贝恩分析技能概要课件

Left HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-1-A Primer on AnalysisOverviewConfidential Document bcA Primer on Analysis bcLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-2-TABLE OF CONTENTSIntroductionGeneral analytical techniquesGraphsDeflatorsRegression analysisSupply side analysisCost structuresDesign differencesFactor costsScale,experience,complexity and utilizationSupply curvesDemand side analysisCustomer understanding-segmentation and“Discovery”-conjoint analysis-multi-dimensional scalingPrice-volume curves and elasticityDemand forecasting-technology/substitution curvesWrap-upTABLE OF CONTENTSIntroductioLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-3-LOGIC AND ANALYSIS CRITICAL TOSTRATEGY DEVELOPMENTKey to strategy development is laying out“logic”toUnderstand what makes business work-economics-interactions 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 elementsLOGIC AND ANALYSIS CRITICALLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-4-ANALYSIS IS MORE THAN NUMBER CRUNCHINGAnalysis is.Integrating quantitative and qualitative knowledgeSeeing the bigger pictureThinking-creatively-conceptuallyNot.Endless calculationsLetting statistics dictate/rule“Classic”scientific rigorANALYSIS IS MORE THAN NUMBLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-5-ANALYTICAL BIAS“Everything can be quantified”Not really,butMost“qualitative”effects are based in economics-explicit or opportunity costs-accurately quantifiable or notClient hires us to analyze and objectifyQuantitative analysis is the basisANALYTICAL BIAS“Everything caLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-6-CREATIVITY AND ANALYTICAL PERSEVERANCE AREIMPORTANT TRAITS FOR SUPERIOR ANALYSTS Strive to address a problem using different approaches 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 dataYour 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 costCREATIVITY AND ANALYTICAL PLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-7-WHERE THIS PRIMER FITSNo document can teach you to be a great analystAnswers look easy,but process of getting there painfulEach problem somewhat different from examplesA primer 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 own initiativeWHERE THIS PRIMER FITSNo doLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-8-DONT LIMIT YOURSELF TO THESE TOOLSThey 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 approachDONT LIMIT YOURSELF TO THLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-9-TABLE OF CONTENTSIntroductionGeneral analytical techniquesGraphsDeflatorsRegression analysisSupply side analysisCost structuresDesign differencesFactor costsScale,experience,complexity and utilizationSupply curvesDemand side analysisCustomer understanding-segmentation and“Discovery”-conjoint analysis-multi-dimensional scalingPrice-volume curves and elasticityDemand forecasting-technology/substitution curvesWrap-upTABLE OF CONTENTSIntroductioLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-10-RELATIONSHIPS HAVE MOST IMPACT WHEN DISPLAYED VISUALLYGraphs and charts should be easily understandable to a“nonquantitative”clientDisplay one main idea per graphMake the point as directly as possibleDemonstrate clear relevance to accompanying material and clients businessClearly label title,axes,and sourcesTailor graph to its audience and purposeExplorationPersuasionDocumentationRELATIONSHIPS HAVE MOST IMPLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-11-CHOOSE GRAPH SCALE THOUGHTFULLYMatch chart boundaries to relevant range of the data as closely as possibleSelect scale to facilitate thinking about proposed relationshipsUse same scale across charts if you intend to compare themCHOOSE GRAPH SCALE THOUGHTFLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-12-LINEAR VS.LOGOn 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 scale124816One CycleLinearLogLogThe ratio of anything to zero is infinite.Zero cannot appear on a log scale.LINEAR VS.LOGOn a linear scLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-13-DATA RELATIONSHIP DETERMINES SELECTION OF SCALEThree Scales Most CommonLinearLogLogLinearLinear(usually time)LogLinearSemi-LogLog-LogConstant Rate of ChangeConstant Growth RateConstant“Elasticity”Given no prior expectation about the form of a relationship,plot it linearlyy=mx+blog y=mx+blog y=mlog x+bDATA RELATIONSHIP DETERMINESLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-14-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 downward sloping lines on linear graph represent points whose x and y values have constant sumRays through origin represent points with common ratioMarket Share(%)Linear GraphHardwareSoftwareWHEN SHOULD A LINEAR GRAPHLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-15-WHEN SHOULD A SEMI-LOG PLOT BE USED?Semi-log graphs are generally used to illustrate constant growth rates,e.g.,Volume of sales growth over timeYearSource:Agricultural StatisticsU.S.Corn Yield(Bushels/Acre)R=.95Semi-Log GraphWHEN SHOULD A SEMI-LOG PLOLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-16-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 show points with common productSalaried and Indirect hourly Employees/Billion Impressions of CapacityPrinting Capacity(Billions of Impressions)78%Scale SlopeR=.6361,00010010WHEN SHOULD A LOG-LOG PLOTLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-17-CIRCLE OR BUBBLE CHARTS OFTEN USED TO SHOW A THIRD DIMENSIONThird dimension should be related to x and y axesCommon examples include:Market sizeAssetsCost flowCircle area(not diameter)is proportionalCIRCLE OR BUBBLE CHARTS OFLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-18-BUBBLE CHART EXAMPLECategory Growth Versus Gross Margin Versus Size1980-84Real CAGR(%)Gross Margin(%)=$1B salesConsumer ElectronicsToysHousewares/GiftsJewelrySportingGoodsSmallAppliancesCamera/PhotoSource:Discount MerchandiserBUBBLE CHART EXAMPLECategoLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-19-TABLE OF CONTENTSIntroductionGeneral analytical techniquesGraphsDeflatorsRegression analysisSupply side analysisCost structuresDesign differencesFactor costsScale,experience,complexity and utilizationSupply curvesDemand side analysisCustomer understanding-segmentation and“Discovery”-conjoint analysis-multi-dimensional scalingPrice-volume curves and elasticityDemand forecasting-technology/substitution curvesWrap-upTABLE OF CONTENTSIntroductioLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-20-DEFLATORS CORRECT EFFECTS OF INFLATIONConverts Variables from“Nominal”to“Real”Time series data in dollars with high or widely fluctuating inflation rates distort picture of growthDeflating data removes some of the distortionUsing a deflator index list,currency data are multiplied by the ratio of the base year deflator index to the data year deflator index,e.g.,1979 sales(1993$)=1979(1979$)x Deflator 1993Deflator 1979DEFLATORS CORRECT EFFECTS OLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-21-SELECT APPROPRIATE DEFLATOR DEPENDING ONTHE QUESTION YOURE TRYING TO ANSWERG.N.P.deflator is best for expressing dollars in terms of average real value to the rest of the economyCurrent(variable)weightsMeasured quarterlyC.P.I.is best only for expressing value in relation to consumer spending on a fixed market basket of goods(1973 base)Measured monthlyIndustry or product-specific indices are best for converting dollars into measures of physical outputAvailable from Commerce Dept.for broad industry categoriesCan be constructed from client or industry data for narrow categoriesSELECT APPROPRIATE DEFLATOR Left HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-22-BE CAREFUL WHEN MIXING EXCHANGERATES AND INFLATION ACROSS COUNTRIESFirst convert each countrys historical data to constant local currencyE.g.,Japan1993 yenW.Germany1993 DMU.S.A.1993 dollarsThen convert to single currency(dollars,for example)at fixed exchange rateBE CAREFUL WHEN MIXING EXCLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-23-EXAMPLE:AN INTEGRATED CIRCUIT MANUFACTURERReported SalesG.N.P.DeflatorAverage I.C.Average I.C.Year($M)(1987=1.00)Price($)Transistor Price()19877861.0001.001.0519885951.033.92.7219897301.075.99.4919908331.119.98.3419911,0621.161.90.2419921,4231.193.98.1819931,8381.2271.14.16Reported sales$15.2%Real sales$11.4%I.C.unit sales8.9%Transistor sales52.4%Growth Rates(per year)EXAMPLE:AN INTEGRATED CIRCLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-24-TABLE OF CONTENTSIntroductionGeneral analytical techniquesGraphsDeflatorsRegression analysisSupply side analysisCost structuresDesign differencesFactor costsScale,experience,complexity and utilizationSupply curvesDemand side analysisCustomer understanding-segmentation and“Discovery”-conjoint analysis-multi-dimensional scalingPrice-volume curves and elasticityDemand forecasting-technology/substitution curvesWrap-upTABLE OF CONTENTSIntroductioLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-25-REGRESSION ANALYSIS IS A POWERFUL TOOL FORUNDERSTANDING RELATIONSHIP BETWEEN TWOOR MORE VARIABLESRegression analysis:Explains variation in one variable(dependent)using variation in one or more other variables(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”REGRESSION ANALYSIS IS A PLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-26-ANY RELATIONSHIP BETWEEN VARIABLES X AND Y?Used alone,graphical methods provide only qualitative and general inferences about relationshipsPercentACV80%70%60%50%40%30%20%10%0%Annual Number of Purchases by ConsumerX:Annual number of purchases by buyerY:Percent ACVPercent ACV is the volume weighted average percent of grocery stores which carry the category.Sources:ScanTrack;IRI Marketing Factbook;BCG AnalysisANY RELATIONSHIP BETWEEN VALeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-27-REGRESSION ANALYSIS ANSWERS THESE QUESTIONSWhat 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 well 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?REGRESSION ANALYSIS ANSWERS Left HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-28-WHAT IS RELATIONSHIP BETWEEN X AND Y?PercentACVAnnual Number of Purchases by CustomerRegression fits a straight line to the data pointsPercent ACV=-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 negativeR2=0.69Multiple R0.83354R Square(%)69.48Adjusted R Square(%)68.35Standard Error0.10394Observations29Regression StatisticsRegression10.664000.6640061.4641.98146E-08Residual270.291680.01080Total280.95568Analysis of VariancedfSum of SquaresMean SquareFSignificant FIntercept(0.27901)0.06286(4.439)0.00013(0.40799)(0.15003)X10.260560.033247.8401.5372E-080.192370.32876CoefficientsStandard Errort StatisticP-valueLower 95%Upper 95%Sources:Scantrack;IRI Marketing Factbook(1990);BCG AnalysisMicrosoft Excel Regression OutputWHAT IS RELATIONSHIP BETWEELeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-29-HOW STRONG IS RELATIONSHIP?t-statistic measures how well X explains YSimply calculated as slope divided by its standard error Closer slope 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,relationship is weak.Multiple R0.83354R Square(%)69.48Adjusted R Square(%)68.35Standard Error0.10394Observations29Regression10.664000.6640061.4641.98146E-08Residual270.291680.01080Total280.95568Regression StatisticsdfSum of SquaresMean SquareFSignificance FIntercept(0.27901)0.06286(4.439)0.00013(0.40799)(0.15003)x10.260560.033247.8401.5372E-080.192370.32876CoefficientsStandard Errort StatisticP-valueLower 95%Upper 95%Analysis of VarianceHOW STRONG IS RELATIONSHIP?Left HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-30-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 overall 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 well-suggests there are other explanatory factors,besides X,that would help explain YMultiple R0.83354R Square(%)69.48Adjusted R Square(%)68.35Standard Error0.10394Observations29Regression10.664000.6640061.4641.98146E-08Residual270.291680.01080Total280.95568Regression StatisticsdfSum of SquaresMean SquareFSignificance FIntercept(0.27901)0.06286(4.439)0.00013(0.40799)(0.15003)x10.260560.033247.8401.5372E-080.192370.32876CoefficientsStandard Errort StatisticP-valueLower 95%Upper 95%Analysis of VarianceHOW WELL DOES MY MODEL WOLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-31-USE MULTIPLE REGRESSION TO SORT OUT EFFECTSOF SEVERAL INFLUENCESUseWhen several factors have an impact simultaneouslyTo help distinguish cause from correlationDont use as“fishing expedition”USE MULTIPLE REGRESSION TO Left HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-32-MULTIPLE REGRESSION CAN ENHANCEPREDICTIVE ABILITY%ACV with Features and/or DisplaysBrand SizePercent of Households BuyingAnnual Number of Purchases per Year%ACV with Features and/or Displays%ACV with Features and/or DisplaysBrand Size($M)Percent of Households BuyingAnnual Number of Purchases/YearR=.67R=.51R=.69R=.87Predicted%ACV with Features and/or DisplaysActual%ACV with Features and/or DisplaysBrand Size,Reach,andPurchase FreqencySources:Scantrack;IRI Marketing Factbook 1990;BCG AnalysisMULTIPLE REGRESSION CAN ENHLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-33-OTHER REGRESSION EXAMPLESVery Low R*PercentACVU.S.Corn Yield(Bushels/Acre)U.S.Corn Yield(Bushels/Acre)Retailer Margin on DealAverage Number of Days on DealTotal Annual Purchases(M)Negative Slope*Nonlinear Raw Data*After Log Transformation*Sources:IRI Marketing Factbook;Certified Price Book;Nielsen;BCG Analysis*Source:Agricultural StatisticsR=.64R=.002R=.95OTHER REGRESSION EXAMPLESVerLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-34-QUESTIONS TO ASK BEFORE RUNNING A REGRESSIONWhich 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 spurious 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?Does 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 variableQUESTIONS TO ASK BEFORE RULeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-35-TABLE OF CONTENTSIntroductionGeneral analytical techniquesGraphsDeflatorsRegression analysisSupply side analysisCost structuresDesign differencesFactor costsScale,experience,complexity and utilizationSupply curvesDemand side analysisCustomer understanding-segmentation and“Discovery”-conjoint analysis-multi-dimensional scalingPrice-volume curves and elasticityDemand forecasting-technology/substitution curvesWrap-upTABLE OF CONTENTSIntroductioLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-36-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 firms actions onCompetitors(short term,reaction)Profit and cash flow of clientNotCost systemsCorrecting average costing for its own sakeWHY DO COST ANALYSIS?Define relevant competitive enLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-37-WHICH COSTS?Competitive cost analysisUse actual costs,not standardsUse fully absorbed costs,since expenses are often the most sensitive to scale/experience,etc.Identify costs and expenses with individual models/product linesTherefore,competitive cost analysis involvesAllocation of variancesAllocation of expensesCapitalization of nonrecurring costs and expensesWHICH COSTS?Competitive cost aLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-38-IN MOST SUPPLY SIDE ANALYSIS,FIRSTLAY OUT THE CLIENTS COST STRUCTUREFocus on Key Cost ElementsProfitOverheadSelling and DistributionVariable ManufacturingRaw MaterialsFixed Manufacturing8%8%16%18%40%10%8%10%35%11%18%18%GainRaw materialsSelling and distributionAdvantageBackward integrationRelated dive