2: Consumer and Consumer Analytics: Analyzing and Predicting preferences and Choice Flashcards
What does Utility consist of?
Utility composed of observed and unobserved attributes.
Observed attributes: Flavor, availability, Price
Unobserved attributes: Mood of consumer and decision maker, Personal context
Two forms of Brand and Product choices?
Brand and Product choice can be
- multinominal: Multinominal Logit Models, IIA property
- binary: Yes or no –>Logistic Regression
What is the Goal of Brand choice models?
goal is to predict the purchasing behavior and to model the process by which consumers make decisions
Why are linear models not appropriate to apply for Brand and Product choice Models?
Because Linear models assume that the disturbance term is normally distributed
disturbance term: is the (unobserved) value that are considered randomly
–>are not appropriate because they give linear continous values
However the observed output are binary (0 or 1, buy or not buy)
What does choice modelling estimate?
Choice modelling estimates the probabilitites (buying and not buying) and determines how they are affected by the observed attributes of consumers choice
What is the Multinominal Logit Model?
is a choice model where the consumer chooces between j alternatives
–>Here as well as in the binary case we assume that the alternative which yields highest utility is chosen
What is the Independence of Irrelevant Alternative (IIA) Property?
Multinominal Logit Models
Multinominal Logit Models suffer from the IIA, this property states
that the odds of choosing one alternative over another are constant regardless of whcih other alternatives are present
Why is the IIA property not realistic in marketing?
- In many marketing applications this is not realistic especially if some alternatives are close substitutes
- If similarities across alternatives are incorrectly assumed , the estimated effects of marketing variables are incorrect
How to deal with the Independence of Irrelevant Alternaitves
- perform statistical test of IIA e.g. attraction model
- use choice model that explicitly accounts for the fact that consumer choice behavior is affected by the composition of the choice set
–>Other models that eliminate the IIA assummptions are the Nested Multinominal Logit Model and the Multinominal Probit model
What do Markov Chain model?
model behavior over time and
consists of several observable (behavior, corresponding profits/losses) and unobservable (probabilities) factors
model how sequence of observations is related to transitioning among states
Markov Chain models define set of customer states based on…
The idea is to define the set of customer states based on:
- observed customer properties, e.g. the purchase of a product
- estimation of transition probabilities between the different states
- corresponding profits and losses
4 Probabilities of Markov Chain Models
- Transition probability: probability of going from one hidden state to another
- Emission probability: probability that observations are emitted from the hidden states ( prob observed outcome given particular state int he system
- Prior probability: basic likelihood for hidden state (prob that a random customer has a preferecne for brand A or B)
- Posterior probability: likelihood for hidden state given observation (prob that a customer prefers Brand A or brand B given that he buys brand A)
What is the goal of Markov Chain Models?
Capture….
Our goal is to capture dynamics in customer behavior over time, for
example, to see how firm interventions influence this behavior
How to infer prefercenes
(Markov Chain models)
Multiply every probabillity along the path (Viterbi Algorithm)
–>Machine learning system which identifies path with highest probability to be chosen
- finding most profitable preference combination
- If preference probabillities are close to each other advertising makes sense
For what do we use Logistic regression?
is used to predict binary or categorical outcomes (buy or not buy)
–>are binary choice models*