This document provides an overview and categorization of various marketing research techniques. It separates the techniques into mature techniques that have been used for some time, such as correlation analysis and regression analysis, and modern techniques that are newer, such as decision trees, dynamic programming, and technological forecasting. For several of the techniques, a brief explanation of the approach is given. The overall purpose is to familiarize management with the key research tools used by researchers.

1. Joseph F. Dash
&
Conrad Berenson

2.  Introduction
 Categorization of Techniques
 Brief Description or Understanding of the
Techniques

3.  An overview of marketing research tools
 Focus on pivotal research tools that the
researcher(s) should be accustomed with
 Enabling the management to have a better
understanding of what the researchers are
trying to convey

4.  Mature Techniques
 Correlation & Regression Analysis
 Discounted Cash Flow
 Incremental Analysis
 Multiple Regression &/or Multiple Correlation
 Random Sampling
 Sampling Theory

5.  Modern Techniques
 Bayesian Approach
 Cost- Benefit Analysis
 Critical Path Method (CPM)
 Decision Trees
 Dynamic Programming
 Exponential Smoothing
 Industrial Dynamics

6.  Modern Techniques (Contd.)
 Input- Output Analysis
 Linear Programming
 Markov Processes
 Monte Carlo Simulation
 Nonlinear Programming
 Numerical Taxonomy
 Program Evaluation Review Technique (PERT)

7.  Modern Techniques (Contd.)
 Queuing Models
 Risk Analysis
 Sensitivity Analysis
 Technological Forecasting

8.  Correlation & Regression Analysis
 Discounted Cash Flow
 Incremental Analysis
 Multiple Regression &/or Multiple Correlation
 Random Sampling
 Sampling Theory

9.  Correlation – It explains the degree to which the variation
in one variable (say X) affects the other variable (say Y).
The correlation coefficient (r) is : -1<r<1
 Regression – This is a procedure to draw a mathematical
relationship between an independent variable (X) and a
dependent variable (Y). The regression equation is:
Y=β0 +β1X
 Discounted Cash Flow – This analysis use future free cash
flow projections and discounts them to arrive at a present
value. If the value arrived at through DCF analysis is higher
than the current cost of the investment, the opportunity
may be a good one.

10.  Incremental Analysis – The analysis uses incremental cost
or expenses (∆C) incurred for particular activities as a
proportion to incremental benefit or revenues (∆ B)
derived thereof. If, (∆C)/ (∆ B) > 1, then the activity can be
given a go- ahead.
 Multiple Regression& Multiple Correlation - It is the
regression analysis between one dependent variable (Y)
and more than one independent variable (X). The
regression is denoted as: Y= β0+ β1X1+ β2X2+ …+ βkXk+ e
 Random Sampling - It is a probability sampling technique
where the chance of every element of the population being
selected in the sample is known or can be determined.

11.  Sampling Theory – It is the part of statistical practice which is
concerned with the selection of individual observations that
could yield some knowledge about a population of
concern, especially for the purposes of statistical inference.
 Bayesian Approach – Unlike other probability
concepts, Bayesian concept works on additional evidences of
any specific event occurring.
 Cost- Benefit analysis – The analysis finds out the aggregate
benefit derived form the aggregate cost incurred. It is generally
done in two steps:
 Total benefit derived from the total cost incurred
 The total opportunity cost incurred to derive the benefit

12.  Critical Path Method (CPM) – It is
methodology that finds out the sequence
of tasks to be performed to carry out a
project at the minimum time.
1
E1=0
4
E4=2
5
E5=6
3
E3=2
2
E2=2
6
E6=10
7
E7=15
8
E8=19
Activity Proceeding
Activities
Duration
1 – 2 NA 2
1 – 3 NA 2
1 – 4 NA 2
2 – 5 1 – 2 4
3 – 6 1 – 3 8
4 – 6 1 – 4 4
5 – 7 2 – 5 2
6 - 7 3 – 6, 4 - 6 5
7 - 8 5 – 7, 6 - 7 4

13.  Decision Trees - Decision Trees
are useful tools for helping to
choose between several courses
of action. They provide a highly
effective structure within which
one can explore options, and
investigate the possible
outcomes of choosing those
options. They also help to form a
balanced picture of the risks and
rewards associated with each
possible course of action.

14.  Dynamic Programming - It Is a method of solving
problems that exhibit the properties of overlapping
sub problems and optimal substructure . Optimal
substructure means that optimal solutions of sub
problems can be used to find the optimal solutions
of the overall problem. One can solve a problem
with optimal substructure using a three-step
process:
 Break the problem into smaller sub problems.
 Solve these problems optimally using this three-step
process recursively.
 Use these optimal solutions to construct an optimal
solution for the original problem.

15.  Exponential Smoothing- It is process of smoothing time
series data. Unlike Moving Averages, in this process the
weights starts decreasing exponentially as observations
get older.
 Industrial Dynamics – This is a simulation technique
where using different kinds of situations, the researcher
tries to understand the effects of decisions on the
performance of the structures that have taken such
decisions.
 Input- Output Analysis - Input-Output analysis creates a
picture of a regional economy describing flows to and from
industries and institutions

16.  Linear Programming- It is a linear constrained
optimization model. The technique specifies how to
use limited resources to meet a particular objective
such as maximization of profits or minimization of
costs, when the resources have alternative uses.
 Find the values of x and y that maximizes the sum x + y
subject to the constraint x>=0, y>=0 and:
x + 2y <= 4
4x + 2y <= 12
-x + y <= 1

17.  Markov Processes – Markov Properties states that given
the current state Xn, any other information about the past
is irrelevant for predicting the next state Xn+1.
 Monte Carlo Simulation - A problem solving technique
used to approximate the probability of certain outcomes by
running multiple trial runs, called simulations, using
random variables. The five steps for this techniques are:
Step 1: Create a parametric model, y = f(x1, x2, ..., xq)
Step 2: Generate a set of random inputs, xi1, xi2, ..., xiq
Step 3: Evaluate the model and store the results as yi
Step 4: Repeat steps 2 and 3 for i = 1 to n
Step 5: Analyze the results using histograms, summary statistics,
confidence intervals, etc

18.  Non- linear Programming - The area of applied
mathematics and operations research concerned with
finding the largest or smallest value of a function subject to
constraints or restrictions on the variables of the function.
Nonlinear programming is sometimes referred to as
nonlinear optimization. the nonlinear programming
problem may be expressed as below:
Minimize f(x) with respect to x,
Where gi (x) ≤ 0, i= 1, 2, …….,m
hj(x) = 0, j= 1, 2, …….,p

19.  Numerical Taxonomy - It aims to create a taxonomy using
numeric algorithms like cluster analysis
 Risk Analysis – It mainly uses the Monte Carlo process as
the variables that contribute to risk are to complex for
exact valuation and their impact on the risk vary over time
 Sensitivity Analysis - A technique used to determine
how different values of an independent variable will
impact a particular dependent variable under a given set of
assumptions. This technique is used
within specific boundaries that will depend on one or more
input variables, s

20.  Program Evaluation Review
Technique (PERT)- PERT is a method
to analyze the involved tasks in
completing a given project, especially
the time needed to complete each
task, and identifying the minimum
time needed to complete the total
project. It was able to incorporate
uncertainty by making it possible to
schedule a project while not knowing
precisely the details and durations of
all the activities.PERT network chart for a seven-
month project with five milestones
(10 through 50) and six activities (A
through F).

21.  Queuing Models - Queuing theory or waiting line theory is
primarily concerned with processes characterised by random
arrivals (i.e., arrivals at random time intervals); the servicing of
the customer is also a random process. There are costs
associated with waiting in line and there are costs of adding
more channels. These models can be used in the cost
computations to determine the number and capacity of service
facilities that are desirable so as to minimise the sum of the
costs of waiting and the costs of providing service facilities.
 Technological Forecasting – It is the methodology and practice
and practice of studying the future state of technology and the
extent of its use. It includes the efforts to project the
technological capabilities and to predict the state of
technological innovations.

22. Sunny Bose
IIMT