MKTG TRENDS & RESEARCH

MODULE 2: MARKETING
TRENDS
1. Scanning the environment,
2. Marketing intelligence and
information system,
3. Market research system,
4. Demand measurement and
forecasting,
5. Data warehousing,
6. Data mining,
7. Changing consumption
pattern of global consumer
and Indian consumer.

Marketing
Plan
􀂙 What is a marketing plan ?
- A marketing plan is a written document that
summarizes what the marketer has learned
about the marketplace and indicates how the
firm plans to reach its marketing objectives.
- It is the act of putting together in a plan, the
various elements of the marketing mix in a
forecasted / futuristic approach
�

􀂙 Components of MIS :
- Internal company records
- Marketing intelligence system
- Marketing research
􀂙 MIS provides information on:
- Buyer wants, preferences and behaviour
- Consumption patterns across geographic areas
Thus the companies can :
- Choose its markets better, develop better offerings,
and execute better planning

Marketing information system
A marketing information system consists
of people, equipment , procedures to
gather, sort, analyse, evaluate &
distribute needed, timely and accurate
information to marketing decision
makers.

Research means –
1. A systematic search for facts
2. Answers to questions and solutions to
problems

 1. What is marketing research ?
 The systematic gathering, recording
and analysing of data about problems
related to the marketing of goods and
services

2. Purpose of research :
- Decision making tool
- facilitation
- Risk reduction
- Discovering profitable opportunities
3. Marketing research acts as a marketing tool in the following
areas of sales and marketing.
- Study of consumer behaviour
- Product design
- Designing distribution channels
- Advertising design – through consumer perception
- Social marketing

4. Six Steps in marketing research
- Problem definition
- Research design/plan
- Field work/Collect information
- Data Analysis
- Report presentation
- Make the decision
Problem definition –
- Define the issue on hand
- Give a correct overview of current scenario and future
objective

1. Formulating research problem
 One should understand the problem
thoroughly and subsequently rephrase the
same into meaningful terms.
 The statement is important in a research
because it determines the method of
research, data to be collected, relations to
be explored, techniques to be employed
and the form of final report.

2. Review of Literature
 Reading books literature, earlier
thesis, journals, periodicals etc to find
out if any work has already been
carried out
 If a similar work has been carried out
the ‘research gap’ has to be found out
and try to fill the gap.

5. Determining the sample design
 Random or probability sampling and
non-random or non-probability
sampling
 The units drawn from the universe or
population to form a sample is called
sampling.

6. Collecting data
 Generally the data in hand will not be
sufficient and hence additional data needs
to be collected for research.
 Primary data can be collected by
observation, personal interview,
questionnaires, schedules, video
conferencing, etc
 Secondary data can be gathered from
published materials, articles, survey reports,
journals, internet, etc.

Formulation of a research
problem is far more
essential than its solution
Albert Einstein 1938

 Formulating research problem:
Select the topic and formulate the
research problem.
 Research starts with a problem and
the problem statement is the axis,
around which the whole research
revolves.
 Problem formulation is the anchor of a
research problem

 The choice of a good research problem
depends on the intuition, knowledge
and expertise of the researcher.
 Problem and purpose are different.
 If there is no clear problem formulation,
the purpose and methods are
meaningless

Research Design
 After the formulation of the problem next task
is to build up a Research design to streamline
the research
 It determines ‘what and ‘how’ the researcher
hopes to find the best solution to he problem.
 Research design is about organising research
activity, including collection of data in ways
that are most likely to achieve the resarch
goals and objectives.

Sampling
 A sample as the name implies is
smaller representative of a larger whole.
 The method of selecting a portion of the
universe for the study is known as
sampling.
 It helps to draw conclusions about the
said universe

 The entire group from which a sample is
chosen is known as the population or
universe
 Census: A complete enumeration of all
items in the population is known as
census enquiry
 Sampling frame: It is a list of items from
which the sample is to be drawn.

 Census is appropriate when the
population size is small.
 Also when the information is needed from
each and every individual object suct as
population census, industrial census, etc.
 Sampling is the best course to adopt if
the population size is large and if both the
cost and time associated is limited.
 Besides in destructive tests, sample can
only be considered.

Survey
 A survey is a planned observation of
objects that are not controlled by the
observer.
 These objects are not themselves
treated but the ‘Nature’ is assumed to
have applied the treatments and all that
analysts can do it to observe the
consequences.

 A survey is a process by which certain
quantitative/qualitative facts pertaining to
certain field of enquiry are collected to throw
light on the objectives of a research problem.
 A descriptive surveys are fact finding surveys
 An analytical surveys deal with interrelations
among different variables of interest and their
interaction

 A Survey of complete enumeration of
population of interest is called Census.
 A Survey based on a subset of the
population which is also called as a
sample is termed as sample survey.

Sampling or Sampling techniques
 A sample as the name implies is
smaller representative of a larger whole.
 The method of selecting a portion of the
universe for the study is known as
sampling.
 It helps to draw conclusions about the
said universe

Sampling methods or Sampling techniques
Sampling Designs:
 Two generic types:
1. Probability or random sampling, and
2. Non-probability or Non-random
sampling

Probability or random sampling
A. Simple designs
1. Simple random sampling
2. Stratified random sampling
3. Systematic random sampling
B. Complex designs
1. Cluster sampling
2. Area sampling
3. Multi-stage and sub-sampling
4. Random sampling with probability proportional to
size
5. Double sampling and multiphase sampling
6. Replicated or interpenetrating sampling

Non-probability or Non-random sampling
A. Simple designs
 Convenience or accidental sampling
 Purposive (or Judgement ) sampling
B. Complex designs
1. Quota sampling
2. Snow-ball sampling

 Reasons for choosing different
sampling designs.
1. Nature of population
2. Simplicity in adoption
3. Availability of frame
4. Representativeness
5. Nature of sampling unit
6. Cost of enumeration
7. Precision criterion

Probability or random sampling
A. Simple designs
1. Simple random sampling
 Simple random sampling is the simplest of
all sampling designs
 Each and every item in the population has
an equal and independent chance of
inclusion
 This can be done for a homogenous
population.
 However for heterogeneous population a
simple random sampling may not give the
desired results.

2. Stratified random sampling
 This is used for a heterogeneous
population.
 Here the population is stratified (Grouped)
into a number of overlapping sub-
populations or strata and sample items are
selected from each stratum.
 Ex: In survey of business establishments,
one may form large, medium and small
establishments.
 Further the sample selection from each
strata is based on simple random selection.

3. Systematic random sampling
 Only the first unit is selected randomly and the
remaining units of the sample are selected at fixed
intervals.
 Ex: To choose every 10th
name or 15th
item and so
on
 In this method the entire list of the universe is given
numbers
 It is easier and less expensive
 It is spread more evenly over the entire population
 The main disadvantage is if there is a hidden
periodicity in the population, this may prove
inefficient.

B. Complex designs
1. Cluster sampling :
 This involves grouping of population and
then selecting the groups or clusters rather
than individual elements for inclusion in the
sample.
 That is the total population is divided into a
number of relatively small subdivisions
which are themselves clusters of smaller
units.
 Further some of these clusters are
randomly selected for inclusion in the
overall selection

2. Area sampling
 Cluster sampling in the form of grids
imposed on maps in certain forms are
is termed as Area sampling.
 It will not be grouped by type of
establishments like villages,
industries, hospitals etc but based on
areas.
 Ex: National population or well defined
political or natural boundaries.

Non-probability sampling
 This sampling does not provide a chance of
selection to each population
 The selection probability is known
 A non-probability sample may not be true
representative
 Population parameters cannot be estimated
from the sample values
 It suffers from sampling bias which suffers
from bias.
 Hence generally not advisable

 When there is no other feasible method
for collection of data or non-availability
of population for collection of data.
 When study does not need
generalisation of conditions
 When cost is a consideration
 When probability sampling needs more
time.

Non-probability or Non-random
sampling
A. Simple designs
1. Convenience or accidental sampling
2. Judgment sampling
B. Complex designs
1. Quota sampling

Non-probability or Non-random
sampling
A. Simple designs
1. Convenience or accidental sampling:
 This method is employed to get
information quickly and inexpensively
 Depends on the convenience of the
researcher
 Keeps in view of the general
population

3.Judgment sampling:
 Judgment sampling is very appropriate when
it is necessary to reach small and specialized
populations.
 The researcher uses judgment to identify
representative samples
 A judgmental sampling is likely to be more
reliable and representative than a probability
sample.
 However unwelcome bias might creep into
results if not honestly judged.

Complex designs
1. Quota sampling:
 We observe the responding units non-
randomly according to some fixed
quota
 It is to assure that the smaller groups
are adequately represented
 Bias can exist

 First someone is identified who meets
the criteria and further asked to
include others.
 Useful where representatives are
inaccessible or hard to find
 Inherent problem is one who is
socially visible are likely to be
selected.

Data Collection
 Data are facts, figures and other
relevant materials, past and present
serving as basis for study and
analysis.
 Types of sources of data
1. Primary data
2. Secondary Data

1. Primary data are those which are
collected afresh and for the first time
and thus happens to be original in
character
2. Secondary data are those which have
already been collected by someone
else and which have salready been
passed through statistical process.

Primary data
1. Primary data Primary data are those
which are collected afresh, for the first
time and thus happens to be original
in character.
2. First formal appearance of results in
the print or electronic literature.

Secondary data
1. Secondary data are those which have
already been collected by someone
else and which have already been
passed through statistical process.
2. Secondary sources are works that
describe, interpret, analyse primary
data
3. Comments and discussion of the
evidence provided by primary sources

Methods of collecting Primary data.
 In many cases the secondary data are
inappropriate, inadequate or obsolete,
primary data have to be gathered.
 Primary data are directly collected by
the researcher from their original
source
 Method is different from a tool
 One or more methods can be chosen
 No method is universal but has its own
uniqueness

1. Observation
2. Interviewing
3. Mail survey
4. Experimentation
5. Simulation
6. Projective technique

 Observation:
 Observation is defined as a systematic
viewing of a specific phenomenon in its
proper setting for the specific purpose
of gathering data for a particular study.
 Observation includes both seeing and
hearing.
 The main body of knowledge has been
developed by observing the nature

Observation
ParticipantParticipant
observationobservation
Non- participantNon- participant
DirectDirect
IndirectIndirect
ControlledControlled
Un-controlledUn-controlled
Researcher’s
Role
Mode of
Observation
System
Adopted

Interviewing
 One of the prominent method of data collection
 People are generally more willing to talk than to write
 It is two way systematic conversation between an
investigator and an informant initiated for obtaining
information relevant to a specific study.
 It is not only conversation, but also learning from the
respondent's gestures, expressions, pauses and
environment
 It is carried out in a structured schedule
 It calls for interviewing skills

 Interviewing can be used as a main
method or a supplementary method
 It is the only method for gathering
information from illiterate and
uneducated method.
 It can be used for collecting personal
and intimate information relating to a
person’s opinions, attitudes, values,
future intentions etc.

Questionnaire
 A questionnaire is a series of questions asked to
individuals to obtain statistically useful information
about a given topic.
 When properly constructed and responsibly
administered, questionnaires become a vital
instrument
 Questionnaires are frequently used in quantitative
research.
 They are a valuable method of collecting a wide
range of information from a large number of
individuals, often referred to as respondents. Good
questionnaire construction is critical to the
success of a survey.

 Types of questions
1. Contingency questions - A question that is
answered only if the respondent gives a particular
response to a previous question. This avoids asking
questions of people that do not apply to them
2. Matrix questions - Identical response categories are
assigned to multiple questions.
3. Closed ended questions - Respondents’ answers
are limited to a fixed set of responses. Most scales are
closed ended. Other types of closed ended questions
include:
1. Yes/no questions - The respondent answers with a “yes” or a
“no”.
2. Multiple choice - The respondent has several option from
which to choose.
3. Scaled questions - Responses are graded on a continuum
(example : rate the appearance of the product on a scale from 1
to 10, with 10 being the most preferred appearance). Examples
of types of scales include the Likert scale, semantic differential
scale, etc

 Open ended questions - No options or predefined
categories are suggested. The respondent supplies their own
answer without being constrained by a fixed set of possible
responses. Examples of types of open ended questions
include:
1. Completely unstructured - For example, “What is your
opinion of questionnaires?”
2. Word association - Words are presented and the
respondent mentions the first word that comes to mind.
3. Sentence completion - Respondents complete an
incomplete sentence. For example, “The most important
consideration in my decision to buy a new house is . . .”
4. Story completion - Respondents complete an incomplete
story.
5. Picture completion - Respondents fill in an empty
conversation.
6. Thematic apperception test - Respondents explain a
picture or make up a story about what they think is happening
in the picture

 Question sequence
1. Questions should flow logically from one to the next.
2. The researcher must ensure that the answer to a
question is not influenced by previous questions.
3. Questions should flow from the more general to the
more specific.
4. Questions should flow from the least sensitive to the
most sensitive.
5. Questions should flow from factual and behavioral
questions to attitudinal and opinion questions.
6. Questions should flow from unaided to aided questions.
7. The sandwich theory - three stage theory : Initial
questions should be screening and rapport questions.
Then in the second stage you ask all the product
specific questions. In the last stage you ask
demographic questions

 Research Design
 - Data collection
 Observational research
 Ethnographic group Research
 Focus group Research
 Survey research
 Behavioral data
 Experimental research( cause & effect
relationships)

 - Research instrument
 Questionnaires:
Close-end
Open-end
 Mechanical instruments: like,
 Galvanometers-emotions
 Tachistoscopes flashes
 Eye cameras
 Audiometer-TV
 - Sampling plan

 Field work
 - Planning and supervision
 Data Analysis
 - Classifying raw data
 - Summarising data
 - Analytical methods to analyse and
then make an inference

 Application of research :
 - Sales and market analysis
 - Product research
 - Corporate research
 - Advertising research

Barriers to the use of MR
 A narrow conception of Marketing
Research
 Uneven caliber of Marketing
researchers
 Poor framing of the problem
 Late and erroneous findings by
marketing research
 Personality and presentational
differences.

 Forecasting and
Demand
measurement

Sales Forecasting
 Forecasting is systematic attempt to
predict the future by inference from the
known facts.
 Sales forecasting is an attempt to
determine the value of sales which can be
reasonably be expected at some future
date on a scientific basis.

Types of sales forecastTypes of sales forecast
Product
Level
Time Period
Geographic
Area
Salespersons
1. Total Sales
2. Industry sales
3. Company sales
4. Product line sales
5. Product variant sales
6. Product item sales
1. Long range
2. Medium range
3. Short range
1. World
2. Nation
3. Region
4. Territory
5. Customer

Sales Forecasting
 Sales forecasting is necessary for the
other functions as follows:
1. Planning production
2. Raising finance
3. Purchase function
4. Human resources
 Hence sales forecast is the forerunner
for all other to all planning

Terms used in sales forecast
1. Market potential
2. Market forecast
3. Sales potential
4. Sales forecast
5. Sales budget
6. Sales quota

Methods of sales forecasting
 Qualitative methods
1. Executive opinion method
2. Delphi method-Rand corporation by 1940
3. Sales force composite method
4. Test marketing method: full blown test market,
controlled test marketing, simulated test
marketing

Quantitative methods
1. Moving average method:
Actual sales for past 3 or 6 years
Number of years
2. Exponential smoothing method:
Sales forecast for the next year=Actual sales
this year x (L) + (1-L) x (this years sales
forecast)
L- smoothing constant or probability weighing
factor 0.8 – 0.2
Quantitative Method

3. Decomposition method:
The company’s previous periods sales
data is broken into four major
components Trend, cycle, seasonal
and erratic
4. Naive/Ratio method: Time series
Sales forecast for next year=
Actual sales of this year x Actual sales of this year
Actual sales of last year

6. Regression analysis: Company sale is
dependent on many factors such as price,
promotional expenditure, population etc.
Statistical forecasting - SPSS used- Multiple
regression analysis is used
7. Econometric analysis : Many regression
equations are built to forecast industry sales. A
forecast is prepared by solving these
equations on computer software.

To improve forecasting accuracy:
1. Use multiple forecasting methods
2. Identify suitable method
3. Obtain a range of forecasts
4. Use computer hardware and software.

Steps in sales forecasting
As per the conference board of America report 1978, 10 steps are listed.
1. Determine the Purpose for which Forecasts are
used
2. Divide the company products into homogenous
groups
3. Determine the factors affecting the sales of each
product and their relative importance
4. Choose the forecasting methods
5. Gather the available data
6. Analyse the data
7. Check and recheck the deductions
8. Make assumptions regarding other factors
9. Convert deductions and assumptions into forecasts

Sales Budget
 A sales budget consists of
estimates of expected volume
of sales and selling expenses.
 Sales budget is generally fixed slightly lower than
the sales forecast to avoid risk
 Selling expense budget consists of the selling
expense budget and sales department
administrative budget
 The sales budget is the key factor for the
successful performance of the sales department

Sales Budget
Sales volume budget
Sales department
Administrative budget
Selling expense budget

Purposes of
the sales budget
1. Planning: From total
corporate plan
marketing and sales
budgets are
developed
considering sales
goals, sales strategy,
action plan, expense,
etc.
2. Coordination:
Coordinating among
various functions
3. Control : Evaluation of
performance

Methods used for deciding sales
expenditure budget
 Sales managers are
required to decide
expenditure levels for
each item of selling
expenses.
1. Percentage of sales method
2. Executive judgment method
3. Objective and task method

Review Situation
Communication
Subordinate budgets
Approval of budget
Other departments
Sales Budget
Process

MKTG TRENDS & RESEARCH

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