Please enable JavaScript.
Coggle requires JavaScript to display documents.
Week 3: Product management I - Coggle Diagram
Week 3: Product management I
goals
: predicting consumer's choices and market shares of existing products
Concept of attraction or utility
total
benefits/ satisfaction
customers derived from the products
increasing attraction => increases
purchase probability
and hence the market share
market share prediction
measure
brand attractions
=> estimate
purchase probabilities
=> predict
market shares
Measure brand attractions
Multi- attributable framework (from survey data) - page 6
Evaluate a product as a bundles of attributes
Different evaluations to different attributes
Form beliefs about
relative strengths/ attractiveness
of different products
attributes importance
(brand name, price, ...) should sum to 1 or 100% (obtained using
survey
)
example: p.8
attraction
= weighted sum attribute strengths of that brand across all attributes
Conjoint analysis
Estimate purchase probabilities
when
predicting market shares from purchase probabilities
, we have
assumed that
all consumers/ segments
buy the same quantity
equal size segments (example: p.10)
unequal sizes segments
(example: p.11)
estimated in several ways
Share of attraction: brand's purchase probability = share of total attraction (p.14),
low involvement, frequently purchased products
Maximum attraction: consumers choose the brand with the
highest attraction
(p.13), suitable for
high involvement, infrequent purchases
3. Multinomial Logit Model
1 and 2 are
simplistic approaches
=> low predictive power
Multinomial Logit Model
Logit model assumes that choice probabilities as well as utilities are
stochastic
(have a
random component
)
example: p.16
probability of choosing a product does not increase linearly with utilities, the relationship is
S shape
3 properties (p.17)
2.
Invariant property
(p.18): only
differences in attractions
affect choices,
adding/ subtracting a constant
to all attractions does not change purchase probabilities
Probabilities are never 0
S shape relationship
between purchase probabilities and attraction
predicting market share
: example p.20
Limitations (p.21)
Table
p.22
Logit choice model
similar to
linear regression model
uses
discrete variable
(for
dependent variable
) while linear regression model uses continuous variable
formula: p.24
differ from
predicting choices
(p.25)
choice model
illustration
(p.26)
predicting a
binary outcome
(p.28)
known the purchase probabilities but don't know the utility