3 macro environment+demand analysis sf
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The document outlines a comprehensive framework for market analysis, emphasizing macro-environment and demand analysis to evaluate market attractiveness and strategic decision-making. It discusses the marketing process, including strategic objectives, customer needs, and competitive positioning, alongside various forecasting methods for demand estimation. It also highlights the significance of understanding external factors and internal market positioning to identify opportunities and threats in the marketplace.
3 macro environment+demand analysis sf
- 1. 1 Value analysis: Macro environment analysis Macro approach to demand analysis
- 2. 2 The marketing process (1/2) Strategic objectives Targeting Choice of market segment/segments Positioning Definition of the distinctive aspects on whihc we would like to be perceived as different and preferable Value proposition Definition of the benefit/value we intend to transmit to the customer Marketing Mix Planning the marketing lever/action plan Realization and control Understanding of the external environment -Breadth, structure, market trends -Segmentation -Competition -Customer needs and expectations -Influence of external factors Understanding of our own position in the market -Comparison between our market services and those of the competition -Diagnosis of the reasons for the diversity of services Our strengths and weaknesses v our competitors OPERATIONALSTRATEGIC Opportunities and threats
- 3. 3 The marketing process (2/2) Strategic objectives Targeting Choice of market segment/segments Positioning Definition of the distinctive aspects on whihc we would like to be perceived as different and preferable Value proposition Definition of the benefit/value we intend to transmit to the customer Marketing Mix Planning the marketing lever/action plan Realization and control Understanding of the external environment -Breadth, structure, market trends -Segmentation -Competition -Customer needs and expectations -Influence of external factors Opportunities and threats Understanding of our own position in the market -Comparison between our market services and those of the competition -Diagnosis of the reasons for the diversity of services Our strengths and weaknesses v our competitors OPERATIONALSTRATEGIC
- 4. 4 Market analysis logic – a new marketing approach The marketing approach considers three dimensions for market analysis: 1. Macro-environment analysis 2. Demand analysis A. From a “macro” viewpoint: I. Definition and qualification of the market (boundaries, breadth, etc.) II. Measurement of the market (estimate and forecast of demand) B. From a “micro” viewpoint: I. Analysis of market needs II. Analysis of buying behaviour 3. Offering analysis A. Analysis of the competitive system
- 5. 5 Demand analysis: a reference framework Objectives: To analyse the target market to evaluate its attractiveness, the decision to enter and the marketing strategy. 1. Evaluation of the marketability of new products. 2. Decision to exit a market. Tasks: 1. Analyse the needs and wants of existing and potential customers 2. Qualify and estimate the reference market Macro demand analysis 3. Analyse customers’ buying behaviour Micro demand analysis
- 6. 6 Macro-environment analysis Objectives: To analyse the current situation and market potential in order to identify: 1. Unmet needs 2. Business opportunities 3. Trends 4. Mega trends
- 7. 7 •Physical environment and resources •Political and institutional environment •Economic environment •Financial environment •Infrastructure •Technology •Public opinion •Demographic and socio-cultural profile •Consumer associations •... Subjects constituting the macro-environment
- 8. 8 A reference framework: PESTE analysis Political Economic Social Technological Ecological
- 9. 9 Political analysis Political / legal factors Licenses (e.g. bakeries) Free trade agreements (e.g. WTO, NAFTA, EU ecc.) Legal environment (e.g. consumption taxes vs. production taxes) Country V.A.T. Corporate tax rate Argentina 21,0 % Bulgaria 20,0 % Cyprus 17,0 % 10% Denmark 25,0 % France 19,6 % 33,22% Germany 19,0 % 29,48% Italy 22,0 % 31,4% UK 20% Romania 24,0% Russia 18,0 % 20% Switzerland 8% 21,7%
- 10. 10 Socio-Economic, Technological & Environmental analysis Economic factors Social factors • Conjunctural impact on the demand • Average purchase power • Distribution of wealth • Interest rate • Health consciousness • Population growth rate • Age distribution • Career attitudes • Emphasis on safety Technological factors Enviornmental factors • Scientific/technological discoveries • Technological infrastructures • Weather • Climate and climate change (e.g. tourism) • Environmental awareness
- 13. 13 Example: OECD Countries Organisation for Economic Co- operation and Development. OECD Country AUSTRALIA AUSTRIA BELGIUM CANADA CHILE CZECH REPUBLIC DENMARK ESTONIA FINLAND FRANCE GERMANY GREECE HUNGARY ICELAND IRELAND ISRAEL ITALY JAPAN KOREA LUXEMBOURG MEXICO NETHERLANDS NEW ZEALAND NORWAY POLAND PORTUGAL SLOVAK REPUBLIC SLOVENIA SPAIN SWEDEN SWITZERLAND TURKEY UNITED KINGDOM UNITED STATES 13 On 14 December 1960, 20 countries originally signed the Convention on the Organisation for Economic Co-operation and Development. Since then, 14 countries have become members of the Organisation. Here is a list of the current Member countries of the Organisation and the dates on which they deposited their instruments of ratification.
- 14. 14 Innovative countries 14 The Global Innovation Index (GII) is a recognition of the key role that innovation serves as a driver of economic growth and prosperity.
- 18. 18 Demand forecasting 1.Market qualification 2.Market estimation
- 20. 20 Market qualification Potential market: market that is achieved when the marketing effort is infinite Qualified market: market with the requisites to be available for the purchase (e.g. 18+ y.o. – or 21 y.o. in some countries – for some products; driving license, etc.) Available market: qualified + able to spend enough to buy the product or interested enough in the category Served market: available market that is reached by the marketing effort of the companies Penetrated market: market that has already purchased products/services in the category
- 22. 22 Potential Market of Cloudea Dimensione azienda Somma di IMPRESE/ISTITUZIONI Somma di IMP. INFORMATIZZATE Somma di IB FY 2009 Somma di ADDETTI Core MM (50-249 PCs) 10.904 10.900 972.884 2.117.021 Core SB (5-24 PCs) 305.733 192.066 1.817.466 3.573.000 Home Business/Low SB (1-4 PCs) 4.002.652 2.147.879 4.260.164 7.782.820 Low MM (25-49 PCs) 13.439 13.327 450.897 925.012 Totale complessivo 4.332.728 2.364.172 7.501.411 14.397.853 Settore Penetrazione Iaas Pubblico Penetrazione SaaS Pubblico Servizi (Editoria, Media, ICT,TLC) 15% 63% Industrial 49% 64% PA 12% 59% Utility/Oil & Gas 27% 47% Bancario, Assicurativo 21% 36% GDO/Vendita al dettaglio 30% 40%
- 23. 23 Demand estimate and forecast Processes and methods for obtaining a qualitative and quantitative valuation of demand The choice of the right model depends on the specific objectives and on the availability of quantitative data to base the esteem
- 24. 24 Choice of the market forecasting method 1. Based on availabile data A. Quantitative data available? Quantitative methods B. Quantitative data not available? Qualitative methods 2. Based on the objective. Three main objectives: A. Estimating market size of an existing product? B. Estimating the market demand after a new product launch? C. Estimating the market demand precisely in a time- lapse?
- 25. 25 Forecasting methods Objective Estimate market size Estimate demand for new products Estimate demand for established products Quantitative • Coefficient method • Diffusion models • Analytical / epidemiological • Time series • Linear Regression Qualitative •“Desk” methods • Gaussian •Adoption models •Market tests •Market research
- 26. 26 Forecasting methods Objective Estimate market size Estimate demand for new products Estimate demand for established products Quantitative • Coefficient method • Diffusion models • Analytical / epidemiological • Time series • Linear Regression Qualitative •“Desk” methods • Gaussian •Adoption models •Market tests •Market research
- 27. 27 Coefficient method What are coefficients? Parameters representing a past experience or a future expectation on the evolution of a phenomenon - Consuming good - Industrial good - Investment good Coefficient models vary according to: - Product type - Repurchase rate 27 Estimate market size
- 28. 28 Fast moving consumer goods and services, not tied to the use of equipment (e.g. preserves, personal or home care products, etc.) N x % x Cu n N Potential market size % effective users of the category (COVERAGE) Individual rate of consumption (PENETRATION) Market research and observation Q= Market size (n° units) 28 Estimate market size
- 29. 29Marketing Multicanale - a.a. 2009/10 - prof.Giuliano Noci 26 Quarterly demand of throw-away razor blades in the Italian market 25.107.509 x x 415 % Number of Italian males over 14 y.o. % of throw-away razor blades users Number of blades used quarterly on average Q= Market size (n° units) 29 = 15.064.505 Estimate market size: an example
- 30. 30 Consuming goods tied to the use of specific equipment N x % x Ta x ct na N % owning the equipment Use rate of the equipment Number of units of goods used per single use (TECHNICAL COEFFICIENT) Market research and observation Product characteristic Q= Potential market size (n° units) 30 Estimate market size
- 31. 31Marketing Multicanale - a.a. 2009/10 - prof.Giuliano Noci 28 Monthly demand for dishwashing tabs 20mln x % x 25 x 135,4 Penetration of dishwashing machines Monthly # of washing Number of tabs per washing cycle Q = Number of Italian households 31 = 177.000.000 Estimate market size: an example
- 32. 32 Desk Research Desk Research is the research technique which is mainly acquired by sitting at a desk. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Internal Desk Research External Desk Research Online Desk Research Government published data Customer desk research Analyst published data (free or paid)
- 33. 33 Forecasting methods Objective Estimate market size Estimate demand for new products Estimate demand for established products Quantitative • Coefficient method • Diffusion models • Analytical / epidemiological • Time series • Linear Regression Qualitative •“Desk” methods • Gaussian •Adoption models •Market tests •Market research
- 34. 34 Epidemiologic models Two kinds of users Purchasers (infected) Non-purchasers (not enfected) Purchasers “infect” non-purchasers Like in an epidemy… The higher the number of purchasers in the time t, say Q(t), the higher the probability of infection 34 Demand forecasting Wikipedia : Journal o Clinical Epidemiology
- 35. 3535 Infection coefficient (or infection probability) y is distributed like a logistic curve t 1/2 1 y t = -K/r r = diffusion rate of innovation K = starting condition Demand forecasting
- 36. 3636 Coefficient y depends on Q(t) The number of purchasers in t Q* The potential market y= Q(t) Q* ____ Demand forecasting
- 37. 3737 Multiplying Q* times and deriving, we get the purchasers in the exact time t q(t) t = -K/r q(t)= y’(t)Q* x t Demand forecasting
- 38. 3838 Customers have different attitudes towards first purchase of a new product • Precursors (buy right because the product is new) • Innovators (recognize value-for-money and usefulness) • innovative majority (deliberation, prudence) • conservative majority (skepticism) • conservatives, or last buyers (tradition) • unyielding Demand forecasting
- 39. 39 Online & Mobile User behavior
- 40. 40 Forrester Research on Purchasing influence In the definition of the process we always remember what influences people to buy in different countries because it might be too different from one area to another
- 41. 4141 t 2,5 13,5 34 34 13,5 Demand 2,5 Hence, Rogers’ model (1965) Demand forecasting
- 42. 4242 precursors Innovators Innovative majority Conservative majority Conservatives and unyelding Cumulate demand t Rogers’ model (1965) Demand forecasting
- 43. 43 Rogers’ Curve Examples Today’s social and cloud services (2011) How political culture influence the high tech buying attitude and how innovators are gaining
- 44. 44 Rogers’ Curve – Product Example
- 45. 4545 Established products Qualitative approaches Opinion surveys: Experts (informal methods, DELPHI) Sales force Customers perception, behavior (intention to buy, buy online, etc…) Etc… Demand forecasting
- 46. 4646 Pros Cons Experts •External (multiple) viewpoint •Voice of experience •Statistically insignificant •Potentially costly Sales force •Cheap and timely •Direct contact with customers •Statistically insignificant •Only internal Customers •Statistically significant (if quantitative) •Unfiltered •Complex •Costly •Long Demand forecasting
- 47. 47 Forecasting methods Objective Estimate market size Estimate demand for new products Estimate demand for established products Quantitative • Coefficient method • Diffusion models • Analytical / epidemiological • Time series • Linear Regression Qualitative •“Desk” methods • Gaussian •Adoption models •Market tests •Market research
- 48. 4848 A. Past demand B. Factors influencing demand Time series Causal models Established products Quantitative approaches Forecast based on: Demand forecasting
- 49. 4949 Time series Past demand data Extrapolation techniques Forecasting Demand forecasting
- 50. 5050 Extrapolation techniques • Autoregressive: AR • Moving Average: MA • Autoregressive with moving average: ARMA • Exponential smoothing etc. Time series Demand forecasting
- 51. 51 Demand forecasting A time series is made up of a combination of 4 “movements”: Trends (T): the underlying tendency that affects the long term. Cyclicity (C): the tendency of the business cycle that characterises the course of the economy in a particular sector in the medium-long term (3 – 7 years). Seasonality (S): involves shorter periods and refers to patterns that are repeated in more or less the same course during the subsequent corresponding periods (years, months, days). Randomness (E): refers to the variations that cannot be explained by the previous characteristics random error.
- 52. 52 Multiplicative model Analyzing a time series it is possible to estimate the effect of the four components • Trend • Moving average (MA) • Weighted MA • Exponential smoothing • Cyclicity and Seasonality • Ratio approach • Randomness • Inestimable by definition analyzing the time series, may be estimated through contextual analysis (e.g. exceptional marketing effort by companies, etc.) ESCTD *** Demand forecasting
- 53. 53 Moving average Allows an approximate evaluation of the trend: reducing the seasonal and random variations where: Aj= demand of period j N = number of periods on which the average is calculated The moving average eliminates the passing fluctuations giving rise to a rounding-off effect. The number of periods on which the average is calculated influences the result (the higher N is, the more the rounding-off effect). If N is the seasonality period, the moving average has a de-seasonalising effect. Trend = M(t)/M(t-1) Trend analysis
- 54. 54 Trend analysis (2/3) Weighted moving average Enables one to give more weight to more recent data and decreasing weight to others according to preference where: Pj= weight of value in the period j t 1Nt j 1Nt1Nt1t 1tt t t p ApApAp MP .......
- 55. 55 Cyclicity and seasonality Multiplicative model Seasonality index (monthly, weekly or daily): More precise estimate: calculating the seasonality index in different intervals and averaging ESCTD *** T ED SC * * A D S t t Where: - D(t) = demand in the interval t - A = average demand in a not-seasonalised interval
- 56. 56 Cyclicity and seasonality For cyclicity the approach is substantially the same, but: t is generally a year A is the average yearly demand (MA) divided by a trend coefficient A D C t t
- 57. 57 Using time series for demand forecasting Demand forecasting through time series hypothesizes that demand in the interval (t+1) is related somehow to the demand in the interval (t) The function f is the combined effect of the time series components )( tt DfD 1
- 58. 58 Time series decomposition method – example (1/2) Consider a company that produces hollow glass; the table below shows sales for 2008 and some elements that characterise the past series Forecast sales for the month of June 2009 2008 sales in tonnes 492,787 Annual growth trend (average last 2 years) +4.2% Cyclical component Irrelevant Seasonal component (June/July/August) +15% Random component (environmental sensitivity + others) +2%
- 59. 59 Time series decomposition method – example (2/2) Consider 2008: let’s say demand is constant in all months except June, July and August, when there is an increase of 15% (seasonality of the product) 9X + 3*1,15X = 492.787 ton X= 39.581 monthly sales in 2008 (except in June, July and August) Forecast for 2009, monthly sales: Every month except, June, July and August: T + E = 39.581*1,042*1,02 = 42.068 ton June, July and August: other months’ demand + S = 42.068*1,15 = 48.970 ton Implicit hypotheses: T, S and E are constant in time
- 60. 60 Forecasting with moving average Often the multiplicative approach is complex • Need to estimate the cycles • Need of data over a long time set A cheaper approach: moving average to forecast demand Simplest case: no underlying trend Ft+1 (next period demand) is generally equal to the average When a increasing/decreasing trend exists use of exponential smoothing to emphasize the trend M AAA F t 1Nt1tt 1t N ....... Stock exchange indexes analisys simple 40 periods
- 61. 61 Example (1/2) Based on historic data, make a forecast for December using the moving average MONTH PERIOD EFFECTIVE DEMAND 3 MONTH FORECAST WITH MOVING AVERAGE 2 MONTH FORECAST WITH MOVING AVERAGE JANUARY 1 230 FEBRUARY 2 135 MARCH 3 210 183 APRIL 4 238 192 173 MAY 5 48 194 224 JUNE 6 225 165 143 JULY 7 155 170 137 AUGUST 8 30 143 190 SEPTEMBER 9 320 137 93 OCTOBER 10 250 168 175 NOVEMBER 11 210 200 285 DECEMBER 12 260 230
- 62. 62 Example (2/2) 0 50 100 150 200 250 300 350 Jan Feb Mar Apr May Jun July Aug Sept Oct Nov Dec Actualdemand 2 month 3 month 4 month
- 63. 6363 Causal methods Data on the factors influencing demand Causal models (e.g. regressive models) Demand forecasting Forecasting
- 64. 6464 Examples of regressive causal models • Linear regression models • Logit models (logistic regression) etc. Demand forecasting Stock exchange values analisys http://www- bcf.usc.edu/~gareth/ISL/ISLR %20Fourth%20Printing.pdf
- 65. 65 Linear regression models Q = k + a*F1 + b*F2 + …. + ε Q = demand Fi = explanatory factors K = constant (known term) ε = random error (unexplainable) Aim: to estimate the coefficients (a,b,c,…). Generally OLS (Ordinary Least Square) are used to obtain: 1. Estimates of the coefficients and of the constant (correct a statistical substance) 2. Reliability indicators of the method: R2: % of the variability of the dependent variable explained by the independent variable around the regression line Significance tests: test t, test F, Durbin-Watson, etc.
- 66. 66 Linear regression: cautions and limits Three dangers: 1. Existence of self-correlation between independent variables 2. Existence of hidden variables 3. Correlation does not imply causation
- 67. 67 Example (1/2) Ice cream shop 1. Daily demand and temperature are known 2. Hypothesis: there is some correlation between demand and temperature Date Temperature (°C) Ice cream demand
- 68. 68 Example (2/2) Estimate of coefficients Variable Coeff Std Error T-Stat Signif ********************************************************** 1. Constant term -351.403 57.783 -6.08 0.000 2. TEMPERATURE 22.032 2.104 10.47 0.000 Significance indicators Centered R**2 0.7965 Adj-R^2 0.7865 Regression F(1,28) 109.5690 Significance Level of F 0.0000 Durbin-Watson 2.1537 The estimates of coefficients and the regression indicators suggest that the model explains the phenomenon well. 0 50 100 150 200 250 300 350 400 450 500 15 20 25 30 35 Icecreams Temperature (°C)
- 69. 6969 Market tests Experimental design Testing with customers Observation and registration In case, what-if analysis Demand forecasting