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Market survey- basics. example

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MARKET AND DEMAND ANALYSIS:

MARKET AND DEMAND ANALYSIS 1

PowerPoint Presentation:

Situational Analysis and Specifications of Objectives Collection of Secondary Information Conduct of Market Survey Characterization of the Market Demand Forecasting Market Planning 2

SITUATIONAL ANALYSIS AND SPECIFICATIONS OF OBJECTIVES:

SITUATIONAL ANALYSIS AND SPECIFICATIONS OF OBJECTIVES 3

COLLECTION OF SECONDARY INFORMATION:

COLLECTION OF SECONDARY INFORMATION General Sources of Secondary Information Industry Specific Sources of Secondary Information Evaluation of Secondary Information 4

SECONDARY SOURCES OF DATA:

SECONDARY SOURCES OF DATA Indian Economic Survey Indian Basic Facts Reports of Export Working Groups on Various Industries Census of Manufacturing Industries Indian Statistical Yearbook Monthly Statistical Bulletin Annual Report of RBI Annual Reports and Accounts of the Companies Listed on the Stock Exchange Annual Reports of the Various Associations of Manufacturers 5

CONDUCT OF MARKET SURVEY:

CONDUCT OF MARKET SURVEY Census Survey Sample Survey Steps in a Sample Survey Define the Target Population Select the Sampling Scheme and Sample Size Develop the Questionnaire Recruit and Train the Field Investigators Obtain Information as Per the Questionnaire from the Sample of Respondents Scrutinizes the Information Gathered Analyze and interpret the Information 6

CONDUCT OF MARKET SURVEY:

CONDUCT OF MARKET SURVEY Some Problems Heterogeneity of the Country Multiplicity of the Languages Design of Questionnaire 7

CHARACTERISATION OF THE MARKET:

CHARACTERISATION OF THE MARKET Effective Demand in the Past and Present Production + Imports – Exports – Change in stock level Breakdown of Demand Nature of Product Consumer Groups Geographical Division 8

CHARACTERISATION OF THE MARKET:

CHARACTERISATION OF THE MARKET Price Methods of Distribution and Sales Promotion Consumers Supply and Competition Government Policy 9

Forecasting:

Forecasting Predicting the future Qualitative forecast methods subjective Quantitative forecast methods based on mathematical formulas 12- 10 10

Types of Forecasting Methods:

Types of Forecasting Methods Depend on time frame demand behavior causes of behavior 12- 11 11

Time Frame:

Time Frame Indicates how far into the future is forecast Short- to mid-range forecast typically encompasses the immediate future daily up to two years Long-range forecast usually encompasses a period of time longer than two years 12- 12 12

Demand Behavior:

Demand Behavior Trend a gradual, long-term up or down movement of demand Random variations movements in demand that do not follow a pattern Cycle an up-and-down repetitive movement in demand Seasonal pattern an up-and-down repetitive movement in demand occurring periodically 12- 13 13

PowerPoint Presentation:

Analytical Cause effect relationship basis Quantitative Explicit Causes of Behavior 14

DEMAND FORECASTING:

DEMAND FORECASTING Qualitative Methods These methods rely essentially on the judgment of experts to translate qualitative information into quantitative estimates Used to generate forecasts if historical data are not available (e.g., introduction of new product) The important qualitative methods are: Jury of Executive Method Delphi Method 15

JURY OF EXECUTIVE OPINION METHOD:

JURY OF EXECUTIVE OPINION METHOD Rationale Upper-level management has best information on latest product developments and future product launches Approach Small group of upper-level managers collectively develop forecasts – Opinion of Group Main advantages Combine knowledge and expertise from various functional areas People who have best information on future developments generate the forecasts 16

JURY OF EXECUTIVE OPINION METHOD:

JURY OF EXECUTIVE OPINION METHOD Main drawbacks Expensive No individual responsibility for forecast quality Risk that few people dominate the group Subjective Reliability is questionable Typical applications Short-term and medium-term demand forecasting 17

DELPHI METHOD:

DELPHI METHOD Rationale Eliciting the opinions of a group of experts with the help of mail survey Anonymous written responses encourage honesty and avoid that a group of experts are dominated by only a few members 18

DELPHI METHOD:

DELPHI METHOD Approach Coordinator Sends Initial Questionnaire Each expert writes response (anonymous) Coordinator performs analysis Coordinator sends updated questionnaire Coordinator summarizes forecast Consensus reached? Yes No 19

DELPHI METHOD:

DELPHI METHOD Main advantages Generate consensus Can forecast long-term trend without availability of historical data Main drawbacks Slow process Experts are not accountable for their responses Little evidence that reliable long-term forecasts can be generated with Delphi or other methods 20

DELPHI METHOD:

DELPHI METHOD Typical application Long-term forecasting Technology forecasting 21

TIME SERIES PROJECTION METHODS:

TIME SERIES PROJECTION METHODS These methods generate forecasts on the basis of an analysis of the historical time series. Assume that what has occurred in the past will continue to occur in the future Relate the forecast to only one factor - time The important time series projection methods are: Trend Projection Method Exponential Smoothing Method Moving Average Method 22

Linear Trend Line:

Linear Trend Line 12- 23 y = a + bx where a = intercept of the relationship b = slope of the line x = time period y = forecast for demand for period x b = a = y - b x where n = number of periods x = = mean of the x values y = = mean of the y values  xy - nxy  x 2 - nx 2  x n  y n 23

Least Squares Example:

Least Squares Example 12- 24 x (PERIOD) y (DEMAND) xy x 2 1 73 73 1 2 40 80 4 3 41 123 9 4 37 148 16 5 45 225 25 6 50 300 36 7 43 301 49 8 47 376 64 9 56 504 81 10 52 520 100 11 55 605 121 12 54 648 144 78 557 3867 650 24

Least Squares Example (cont.):

Least Squares Example (cont.) 12- 25 x = = 6.5 y = = 46.42 b = = =1.72 a = y - bx = 46.42 - (1.72)(6.5) = 35.2 3867 - (12)(6.5)(46.42) 650 - 12(6.5) 2  xy - nxy  x 2 - nx 2 78 12 557 12 25

PowerPoint Presentation:

12- 26 Linear trend line y = 35.2 + 1.72 x Forecast for period 13 y = 35.2 + 1.72(13) = 57.56 units 70 – 60 – 50 – 40 – 30 – 20 – 10 – 0 – | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 Actual Demand Period Linear trend line 26

PowerPoint Presentation:

Advantages It uses all observations The straight line is derived by statistical procedure A measure of goodness fit is available Disadvantages More complicated The results are valid only when certain conditions are satisfied Trend Projection Method 27

Exponential Smoothing:

Exponential Smoothing 12- 28 Averaging method Weights most recent data more strongly Reacts more to recent changes Widely used, accurate method 28

Exponential Smoothing (cont.):

Exponential Smoothing (cont.) 12- 29 F t +1 =  D t + (1 -  ) F t where: F t +1 = forecast for next period D t = actual demand for present period F t = previously determined forecast for present period  = weighting factor, smoothing constant 29

Effect of Smoothing Constant:

Effect of Smoothing Constant 12- 30 0.0    1.0 If  = 0.20, then F t +1 = 0.20  D t + 0.80 F t If  = 0, then F t +1 = 0  D t + 1 F t = F t Forecast does not reflect recent data If  = 1, then F t +1 = 1  D t + 0 F t =  D t Forecast based only on most recent data 30

Exponential Smoothing (α=0.30):

Exponential Smoothing ( α =0.30) 12- 31 F 2 =  D 1 + (1 -  ) F 1 = (0.30)(37) + (0.70)(37) = 37 F 3 =  D 2 + (1 -  ) F 2 = (0.30)(40) + (0.70)(37) = 37.9 F 13 =  D 12 + (1 -  ) F 12 = (0.30)(54) + (0.70)(50.84) = 51.79 PERIOD MONTH DEMAND 1 Jan 37 2 Feb 40 3 Mar 41 4 Apr 37 5 May 45 6 Jun 50 7 Jul 43 8 Aug 47 9 Sep 56 10 Oct 52 11 Nov 55 12 Dec 54 31

Exponential Smoothing (cont.):

Exponential Smoothing (cont.) 12- 32 FORECAST, F t + 1 PERIOD MONTH DEMAND (  = 0.3) (  = 0.5) 1 Jan 37 – – 2 Feb 40 37.00 37.00 3 Mar 41 37.90 38.50 4 Apr 37 38.83 39.75 5 May 45 38.28 38.37 6 Jun 50 40.29 41.68 7 Jul 43 43.20 45.84 8 Aug 47 43.14 44.42 9 Sep 56 44.30 45.71 10 Oct 52 47.81 50.85 11 Nov 55 49.06 51.42 12 Dec 54 50.84 53.21 13 Jan – 51.79 53.61 32

Exponential Smoothing (cont.):

Exponential Smoothing (cont.) 12- 33 70 – 60 – 50 – 40 – 30 – 20 – 10 – 0 – | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 Actual Orders Month  = 0.50  = 0.30 33

Moving Average:

Moving Average Naive forecast demand in current period is used as next period’s forecast Simple moving average uses average demand for a fixed sequence of periods stable demand with no pronounced behavioral patterns Weighted moving average weights are assigned to most recent data 12- 34 34

Moving Average: Naïve Approach:

Moving Average: Naïve Approach 12- 35 Jan 120 Feb 90 Mar 100 Apr 75 May 110 June 50 July 75 Aug 130 Sept 110 Oct 90 ORDERS MONTH PER MONTH - 120 90 100 75 110 50 75 130 110 90 Nov - FORECAST 35

Simple Moving Average :

Simple Moving Average 12- 36 MA n = n i = 1  D i n where n = number of periods in the moving average D i = demand in period i 36

3-month Simple Moving Average:

3-month Simple Moving Average 12- 37 Jan 120 Feb 90 Mar 100 Apr 75 May 110 June 50 July 75 Aug 130 Sept 110 Oct 90 Nov - ORDERS MONTH PER MONTH MA 3 = 3 i = 1  D i 3 = 90 + 110 + 130 3 = 110 orders for Nov – – – 103.3 88.3 95.0 78.3 78.3 85.0 105.0 110.0 MOVING AVERAGE 37

5-month Simple Moving Average:

5-month Simple Moving Average 12- 38 Jan 120 Feb 90 Mar 100 Apr 75 May 110 June 50 July 75 Aug 130 Sept 110 Oct 90 Nov - ORDERS MONTH PER MONTH MA 5 = 5 i = 1  D i 5 = 90 + 110 + 130+75+50 5 = 91 orders for Nov – – – – – 99.0 85.0 82.0 88.0 95.0 91.0 MOVING AVERAGE 38

Smoothing Effects:

Smoothing Effects 12- 39 150 – 125 – 100 – 75 – 50 – 25 – 0 – | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov Actual Orders Month 5-month 3-month 39

Weighted Moving Average:

Weighted Moving Average 12- 40 WMA n = i = 1  W i D i where W i = the weight for period i , between 0 and 100 percent  W i = 1.00 Adjusts moving average method to more closely reflect data fluctuations n 40

Weighted Moving Average Example:

Weighted Moving Average Example 12- 41 MONTH WEIGHT DATA August 17% 130 September 33% 110 October 50% 90 WMA 3 = 3 i = 1  W i D i = (0.50)(90) + (0.33)(110) + (0.17)(130) = 103.4 orders November Forecast 41

CAUSAL METHODS:

CAUSAL METHODS Causal methods seek to develop forecasts on the basis of cause-effects relationships specified in an explicit, quantitative manner. Chain Ratio Method Consumption Level Method End Use Method Leading Indicator Method Econometric Method 42

CHAIN RATIO METHOD:

CHAIN RATIO METHOD Market Potential for heated coats in the U.S.: Population (U) = 280,000,000 Proportion of U that are age over 16 (A) = 75% Proportion of A that are men (M) = 50% Proportion of M that have incomes over $65k (I) = 50% Proportion of I that live in cold states (C) = 50% Proportion of C that ski regularly (S) = 10% Proportion of S that are fashion conscious (F) = 30% Proportion of F that are early adopters (E) = 10% Average number of ski coats purchased per year (Y) = .5 coats Average price per coat (P) = $ 200 43

CHAIN RATIO METHOD:

CHAIN RATIO METHOD Market Potential for heated coats in the U.S.: Market Sales Potential = U x A x M x I x C x S x F x E x Y = 280 Million x 0.75 x 0.50 x 0.50 x 0.50 x 0.10 x 0.30 x 0.10 x200 = $7.88 Million 44

CONSUMPTION LEVEL METHOD:

CONSUMPTION LEVEL METHOD This method is used for those products that are directly consumed. This method measures the consumption level on the basis of elasticity coefficients. The important ones are 45

CONSUMPTION LEVEL METHOD:

CONSUMPTION LEVEL METHOD Income Elasticity: This reflects the responsiveness of demand to variations in income. It is calculated as: E1 = [Q2 - Q1/ I2- I1] * [I1+I2/ Q2 +Q1] Where E1 = Income elasticity of demand Q1 = quantity demanded in the base year Q2 = quantity demanded in the following year I1 = income level in the base year I2 = income level in the following year 46

CONSUMPTION LEVEL METHOD:

CONSUMPTION LEVEL METHOD Price Elasticity: This reflects the responsiveness of demand to variations in price. It is calculated as: EP = [Q2 - Q1/ P2- P1] * [P1+P2/ Q2 +Q1] Where EP = Price elasticity of demand Q1 = quantity demanded in the base year Q2 = quantity demanded in the following year P1 = price level in the base year P2 = price level in the following year 47

END USE METHOD:

Suitable for estimating demand for intermediate products Also called as consumption coefficient method Steps Identify the possible uses of the products Define the consumption coefficient of the product for various uses Project the output levels for the consuming industries Derive the demand for the project END USE METHOD 48

END USE METHOD:

END USE METHOD This method forecasts the demand based on the consumption coefficient of the various uses of the product. Projected Demand for Indchem Consumption Coefficient Projected Output in Year X Projected Demand for Indchem in Year X Alpha Beta Kappa Gamma 2.0 1.2 0.8 0.5 10,000 15,000 20,000 30,000 Total 20,000 18,000 16,000 15,000 69,000 49

LEADING INDICATOR METHOD:

LEADING INDICATOR METHOD This method uses the changes in the leading indicators to predict the changes in the lagging indicators. Two basic steps: Identify the appropriate leading indicator(s) Establish the relationship between the leading indicator(s) and the variable to forecast. 50

ECONOMETRIC METHOD:

ECONOMETRIC METHOD An advanced forecasting tool, it is a mathematical expression of economic relationships derived from economic theory. Economic variables incorporated in the model 1. Single Equation Model D t = a 0 + a 1 P t + a 2 N t Where D t = demand for a certain product in year t. P t = price of the product in year t. N t = income in year t. 51

ECONOMETRIC METHOD:

ECONOMETRIC METHOD 2. Simultaneous equation method GNP t = G t + I t + C t I t = a 0 + a 1 GNP t C t = b 0 + b 1 GNP t Where GNP t = gross national product for year t. G t = Governmental purchase for year t. I t = Gross investment for year t. C t = Consumption for year t. 52

ECONOMETRIC METHOD:

Advantages The process sharpens the understanding of complex cause – effect relationships This method provides basis for testing assumptions Disadvantages It is expensive and data demanding To forecast the behaviour of dependant variable, one needs the projected values of independent variables ECONOMETRIC METHOD 53

UNCERTANITIES IN DEMAND FORECASTING:

UNCERTANITIES IN DEMAND FORECASTING Data about past and present markets. Lack of standardization:- product, price, quantity, cost, income…. Few observations Influence of abnormal factors:- war, natural calamity Methods of forecasting Inability to handle unquantifiable factors Unrealistic assumptions Excessive data requirement 54

UNCERTANITIES IN DEMAND FORECASTING:

UNCERTANITIES IN DEMAND FORECASTING Environmental changes Technological changes Shift in government policy Developments on the international scene Discovery of new source of raw material Vagaries of monsoon 55

COPING WITH UNCERTAINTIES:

COPING WITH UNCERTAINTIES Conduct analysis with data based on uniform and standard definitions. Ignore the abnormal or out-of-ordinary observations. Critically evaluate the assumptions Adjust the projections. Monitor the environment. Consider likely alternative scenarios. Conduct sensitivity analysis 56

Market planning:

Market planning Current marketing situation - Market, Competition, Distribution, PEST. Opportunity and issue analysis - SWOT Objectives- Break even, % market share… Marketing strategy- target segment, positioning, 4 Ps Action program- Quarter 1, Q2, Q3…. 57