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.
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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.
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 futureobjective
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
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.
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 designs1. Simple random sampling2. Stratified random sampling3. Systematic random samplingB. Complex designs1. Cluster sampling2. Area sampling3. Multi-stage and sub-sampling4. Random sampling with probability proportional to
size5. Double sampling and multiphase sampling6. Replicated or interpenetrating sampling
1. Nature of population2. Simplicity in adoption3. Availability of frame4. Representativeness5. Nature of sampling unit6. Cost of enumeration7. Precision criterion
Probability or random sampling
A. Simple designs1. 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 designs1. 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
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 questions1. 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 sequence1. 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-endOpen-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 PeriodGeographic
Area
Salespersons
1. Total Sales2. Industry sales3. Company sales4. Product line sales5. Product variant sales6. Product item sales
1. Long range2. Medium range3. Short range
1. World2. Nation3. Region4. Territory5. Customer
Sales Forecasting
Sales forecasting is necessary for the other functions as follows:
1. Planning production2. Raising finance3. Purchase function4. Human resources Hence sales forecast is the
Qualitative methods1. Executive opinion method2. Delphi method-Rand corporation by 19403. 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 yearsNumber of years2. 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 seriesSales 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 methods2. Identify suitable method3. Obtain a range of forecasts4. Use computer hardware and
software.
Steps in sales forecastingAs per the conference board of America report 1978, 10 steps are listed.
1. Determine the Purpose for which Forecasts are used2. Divide the company products into homogenous
groups3. Determine the factors affecting the sales of each
product and their relative importance4. Choose the forecasting methods5. Gather the available data6. Analyse the data7. Check and recheck the deductions8. Make assumptions regarding other factors9. Convert deductions and assumptions into forecasts10. Apply the forecast to company operations
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 budgetSales 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.