part ii market response models

8/3/2019 Part II Market Response Models

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Part II.IPart II.I ± ± Market Response ModelsMarket Response Models

S.Venkat

AICAR Business School



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



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



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



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



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?



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



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.



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?



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



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



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



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



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.



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



Types of Response ModelsTypes of Response Models

To a craftsman with a

hammer the whole

world is a nail.




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)




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)




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




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



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




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




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)



X (Advertising)

Y

Sales

1

b ± slope of

sales line

a

when

X

= 0

Y = a + bX



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




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.




Input refers to the level of marketing effort

(X or independent variable) and output

refers to the result (Y or dependent variable)



P1 ± output is zero when input is zero . P1 through origin

Y

X



P2 ± the relationship between input and output is linear

Y

X



P3 ± Returns decrease as the scale of input increases(Every additional

input gives less output than the previous input (Concave)

Y

X



P4 ± the output cannot exceed some level (Q) indicating

saturation

X

Y



P6± returns first increase and then decrease as input

increases

X

Y



P7± Input must exceed some level before it produces

any output (threshold)

Y

X



Y

X

P8± Beyond some level of input output declines (super

saturation)

part ii market response models

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