1、Left HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-1-A Primer on AnalysisOverviewConfidential Document bcA P r i me r o n A n a l y s i s b cLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-2-TABLE OF CONTENTSIntro
2、ductionGeneral 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-dimen
3、sional scaling Price-volume curves and elasticity Demand forecasting-technology/substitution curvesWrap-upT A B L E O F C O N T E N T S I n t r o d u c t i oLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-3-LOGIC AND ANALYSIS CRITICAL TOSTRATEGY DEVELOPME
4、NTKey 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 analysisReducin
5、g complex reality to a few salient pointsIsolating important economic elementsL O G I C A N D A N A L Y S I S C R I T I C A LLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-4-ANALYSIS IS MORE THAN NUMBER CRUNCHINGAnalysis is.Integrating quantitative and q
6、ualitative knowledgeSeeing the bigger pictureThinking-creatively-conceptuallyNot.Endless calculationsLetting statistics dictate/rule“Classic”scientific rigorA N A L Y S I S I S MO R E T H A N N U MBLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-5-ANALYTI
7、CAL 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 basisA N A L Y T I C A L B I A S“E v e r y t h i n g c aLeft HeaderRigh
8、t 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 dat
9、a 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 devel
10、oped,in how much time,and at what costC R E A T I V I T Y A N D A N A L Y T I C A L 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 pain
11、fulEach 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
12、 othersYou are expected to locate knowledge on your own initiativeWH E R E T H I S P R I ME R F I T S N o d oLeft 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 b
13、e of use in specific situationsValue management(CFROI,asset growth,etc.)Additionally,no tool can substitute for a new creative approachD O N T L I MI T Y O U R S E L F T O T HLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-9-TABLE OF CONTENTSIntroductionG
14、eneral 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-dimensional s
15、caling Price-volume curves and elasticity Demand forecasting-technology/substitution curvesWrap-upT A B L E O F C O N T E N T S I n t r o d u c t i oLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-10-RELATIONSHIPS HAVE MOST IMPACT WHEN DISPLAYED VISUALLYG
16、raphs 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 purposeExplor
17、ationPersuasionDocumentationR E L A T I O N S H I P S H A V E MO S T I MPLeft 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 facilitat
18、e thinking about proposed relationshipsUse same scale across charts if you intend to compare themC H O O S E G R A P H S C A L E T H O U G H T FLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-12-LINEAR VS.LOGOn a linear scale,a given difference between tw
19、o 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 CycleLinearLogLogThe ratio of anything to zero is infinite.Zero cannot appear on a log scale.L I N E A R V S.L O
20、 G O n a l i n e a r s cLeft 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 RateCons
21、tant“Elasticity”Given no prior expectation about the form of a relationship,plot it linearlyy=mx+blog y=mx+blog y=mlog x+bD A T A R E L A T I O N S H I P D E T E R MI N E SLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-14-WHEN SHOULD A LINEAR GRAPH BE US
22、ED?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 ratio1988899091920
23、510152025303540455055Market Share(%)Linear GraphHardwareSoftwareWH E N S H O U L D A L I N E A R G R A P HLeft 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
24、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 GraphWH E N S H O U L D A S E MI-L O G P L OLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All righ
25、ts 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 C
26、apacity(Billions of Impressions)78%Scale SlopeR=.6361,00010010WH E N S H O U L D A L O G-L O G P L O TLeft 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
27、 y axesCommon examples include:Market sizeAssetsCost flowCircle area(not diameter)is proportionalC I R C L E O R B U B B L E C H A R T S O FLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-18-BUBBLE CHART EXAMPLECategory Growth Versus Gross Margin Versus S
28、ize-6-4-20246810121410152025303540451980-84Real CAGR(%)Gross Margin(%)=$1B salesConsumer ElectronicsToysHousewares/GiftsJewelrySportingGoodsSmallAppliancesCamera/PhotoSource:Discount MerchandiserB U B B L E C H A R T E X A MP L EC a t e g oLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Cons
29、ulting Group All rights reserved-19-TABLE OF CONTENTSIntroductionGeneral analytical techniques Graphs Deflators Regression analysisSupply side analysis Cost structures Design differences Factor costs Scale,experience,complexity and utilization Supply curvesDemand side analysis Customer understanding
30、-segmentation and“Discovery”-conjoint analysis-multi-dimensional scaling Price-volume curves and elasticity Demand forecasting-technology/substitution curvesWrap-upT A B L E O F C O N T E N T S I n t r o d u c t i oLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights r
31、eserved-20-DEFLATORS CORRECT EFFECTS OF INFLATIONConverts Variables from“Nominal”to“Real”Time series data in dollars with high or widely fluctuating inflation rates distort picture of growthDeflating data removes some of the distortionUsing a deflator index list,currency data are multiplied by the r
32、atio of the base year deflator index to the data year deflator index,e.g.,1979 sales(1993$)=1979(1979$)x Deflator 1993Deflator 1979D E F L A T O R S C O R R E C T E F F E C T S OLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-21-SELECT APPROPRIATE DEFLATO
33、R DEPENDING ONTHE QUESTION YOURE TRYING TO ANSWERG.N.P.deflator is best for expressing dollars in terms of average real value to the rest of the economyCurrent(variable)weightsMeasured quarterlyC.P.I.is best only for expressing value in relation to consumer spending on a fixed market basket of goods
34、(1973 base)Measured monthlyIndustry or product-specific indices are best for converting dollars into measures of physical outputAvailable from Commerce Dept.for broad industry categoriesCan be constructed from client or industry data for narrow categoriesS E L E C T A P P R O P R I A T E D E F L A T
35、 O R Left HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-22-BE CAREFUL WHEN MIXING EXCHANGERATES AND INFLATION ACROSS COUNTRIESFirst convert each countrys historical data to constant local currencyE.g.,Japan1993 yenW.Germany1993 DMU.S.A.1993 dollarsThen conve
36、rt to single currency(dollars,for example)at fixed exchange rateB E C A R E F U L WH E N MI X I N G E X CLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-23-EXAMPLE:AN INTEGRATED CIRCUIT MANUFACTURERReported SalesG.N.P.DeflatorAverage I.C.Average I.C.Year(
37、$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)E X A MP L E:A N I N T E G R
38、 A T E D C I R CLeft HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-24-TABLE OF CONTENTSIntroductionGeneral analytical techniques Graphs Deflators Regression analysisSupply side analysis Cost structures Design differences Factor costs Scale,experience,complex
39、ity 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-upT A B L E O F C O N T E N T S I n t r o d u c t i oLeft Head
40、erRight 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(inde
41、pendent)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”R E G R E S S I O N A N A L Y S I S I S A
42、 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 relationships1234PercentACV80%70%60%50%40%30%20%10%0%Annual Number of Purcha
43、ses 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 AnalysisA N Y R E L A T I O N S H I P B E T WE E N V ALeft HeaderRight HeaderShadow BoxSource:19
44、94 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 mod
45、el work overall?How well have I explained Y in general?Are there other variables that I should be including?R E G R E S S I O N A N A L Y S I S A N S WE R S Left HeaderRight HeaderShadow BoxSource:1994 The Boston Consulting Group All rights reserved-28-WHAT IS RELATIONSHIP BETWEEN X AND Y?PercentACV
46、Annual 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 wheth
47、er 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 FInter
48、cept(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 OutputWH A T I S R E L A T I O N S H I P B E T WE ELeft
49、 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 r
50、elationshipA 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 Squar
