p-value in R output - significance
R-squared: model fit or strength of association - % of variation in DV explained by model
How good is the model for prediction? – validate the model
Test significance of individual coefficient: H0, t-test, check p-value
, o Making predictions: use predict() function, a new data set and confidence interval
Extending the use of linear regression
o Nominal variables: cannot directly put into a regression - need to be numeric
Designate a variable as factor: R will do the rest – weather <- as.factor(weather)
o Dummy coding (binary variable, 0-1) - always baseline
M-1 dummy variables, choose baseline: weather <- relevel (weather, ref=”sunny”)
o Interpretation of coefficients: we only know the difference between conditions
When coefficient not significant: difference of baseline not significant: same level
Risk control: assumptions in linear regression on residuals
o Normality - test using residuals in histogram or K-S test
o Equal variance - test using scatter plot or Y^ and residuals
Obtain residuals and DV -> standardize both -> draw scatter plot x-as DV and y-as residue
TUTORIAL
Step 1 checking the VIFS: vnames <- colnames(train)[2:5] & vif(vnames, train)
Step 2 formulate model: Sales = β0 + β1IV1 + β2IV2 + β3IV3 + β4IV4 + е
Step 3 estimate model: model <- lm(Sales ~ IV + IV + IV + IV, data = train) & summary(model)
Step 4 validate model: check significance of overall model and coefficients
o Test H0: β1 = β2 = … = 0 & H0: βk = 0 | when p < 0.05 reject H0: predictive value
o Check R-squared = % variation explained by model – depends on environment (>90% sales)
Step 5 make predictions: test <- set[76:100,] & str(test) & model2 <-predict(model, newdata = test)
o Model2 <- as.data.frame(model2) & model2$week <- 76:100 & ggplot
Comparison: Repeat steps without IV Brand Equity and compare the two models
Risk control: violation leads to biased estimation and bad prediction
o Normality assumption: KS test H0: The variable follows a normal distribution
H0 should not be rejected, so KS test should NOT be significant p > 0.05
o Equal variance assumption: plot residuals and check if span/ranges are similar
Categorical variables cannot go in regression: first transformed to factors – setting a baseline
o Interpretation is tricky: always relative to the baseline
CASE
Product line cannibalization = older lines not selling anymore after introducing new ones
Objective: to find possible cannibalization effects
o How introduction and sales of new styles influence sales of the previous line
Causation ≠ correlation – causal structures can produce same correlation pattern
o Confounder variables: contaminates the causal effect
add to regression as control variables to kill lurking variables
Week as control variable - week 1 is baseline
Simple running a regression gives you correlation: causation is difficult to get in practices
WEEK 3 | CONJOINT ANALYSIS
LECTURE
To understand preferences = holy grail – voting, consumption, social life
Product is combination of attributes & levels – e.g. decide product attributes of laptop
o Manager = chef, attributes = ingredients, conjoint analysis = recipe
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