某咨询行业分析overviewonanalysis1

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1、Left HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-1-A Primer on AnalysisOverviewConfidential DocumentLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-2-TABLE OF CONTENTSIntroductionGeneral analytical techniques Gr

2、aphs Deflators Regression analysisSupply side analysis Cost structures Design differences Factor costs Scale,experience,complexity and utilization Supply curvesDemand side analysis Customer understanding-segmentation and“Discovery”-conjoint analysis-multi-dimensional scaling Price-volume curves and

3、elasticity Demand forecasting-technology/substitution curvesWrap-upLeft 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-ec

4、onomics-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 elementsLeft HeaderR

5、ight 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“Clas

6、sic”scientific rigorLeft 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

7、 analyze and objectifyQuantitative analysis is the basisLeft 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 hypot

8、heses 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 do

9、es not want to hear that“there is no data,”but rather what can be developed,in how much time,and at what costLeft 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 proce

10、ss 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 workDiscus

11、sions with John Tang and othersYou are expected to locate knowledge on your own initiativeLeft 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 specifi

12、c situationsValue management(CFROI,asset growth,etc.)Additionally,no tool can substitute for a new creative approachLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-9-TABLE OF CONTENTSIntroductionGeneral analytical techniques Graphs Deflators Regression an

13、alysisSupply side analysis Cost structures Design differences Factor costs Scale,experience,complexity and utilization Supply curvesDemand side analysis Customer understanding-segmentation and“Discovery”-conjoint analysis-multi-dimensional scaling Price-volume curves and elasticity Demand forecastin

14、g-technology/substitution curvesWrap-upLeft 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 graphMak

15、e the point as directly as possibleDemonstrate clear relevance to accompanying material and clients businessClearly label title,axes,and sourcesTailor graph to its audience and purposeExplorationPersuasionDocumentationLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All right

16、s 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 themLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Co

17、nsulting 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 scale01002003004000.010.1110100124816One CycleLinearLog

18、LogThe ratio of anything to zero is infinite.Zero cannot appear on a log scale.Left HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-13-DATA RELATIONSHIP DETERMINES SELECTION OF SCALEThree Scales Most CommonLinearLogLogLinearLinear(usually time)LogLinearSemi-Lo

19、gLog-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+bLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-14-WHEN SHOULD A LINEAR GRAPH

20、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 ratio19888990

21、91920510152025303540455055Market Share(%)Linear GraphHardwareSoftwareLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-15-1010010001920 1930 1940 1950 1960 1970 1980 1990 2000WHEN SHOULD A SEMI-LOG PLOT BE USED?Semi-log graphs are generally used to illustra

22、te constant growth rates,e.g.,Volume of sales growth over timeYearSource:Agricultural StatisticsU.S.Corn Yield(Bushels/Acre)R=.95Semi-Log GraphLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-16-WHEN SHOULD A LOG-LOG PLOT BE USED?Log-log graphs are general

23、ly 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=.6361,00010010Left HeaderRig

24、ht 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 proportionalLeft HeaderRight He

25、aderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-18-BUBBLE CHART EXAMPLECategory Growth Versus Gross Margin Versus Size-6-4-20246810121410152025303540451980-84Real CAGR(%)Gross Margin(%)=$1B salesConsumer ElectronicsToysHousewares/GiftsJewelrySportingGoodsSmallAppliancesCame

26、ra/PhotoSource:Discount MerchandiserLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-19-TABLE OF CONTENTSIntroductionGeneral analytical techniques Graphs Deflators Regression analysisSupply side analysis Cost structures Design differences Factor costs Scal

27、e,experience,complexity and utilization Supply curvesDemand side analysis Customer understanding-segmentation and“Discovery”-conjoint analysis-multi-dimensional scaling Price-volume curves and elasticity Demand forecasting-technology/substitution curvesWrap-upLeft HeaderRight HeaderShadow BoxSource:

28、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 ind

29、ex 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 1979Left HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-21-SELECT APPROPRIATE DEFLATOR

30、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(1

31、973 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 categoriesLeft HeaderRight HeaderShadow BoxSource:1994 Th

32、e 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 e

33、xchange rateLeft 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

34、.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)Left HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-24-TABLE OF CONTENTSIntroduc

35、tionGeneral analytical techniques Graphs Deflators Regression analysisSupply side analysis Cost structures Design differences Factor costs Scale,experience,complexity and utilization Supply curvesDemand side analysis Customer understanding-segmentation and“Discovery”-conjoint analysis-multi-dimensio

36、nal scaling Price-volume curves and elasticity Demand forecasting-technology/substitution curvesWrap-upLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-25-REGRESSION ANALYSIS IS A POWERFUL TOOL FORUNDERSTANDING RELATIONSHIP BETWEEN TWOOR MORE VARIABLESRegr

37、ession 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

38、portfolio of analytical techniquesCan be morass-“lose forest for trees”Left 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 relatio

39、nships1234PercentACV80%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 AnalysisLeft HeaderRight He

40、aderShadow 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

41、”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?Left HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-28-WHAT IS RELATIONSHIP BETWEEN X AND Y?PercentACVAnnual Number of Purchases

42、 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 n

43、egativeR2=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

44、)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 OutputLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group Al

45、l 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 re

46、lationship 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

47、.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 Variance

48、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 th

49、e 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 Squ

50、are(%)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-

51、080.192370.32876CoefficientsStandard Errort StatisticP-valueLower 95%Upper 95%Analysis of VarianceLeft 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 sim

52、ultaneouslyTo help distinguish cause from correlationDont use as“fishing expedition”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 Buyin

53、gAnnual 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,and

54、Purchase FreqencySources:Scantrack;IRI Marketing Factbook 1990;BCG AnalysisLeft 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 DealAver

55、age 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=.95Left HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Grou

56、p 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 correlation

57、sthink 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 val

58、ue 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 variableLeft HeaderRight HeaderShadow BoxSour

59、ce:1994 The Boston Consulting Group All rights reserved-35-TABLE OF CONTENTSIntroductionGeneral analytical techniques Graphs Deflators Regression analysisSupply side analysis Cost structures Design differences Factor costs Scale,experience,complexity and utilization Supply curvesDemand side analysis

60、 Customer understanding-segmentation and“Discovery”-conjoint analysis-multi-dimensional scaling Price-volume curves and elasticity Demand forecasting-technology/substitution curvesWrap-upLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-36-Define relevant c

61、ompetitive 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 aver

62、age costing for its own sakeWHY DO COST ANALYSIS?Left 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

63、,etc.Identify costs and expenses with individual models/product linesTherefore,competitive cost analysis involvesAllocation of variancesAllocation of expensesCapitalization of nonrecurring costs and expensesLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-

64、38-IN MOST SUPPLY SIDE ANALYSIS,FIRSTLAY OUT THE CLIENTS COST STRUCTUREFocus on Key Cost ElementsIndustry AIndustry BProfitOverheadSelling and DistributionVariable ManufacturingRaw MaterialsFixed Manufacturing8%8%16%18%40%10%8%10%35%11%18%18%GainRaw materialsSelling and distributionAdvantageBackward

65、 integrationRelated diversification to further Throughuse sales force?Purchasing scaleSales focus,toolsLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-39-COST DATA CAN BE FOUND IN CLIENTACCOUNTING SYSTEMS.Client accounting systems good forControl/audit of

66、 short-term evolutionNot for strategic analysisGenerally broken down by type of costDirectIndirectOverheadsEmphasis is on efficiency,not on understanding long-term cost dynamics as a function of scale,run length,etc.Left HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-40-.BUT OFTEN REQUIRES RECASTINGMaterials30Manufacturing costs40Direct15Indirect10Overheads15Commercial costs30Variable10Fixed20Total cost100Materials30Manufacturing costs40Metalworking15Pain