part ii market response models
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Part II.IPart II.I ± ± Market Response ModelsMarket Response Models
S.Venkat
AICAR Business School
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1
Learning ObjectivesLearning Objectives Define and classify response models, the key
components of the modeling approach to decision
making Provide details of some of the types of response
models namely
Aggregate market response models to represent the
response of the market as a wholeIndividual response models (Can be added up to
represent the market)
Other response models ± shared experience and
qualitative response models
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Learning ObjectivesLearning Objectives Develop the criteria for calibrating and selecting
response models
Describe alternative ways to specify modelobjectives
Outline criteria for selecting response models
Understand Visual response modeling and Excel
Solver tool to find good values of parameters for response functions and to determine cost effectivemarketing strategies
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Why do you need responseWhy do you need response
models?models? The market is not a simple laboratory where
you can carefully observe processes to
understand them clearly and unambiguously
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Example I
Example I
You are the brand manager of Pepsi and an ad campaign
has been developed. You want to determine how effective
it is so as to take a decision to introduce the campaign intothe market. This campaign will have an immediate effect
on
Customer awareness
Attitudes
Brand preference
Sales
Immediately or in the future
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Example I
Example I
You focus only on current sales effects
This effect is bound to be influenced by
Current advertising campaigns of Coke and
Thums up or other
Prices and promotions of other brands as well
as your promotions
What can you control or vice versa?
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Example I
Example I
You can control trade promotions to retailers
You cannot control the price the retailer charges to
the consumer. Some may pass on trade discountsothers may not. A retailer may run his own promotion.
The campaign could have different appeals to
different age groups Different markets in the country have different
proportions of these groups
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Example I
Example I
Pepsi can be purchased from Bigbazaar,D Mart,through your local bania, supermarkets, hotels,
vending machines, McD
onalds or Pizza Hut. Eachchannel will produce a different level of sales
The response will differ according to packagesizes
The response will differ according to variants
Your campaign may complement or cannibalizeother brands like Mirinda.
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Example I
Example I
Very complicated eh? Well I can
complicate it further as I have restricted the
competitive variables to only Coke and
Thums up. What about Pesticide report?
And what if Sharukh was Dawoods man?
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Example I
Example I
What are the goals or non sale objectives to
be considered?
One thing is very clear ± that marketing
decisions take place in an environment that
is difficult to analyze or control
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Example I
Example I
The DM approach requires that the following
be made explicit
Inputs ± these are marketing actions that you
can control such as price, advertising,
selling effort I.e. the marketing mix. Non
controllable factors such as market size,competitive environment and the like
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Example I
Example I
Response model ± this is a linkage from
those inputs to the measurable outputs of
concern to the firm ± awareness,
perceptions, sales levels, profits etc
Objectives ± this is a measure for
monitoring and evaluating actions such assales in response to a promotion, the %age
of the audience that recalls the ad etc
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Marketing
actions (Inputs)
The four P¶s
Advertising
P design
Price
Selling effort
Competitive actions
Market
Response
Model
Environment
Observed
Market
outputs
Levels of
Awareness
Preference
Sales
objectives EvaluationControl
Adaptation
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The Decision model approachThe Decision model approach
Enables you to be more systematic about
structured decision situations
Let us see 2 approaches for a situation
Sales in the west zone are down 5% in
comparison to the forecast. We propose to
increase ad spend by 10% over the nextquarter.
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The Decision model approachThe Decision model approach
Assumption 1 ± the goal is to meet the forecast
and an increase of 10% in ad spend will increase
sales by 5% over the short term and this will also be cost effective
A DM assumption ± Sales are down 5%. After
incorporating this an recalibrating the model an ad
spend of 12.2% is recommended to maximize profits
Response models are usually mathematical
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Types of Response ModelsTypes of Response Models
To a craftsman with a
hammer the whole
world is a nail.
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Types of Response ModelsTypes of Response Models
A) How do you characterize responsemodels?
By the number of variables. Do youconsider the relationship betweenadvertising and sales alone (one variablemodel) or
Do you include price as well? ( twovariable model)
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Types of Response ModelsTypes of Response Models
B) Does the model include actions and reactions of
competition explicitly
C) The nature of relationship between inputs andoutputs. If sales is an output then does every rupee
of advertising produce the same effect on sales I.e.
a linear response or are there ranges of spending
wherein the additional Rupee gives larger or smaller returns. (S shaped response)
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Types of Response ModelsTypes of Response Models
D) Is your response model static or dynamic?Do
you want to analyze market responses over time or
simply to consider at one point in timeE) Does the model reflect individual response or
aggregate response? Individual response is used in
Direct Marketing. Aggregate response is the sum
of the responses of individuals
F) Are you trying to analyze brand sales OR market
share and total market demand
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Types of Response ModelsTypes of Response Models
We start with simple model types
Aggregate response to a single marketing
instrument in a static and non competitive
environment
Then
We start introducing additional marketing
instruments, dynamics and competition
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Vocabular y for response modelsVocabular y for response models
There are terms to denote equations or sets of
equations that relate dependent variables to
independent variables. These vocabulary are
relationship, specification and
mathematical form.
Parameters are the constants usually a¶s andb¶s. We must guess what these values are to
infuse life into an abstract model
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Vocabular y for response modelsVocabular y for response models
These constants have direct marketing
implications. (Eg. Market potential)
C alibration is the process of determining
approximate values for these parameters
which could be derived using statistical
methods, judgment or a combination of bothAn example
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Vocabular y for response modelsVocabular y for response models
A simple model is
Y = a + bX
X is an independent variable (Advertising)Y is the dependent variable (sales)
a and b are parameters or constants
a is the level of sales when X = 0 or the base sales level
For every rupee increase in advertising the expected changein sales is b units. b is the slope of sales/advertising
If a = 23,000 and b=4 then
Y = 23,000 + 4X. (23000 and 4 are calibrations)
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X (Advertising)
Y
Sales
1
b ± slope of
sales line
a
when
X
= 0
Y = a + bX
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Simple market response modelsSimple market response models
The response models need not be complex
Even simple disciplined analysis to marketing
problems can yield great benefits as compared torelying on mental models
Using complex models is not necessarily better
Complexity may hinder understanding and usingthem
Start with simple tools and then add complexity
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Simple market response modelsSimple market response models
Widely used models of market responserelate one dependent variable to one
independent variable in the absence of competition. While a linear model asshown before is used frequently it is farfrom consistent with the ways the market
appears to behave. The simplephenomena are summarized in the nextslide.
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Simple market response modelsSimple market response models
Input refers to the level of marketing effort
(X or independent variable) and output
refers to the result (Y or dependent variable)
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P1 ± output is zero when input is zero . P1 through origin
Y
X
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P2 ± the relationship between input and output is linear
Y
X
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P3 ± Returns decrease as the scale of input increases(Every additional
input gives less output than the previous input (Concave)
Y
X
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P4 ± the output cannot exceed some level (Q) indicating
saturation
X
Y
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P6± returns first increase and then decrease as input
increases
X
Y
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P7± Input must exceed some level before it produces
any output (threshold)
Y
X
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Y
X
P8± Beyond some level of input output declines (super
saturation)