《行业分析overview+on+analysis1.ppt》由会员分享，可在线阅读，更多相关《行业分析overview+on+analysis1.ppt（155页珍藏版）》请在三一办公上搜索。
1、A Primer on AnalysisOverviewConfidential Document,TABLE OF CONTENTS,IntroductionGeneral analytical techniquesGraphsDeflatorsRegression analysisSupply side analysisCost structuresDesign differencesFactor costsScale,experience,complexity and utilizationSupply curvesDemand side analysisCustomer underst
2、andingsegmentation and“Discovery”conjoint analysismulti-dimensional scalingPrice-volume curves and elasticityDemand forecastingtechnology/substitution curvesWrap-up,LOGIC AND ANALYSIS CRITICAL TOSTRATEGY DEVELOPMENT,Key to strategy development is laying out“logic”toUnderstand what makes business wor
3、keconomicsinteractions across competitors,segments,time,.Conceptually organize client goalsDevise ways to achieve clients goalsHelp client“make it happen”A tightly developed piece of this logic is analysisReducing complex reality to a few salient pointsIsolating important economic elements,ANALYSIS
4、IS MORE THAN NUMBER CRUNCHING,Analysis is.Integrating quantitative and qualitative knowledgeSeeing the bigger pictureThinkingcreativelyconceptuallyNot.Endless calculationsLetting statistics dictate/rule“Classic”scientific rigor,ANALYTICAL BIAS,“Everything can be quantified”Not really,butMost“qualita
5、tive”effects are based in economicsexplicit or opportunity costsaccurately quantifiable or notClient hires us to analyze and objectifyQuantitative analysis is the basis,CREATIVITY AND ANALYTICAL PERSEVERANCE AREIMPORTANT TRAITS FOR SUPERIOR ANALYSTS,Strive to address a problem using different approa
6、ches to test hypotheses and find inconsistenciesTriangulate on answersNever believe a data series blindlyNever stop at first obstacleClients often stop short of good analysis because they quickly surrender in the absence of good,readily available dataWe never surrender to the unavailability of dataY
7、our case leader does not want to hear that“there is no data,”but rather what can be developed,in how much time,and at what cost,WHERE THIS PRIMER FITS,No document can teach you to be a great analystAnswers look easy,but process of getting there painfulEach problem somewhat different from examplesA p
8、rimer canGive flavor of expected analysesShow which analyses have been most productive historicallyExplain basic techniques and warn of common methodological errorsBest training comes fromExperience in project team workDiscussions with John Tang and othersYou are expected to locate knowledge on your
9、 own initiative,DONT LIMIT YOURSELF TO THESE TOOLS,They are a sample of the most commonly used toolsOthers will be of use in specific situationsValue management(CFROI,asset growth,etc.)Additionally,no tool can substitute for a new creative approach,TABLE OF CONTENTS,IntroductionGeneral analytical te
10、chniquesGraphsDeflatorsRegression analysisSupply side analysisCost structuresDesign differencesFactor costsScale,experience,complexity and utilizationSupply curvesDemand side analysisCustomer understandingsegmentation and“Discovery”conjoint analysismulti-dimensional scalingPrice-volume curves and el
11、asticityDemand forecastingtechnology/substitution curvesWrap-up,RELATIONSHIPS HAVE MOST IMPACT WHEN DISPLAYED VISUALLY,Graphs and charts should be easily understandable to a“nonquantitative”clientDisplay one main idea per graphMake the point as directly as possibleDemonstrate clear relevance to acco
12、mpanying material and clients businessClearly label title,axes,and sourcesTailor graph to its audience and purposeExplorationPersuasionDocumentation,CHOOSE GRAPH SCALE THOUGHTFULLY,Match chart boundaries to relevant range of the data as closely as possibleSelect scale to facilitate thinking about pr
13、oposed relationshipsUse same scale across charts if you intend to compare them,LINEAR VS.LOG,On a linear scale,a given difference between two values covers the same distance anywhere on the scaleOn a logarithmic scale,a given ratio of two values covers the same distance anywhere on the scale,1,2,4,8
14、,16,One Cycle,Linear,Log,Log,The ratio of anything to zero is infinite.Zero cannot appear on a log scale.,DATA RELATIONSHIP DETERMINES SELECTION OF SCALEThree Scales Most Common,Linear,Log,Log,Linear,Linear(usually time),Log,Linear,Semi-Log,Log-Log,Constant Rate of Change,Constant Growth Rate,Consta
15、nt“Elasticity”,Given no prior expectation about the form of a relationship,plot it linearly,y=mx+b,log y=mx+b,log y=mlog x+b,WHEN SHOULD A LINEAR GRAPH BE USED?,Linear graphs are best when the change in unit terms is of interest,e.g.,Market share over timeProfit margin over timeForty-five degree dow
16、nward sloping lines on linear graph represent points whose x and y values have constant sumRays through origin represent points with common ratio,Market Share(%),Linear Graph,Hardware,Software,WHEN SHOULD A SEMI-LOG PLOT BE USED?,Semi-log graphs are generally used to illustrate constant growth rates
17、,e.g.,Volume of sales growth over time,Year,Source:Agricultural Statistics,U.S.Corn Yield(Bushels/Acre),R=.95,Semi-Log Graph,WHEN SHOULD A LOG-LOG PLOT BE USED?,Log-log graphs are generally used to plot“elasticities,”e.g.,Price elasticity of demandScale slopeForty-five degree downward sloping lines
18、show points with common product,Salaried and Indirect hourly Employees/Billion Impressions of Capacity,Printing Capacity(Billions of Impressions),78%Scale SlopeR=.636,1,000,100,10,CIRCLE OR BUBBLE CHARTS OFTEN USED TO SHOW A THIRD DIMENSION,Third dimension should be related to x and y axesCommon exa
19、mples include:Market sizeAssetsCost flowCircle area(not diameter)is proportional,BUBBLE CHART EXAMPLECategory Growth Versus Gross Margin Versus Size,1980-84Real CAGR(%),Gross Margin(%),=$1B sales,Consumer Electronics,Toys,Housewares/Gifts,Jewelry,SportingGoods,SmallAppliances,Camera/Photo,Source:Dis
20、count Merchandiser,TABLE OF CONTENTS,IntroductionGeneral analytical techniquesGraphsDeflatorsRegression analysisSupply side analysisCost structuresDesign differencesFactor costsScale,experience,complexity and utilizationSupply curvesDemand side analysisCustomer understandingsegmentation and“Discover
21、y”conjoint analysismulti-dimensional scalingPrice-volume curves and elasticityDemand forecastingtechnology/substitution curvesWrap-up,DEFLATORS CORRECT EFFECTS OF INFLATIONConverts Variables from“Nominal”to“Real”,Time series data in dollars with high or widely fluctuating inflation rates distort pic
22、ture of growthDeflating data removes some of the distortionUsing a deflator index list,currency data are multiplied by the ratio of the base year deflator index to the data year deflator index,e.g.,1979 sales(1993$)=1979(1979$)x,Deflator 1993Deflator 1979,SELECT APPROPRIATE DEFLATOR DEPENDING ONTHE
23、QUESTION YOURE TRYING TO ANSWER,G.N.P.deflator is best for expressing dollars in terms of average real value to the rest of the economyCurrent(variable)weightsMeasured quarterlyC.P.I.is best only for expressing value in relation to consumer spending on a fixed market basket of goods(1973 base)Measur
24、ed monthlyIndustry or product-specific indices are best for converting dollars into measures of physical outputAvailable from Commerce Dept.for broad industry categoriesCan be constructed from client or industry data for narrow categories,BE CAREFUL WHEN MIXING EXCHANGERATES AND INFLATION ACROSS COU
25、NTRIES,First convert each countrys historical data to constant local currencyE.g.,Japan1993 yenW.Germany1993 DMU.S.A.1993 dollarsThen convert to single currency(dollars,for example)at fixed exchange rate,EXAMPLE:AN INTEGRATED CIRCUIT MANUFACTURER,Reported SalesG.N.P.DeflatorAverage I.C.Average I.C.Y
26、ear($M)(1987=1.00)Price($)Transistor Price(),19877861.0001.001.0519885951.033.92.7219897301.075.99.4919908331.119.98.3419911,0621.161.90.2419921,4231.193.98.1819931,8381.2271.14.16,Reported sales$15.2%Real sales$11.4%I.C.unit sales8.9%Transistor sales52.4%,Growth Rates(per year),TABLE OF CONTENTS,In
27、troductionGeneral analytical techniquesGraphsDeflatorsRegression analysisSupply side analysisCost structuresDesign differencesFactor costsScale,experience,complexity and utilizationSupply curvesDemand side analysisCustomer understandingsegmentation and“Discovery”conjoint analysismulti-dimensional sc
28、alingPrice-volume curves and elasticityDemand forecastingtechnology/substitution curvesWrap-up,REGRESSION ANALYSIS IS A POWERFUL TOOL FORUNDERSTANDING RELATIONSHIP BETWEEN TWOOR MORE VARIABLES,Regression analysis:Explains variation in one variable(dependent)using variation in one or more other varia
29、bles(independent)Quantifies and validates relationshipsIs useful for prediction and causal explanationBut.Must not substitute for clear independent thinking about a problemUse as single element in portfolio of analytical techniquesCan be morass“lose forest for trees”,ANY RELATIONSHIP BETWEEN VARIABL
30、ES X AND Y?,Used alone,graphical methods provide only qualitative and general inferences about relationships,PercentACV,80%,70%,60%,50%,40%,30%,20%,10%,0%,Annual Number of Purchases by Consumer,X:Annual number of purchases by buyerY:Percent ACV,Percent ACV is the volume weighted average percent of g
31、rocery stores which carry the category.Sources:ScanTrack;IRI Marketing Factbook;BCG Analysis,REGRESSION ANALYSIS ANSWERS THESE QUESTIONS,What is relationship between X and YHow big an effect does X have on Y?What is the functional form?Is effect positive or negative?How strong is relationship?How we
32、ll does X“explain”Y?How well does my model work overall?How well have I explained Y in general?Are there other variables that I should be including?,WHAT IS RELATIONSHIP BETWEEN X AND Y?,PercentACV,Annual Number of Purchases by Customer,Regression fits a straight line to the data pointsPercent ACV=-
33、0.2790+0.2606 annual purchasesOne more annual purchase will raise percent ACV by 0.2606 percentage pointsSlope of line(here 0.2606)indicates size of effect;sign of slope(here positive)indicates whether effect is positive or negative,R2=0.69,Multiple R0.83354R Square(%)69.48Adjusted R Square(%)68.35S
34、tandard Error0.10394Observations29,Regression Statistics,Regression10.664000.6640061.4641.98146E-08Residual270.291680.01080Total280.95568,Analysis of VariancedfSum of SquaresMean SquareFSignificant F,Intercept(0.27901)0.06286(4.439)0.00013(0.40799)(0.15003)X10.260560.033247.8401.5372E-080.192370.328
35、76,CoefficientsStandard Errort StatisticP-valueLower 95%Upper 95%,Sources:Scantrack;IRI Marketing Factbook(1990);BCG Analysis,Microsoft Excel Regression Output,HOW STRONG IS RELATIONSHIP?,t-statistic measures how well X explains YSimply calculated as slope divided by its standard error Closer slope
36、is to zero,and/or higher standard error(variability),the weaker the relationshipA short-cut:t-statistic greater in magnitude than 2 means relationship is very strong(i.e.,roughly 95%confidence level).Between 1.5 and 2,relationship is relatively strong(i.e.,roughly 85-95%confidence level).Under 1.5,r
37、elationship is weak.,Multiple R0.83354R Square(%)69.48Adjusted R Square(%)68.35Standard Error0.10394Observations29,Regression10.664000.6640061.4641.98146E-08Residual270.291680.01080Total280.95568,Regression Statistics,dfSum of SquaresMean SquareFSignificance F,Intercept(0.27901)0.06286(4.439)0.00013
38、(0.40799)(0.15003)x10.260560.033247.8401.5372E-080.192370.32876,CoefficientsStandard Errort StatisticP-valueLower 95%Upper 95%,Analysis of Variance,HOW WELL DOES MY MODEL WORK OVERALL?,R2 measures proportion of variation in Y that is explained by the variables in the model-here just XIndicates overa
39、ll how well model explains YBased on how dispersed the data points are around the regression lineR2 measured on scale of 0 to 100%100%indicates perfect fit of regression line to the data pointsLow R2 indicates current model does not fit the data wellsuggests there are other explanatory factors,besid
40、es X,that would help explain Y,Multiple R0.83354R Square(%)69.48Adjusted R Square(%)68.35Standard Error0.10394Observations29,Regression10.664000.6640061.4641.98146E-08Residual270.291680.01080Total280.95568,Regression Statistics,dfSum of SquaresMean SquareFSignificance F,Intercept(0.27901)0.06286(4.4
41、39)0.00013(0.40799)(0.15003)x10.260560.033247.8401.5372E-080.192370.32876,CoefficientsStandard Errort StatisticP-valueLower 95%Upper 95%,Analysis of Variance,USE MULTIPLE REGRESSION TO SORT OUT EFFECTSOF SEVERAL INFLUENCES,UseWhen several factors have an impact simultaneouslyTo help distinguish caus
42、e from correlationDont use as“fishing expedition”,MULTIPLE REGRESSION CAN ENHANCEPREDICTIVE ABILITY,%ACV with Features and/or Displays,Brand Size,Percent of Households Buying,Annual Number of Purchases per Year,%ACV with Features and/or Displays,%ACV with Features and/or Displays,Brand Size($M),Perc
43、ent of Households Buying,Annual Number of Purchases/Year,R=.67,R=.51,R=.69,R=.87,Predicted%ACV with Features and/or Displays,Actual%ACV with Features and/or Displays,Brand Size,Reach,andPurchase Freqency,Sources:Scantrack;IRI Marketing Factbook 1990;BCG Analysis,OTHER REGRESSION EXAMPLES,Very Low R*
44、,PercentACV,U.S.Corn Yield(Bushels/Acre),U.S.Corn Yield(Bushels/Acre),Retailer Margin on Deal,Average Number of Days on Deal,Total Annual Purchases(M),Negative Slope*,Nonlinear Raw Data*,After Log Transformation*,*Sources:IRI Marketing Factbook;Certified Price Book;Nielsen;BCG Analysis*Source:Agricu
45、ltural Statistics,R=.64,R=.002,R=.95,QUESTIONS TO ASK BEFORE RUNNING A REGRESSION,Which variable is the predictive(or dependent)variable?Often straightforward but sometimes requires thoughtConsider direction of causationWhat explanatory variables do I believe are appropriate to include?Avoid spuriou
46、s correlationsthink independently about what factors are logical to includeAvoid including explanatory variables that are highly correlated with each otherShould the regression have an intercept term?How far can the data be reasonably extrapolated?Should the regression line cut through the origin?Do
47、es a zero value of explanatory variable imply a zero value for predictive variable?Have I plotted the data?Watch out for outliersLook for form of data(linear,exponential,power,etc.)Do I have enough observations?Rough rule of thumb:10 observations for each explanatory variable,TABLE OF CONTENTS,Intro
48、ductionGeneral analytical techniquesGraphsDeflatorsRegression analysisSupply side analysisCost structuresDesign differencesFactor costsScale,experience,complexity and utilizationSupply curvesDemand side analysisCustomer understandingsegmentation and“Discovery”conjoint analysismulti-dimensional scali
49、ngPrice-volume curves and elasticityDemand forecastingtechnology/substitution curvesWrap-up,Define relevant competitive environmentBasis of advantage(profit levers)Relative strengths/weaknesses of competitorsBarrier to new competitorsEffect of changes over time(technology,scale)Predict effect of one
50、 firms actions onCompetitors(short term,reaction)Profit and cash flow of clientNotCost systemsCorrecting average costing for its own sake,WHY DO COST ANALYSIS?,WHICH COSTS?,Competitive cost analysisUse actual costs,not standardsUse fully absorbed costs,since expenses are often the most sensitive to
