Marketing Analytics Flashcards
Why segment the market?
Value may be more significant to the consumer; better consumer experience; more cost effective
What is mass marketing?
Offer 1 thing to the whole mass
What is segment-based marketing?
Offer different offerings for each segment - ex. Group A vs. Group B
What is one-to-one marketing?
Offer different offerings to each customer - ex. A -> Customer 1, B -> Customer 2, etc.
What are the 4 bases to segment consumer markets?
- Demographic: age, gender, income, ethnicity, etc.
- Geographical: world region, country, city vs. rural, etc.
- Behavioral: usage, loyalty, etc.
- Psychographic: lifestyles, beliefs, attitudes, interests, personality, values, etc.
What is the OCEAN model for segmenting markets?
O = openness - do they enjoy new experiences?
C = conscientiousness - do they prefer plans and order?
E = extraversion - do they like spending time with others?
A = agreeableness - do they put other people’s needs before theirs?
N = neuroticism - do they tend to worry a lot?
How would you calculate the distance between respondents? (3 steps)
- Decide on the clustering variables & importance of each of the characteristics
- Select a measure of (dis)similarity using the Euclidean distance
- Select a clustering method like hierarchical clustering
What is the Euclidean Distance formula?
Euclidean distance (De) of (x,y) = √ (x2 – x1)^2 + √ (y2 – y1)^2
You need a data matrix to be able to compute the distance matrix (distance matrix is the values from Euclidean distance formula). This would be used if you wanted to calculate the distance between consumer perspectives (for example, importance of innovation vs. constant communication)
What are the 3 key criteria for actionable segmentation?
- Distinctive: customers within a segment are similar but differ from customers in other segments
- Substantial: sufficient large to create value
- Accessible: ability to reach customers within segments
What does the intercept in a linear regression stand for?
It is the value of the dependent variable when both independent variables are 0. Ex. it’s the value of sales when radio and tv advertising is 0.
What is the p-value stand for?
It is the measure of statistical significance, so if the p-value is below 0.05 then the pattern or relation we find is statistically significant.
What is the adjusted r-squared show?
It shows the variation in intercept vs. independent variables. Ex. sales vs. radio and tv advertising.
This is a measure of the goodness of fit of the model therefore the higher it is, the better you can explain your marketing mix.
What is an elasticity?
It’s the % change in response variable for a 1% change in the predictor variable
Ex. the % change in sales for a 1% change in advertising spending.
How do you use the ratio of elasticities method? (3 steps)
- Sum elasticities
- Compute ratio of elasticities
- Multiply ratio of elasticities by total budget to spend & make recommendation
How do you compute elasticities from a linear regression model?
Advertising elasticity = advertising estimate (from linear regression) * (baseline advertising / baseline sales)
What is underfitting?
low R-squared and low predictive accuracy
What is overfitting?
high R-squared and low predictive accuracy
If a model has a high R-squared and a low predictive accuracy, the model may be tailored to the specific data but fails to generalise outside the sample
What happens when a firm always spends the same amount on facebook campaigns and instagram campaigns in the same weeks?
Both variables will be perfectly correlated (ie. 1) and the model will not be able to distinguish between the impact of the two perfectly correlated variables
So, even if predictors are highly correlated (ie. higher than .80), the model can suffer from ‘multicollinearity’ which reduces the accuracy of the estimates because the observed effect might be overstated
How do you deal with multicollinearity?
- Calculate correlations between predictor variables
- If the correlations are high, you can reformulate the variables where possible
Ex. you can sum advertising spendings across channels (ie. online vs. offline)
Omitted variable bias exists if two conditions are met:
- an omitted variable affects your response variable
AND - the omitted variable correlates with one or more predictor variables
What do we know about carryover effects? When can they happen?
In reality, consumer response to advertising can be delayed, therefore not accounting for carryover effects can cause advertising elasticities to be under-valued.
How do you measure the effect of x (ex. advertising) beyond the current time period?
Adstock equation
Ex. Adstock(t) = coefficient(t) + lambda *Adstock(t-1)
For example, if lambda = 0.3, then adstock from one time period ago still had a 30% effect in the current time period
What is synergy effects?
The combined used of marketing mix instruments
Why use predictive analytics in marketing?
To leverage historical data to identify the likelihood of future outcomes.
It also helps us predict how consumers will behave and then we can predict what/how to change the marketing strategy for them.