貝恩分析技能概要（overviewonanalysis）

1、A Primer on Analysis Overview Confidential Document,TABLE OF CONTENTS,Introduction General analytical techniques Graphs Deflators Regression analysis Supply side analysis Cost structures Design differences Factor costs Scale, experience, complexity and utilization Supply curves Demand side analysis

2、Customer understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up,LOGIC AND ANALYSIS CRITICAL TOSTRATEGY DEVELOPMENT,Key to strategy development is laying out “l(fā)ogic” to Underst

3、and what makes business work economics interactions across competitors, segments, time, . Conceptually organize client goals Devise ways to achieve clients goals Help client “make it happen” A tightly developed piece of this logic is analysis Reducing complex reality to a few salient points Isolatin

4、g important economic elements,ANALYSIS IS MORE THAN NUMBER CRUNCHING,Analysis is. Integrating quantitative and qualitative knowledge Seeing the bigger picture Thinking creatively conceptually Not . . . Endless calculations Letting statistics dictate/rule “Classic” scientific rigor,ANALYTICAL BIAS,“E

5、verything can be quantified” Not really, but Most “qualitative” effects are based in economics explicit or opportunity costs accurately quantifiable or not Client hires us to analyze and objectify Quantitative analysis is the basis,CREATIVITY AND ANALYTICAL PERSEVERANCE AREIMPORTANT TRAITS FOR SUPER

6、IOR ANALYSTS,Strive to address a problem using different approaches to test hypotheses and find inconsistencies Triangulate on answers Never believe a data series blindly Never stop at first obstacle Clients often stop short of good analysis because they quickly surrender in the absence of good, rea

7、dily available data We never surrender to the unavailability of data Your 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 analyst Answers look easy, but proc

8、ess of getting there painful Each problem somewhat different from examples A primer can Give flavor of expected analyses Show which analyses have been most productive historically Explain basic techniques and warn of common methodological errors Best training comes from Experience in project team wo

9、rk Discussions with John Tang and others You are expected to locate knowledge on your own initiative,DONT LIMIT YOURSELF TO THESE TOOLS,They are a sample of the most commonly used tools Others will be of use in specific situations Value management (CFROI, asset growth, etc.) Additionally, no tool ca

10、n substitute for a new creative approach,TABLE OF CONTENTS,Introduction General analytical techniques Graphs Deflators Regression analysis Supply side analysis Cost structures Design differences Factor costs Scale, experience, complexity and utilization Supply curves Demand side analysis Customer un

11、derstanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up,RELATIONSHIPS HAVE MOST IMPACT WHEN DISPLAYED VISUALLY,Graphs and charts should be easily understandable to a “nonquantita

12、tive” client Display one main idea per graph Make the point as directly as possible Demonstrate clear relevance to accompanying material and clients business Clearly label title, axes, and sources Tailor graph to its audience and purpose Exploration Persuasion Documentation,CHOOSE GRAPH SCALE THOUGH

13、TFULLY,Match chart boundaries to relevant range of the data as closely as possible Select scale to facilitate thinking about proposed relationships Use 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 dista

14、nce anywhere on the scale On a logarithmic scale, a given ratio of two values covers the same distance anywhere on the scale,1,2,4,8,16,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 M

15、ost Common,Linear,Log,Log,Linear,Linear (usually time),Log,Linear,Semi-Log,Log-Log,Constant Rate of Change,Constant Growth Rate,Constant “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

16、BE USED?,Linear graphs are best when the change in unit terms is of interest, e.g., Market share over time Profit margin over time Forty-five degree downward sloping lines on linear graph represent points whose x and y values have constant sum Rays through origin represent points with common ratio,M

17、arket Share (%),Linear Graph,Hardware,Software,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 time,Year,Source: Agricultural Statistics,U.S. Corn Yield (Bushels/ Acre),R=.95,Semi-Log Graph,WHEN SHOULD A L

18、OG-LOG PLOT BE USED?,Log-log graphs are generally used to plot “elasticities,” e.g., Price elasticity of demand Scale slope Forty-five degree downward sloping lines show points with common product,Salaried and Indirect hourly Employees/ Billion Impressions of Capacity,Printing Capacity (Billions of

19、Impressions),78% Scale Slope R=.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 axes Common examples include: Market size Assets Cost flow Circle area (not diameter) is proportional,BUBBLE CHART EXAMPLECategory Growth Versus

20、Gross Margin Versus Size,1980-84 Real CAGR (%),Gross Margin (%),= $1B sales,Consumer Electronics,Toys,Housewares/ Gifts,Jewelry,Sporting Goods,Small Appliances,Camera/ Photo,Source: Discount Merchandiser,TABLE OF CONTENTS,Introduction General analytical techniques Graphs Deflators Regression analysi

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

22、g technology/substitution curves Wrap-up,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 growth Deflating data removes some of the distortion Using a deflator index list,

23、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 1993 Deflator 1979,SELECT APPROPRIATE DEFLATOR DEPENDING ONTHE QUESTION YOURE TRYING TO ANSWER,G.N.P. deflator is best for expressing dollar

24、s in terms of average real value to the rest of the economy Current (variable) weights Measured quarterly C.P.I. is best only for expressing value in relation to consumer spending on a fixed market basket of goods (1973 base) Measured monthly Industry or product-specific indices are best for convert

25、ing dollars into measures of physical output Available from Commerce Dept. for broad industry categories Can be constructed from client or industry data for narrow categories,BE CAREFUL WHEN MIXING EXCHANGERATES AND INFLATION ACROSS COUNTRIES,First convert each countrys historical data to constant l

26、ocal currency E.g., Japan1993 yen W. Germany1993 DM U.S.A.1993 dollars Then 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.Year($M)(1987 = 1.00)Price ($)Transistor Price (),1987

27、7861.0001.001.05 19885951.033.92.72 19897301.075.99.49 19908331.119.98.34 19911,0621.161.90.24 19921,4231.193.98.18 19931,8381.2271.14.16,Reported sales $15.2%Real sales $11.4% I.C. unit sales8.9% Transistor sales52.4%,Growth Rates (per year),TABLE OF CONTENTS,Introduction General analytical techniq

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

29、curves and elasticity Demand forecasting technology/substitution curves Wrap-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 variables (indepe

30、ndent) Quantifies and validates relationships Is useful for prediction and causal explanation But . . . Must not substitute for clear independent thinking about a problem Use as single element in portfolio of analytical techniques Can be morass “l(fā)ose forest for trees”,ANY RELATIONSHIP BETWEEN VARIAB

31、LES X AND Y?,Used alone, graphical methods provide only qualitative and general inferences about relationships,Percent ACV,80%,70%,60%,50%,40%,30%,20%,10%,0%,Annual Number of Purchases by Consumer,X:Annual number of purchases by buyer Y:Percent ACV,Percent ACV is the volume weighted average percent

32、of grocery stores which carry the category. Sources: ScanTrack; IRI Marketing Factbook; BCG Analysis,REGRESSION ANALYSIS ANSWERS THESE QUESTIONS,What is relationship between X and Y How big an effect does X have on Y? What is the functional form? Is effect positive or negative? How strong is relatio

33、nship? 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?,WHAT IS RELATIONSHIP BETWEEN X AND Y?,Percent ACV,Annual Number of Purchases by Customer,Regression fits a straight line to the data

34、points Percent ACV = -0.2790 + 0.2606 annual purchases One more annual purchase will raise percent ACV by 0.2606 percentage points Slope 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 R0.83354 R Square

35、 (%)69.48 Adjusted R Square (%)68.35 Standard Error0.10394 Observations29,Regression Statistics,Regression10.664000.6640061.4641.98146E-08 Residual270.291680.01080 Total280.95568,Analysis of VariancedfSum of SquaresMean SquareFSignificant F,Intercept(0.27901)0.06286(4.439)0.00013(0.40799)(0.15003) X

36、10.260560.033247.8401.5372E-080.192370.32876,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 Y Simply calculated as sl

37、ope divided by its standard error Closer slope is to zero, and/or higher standard error (variability), the weaker the relationship A 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 relativel

38、y strong (i.e., roughly 85-95% confidence level). Under 1.5, relationship is weak.,Multiple R0.83354 R Square (%)69.48 Adjusted R Square (%)68.35 Standard Error0.10394 Observations29,Regression10.664000.6640061.4641.98146E-08 Residual270.291680.01080 Total280.95568,Regression Statistics,dfSum of Squ

39、aresMean SquareFSignificance F,Intercept(0.27901)0.06286(4.439)0.00013(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 tha

40、t is explained by the variables in the model - here just X Indicates overall how well model explains Y Based on how dispersed the data points are around the regression line R2 measured on scale of 0 to 100% 100% indicates perfect fit of regression line to the data points Low R2 indicates current mod

41、el does not fit the data well suggests there are other explanatory factors, besides X, that would help explain Y,Multiple R0.83354 R Square (%)69.48 Adjusted R Square (%)68.35 Standard Error0.10394 Observations29,Regression10.664000.6640061.4641.98146E-08 Residual270.291680.01080 Total280.95568,Regr

42、ession Statistics,dfSum of SquaresMean SquareFSignificance F,Intercept(0.27901)0.06286(4.439)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 SEVER

43、AL INFLUENCES,Use When several factors have an impact simultaneously To help distinguish cause from correlation Dont 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 pe

44、r Year,% ACV with Features and/or Displays,% ACV with Features and/or Displays,Brand Size ($M),Percent 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

45、Freqency,Sources: Scantrack; IRI Marketing Factbook 1990; BCG Analysis,OTHER REGRESSION EXAMPLES,Very Low R*,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

46、*,After Log Transformation*,*Sources: IRI Marketing Factbook; Certified Price Book; Nielsen; BCG Analysis *Source: Agricultural 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 req

47、uires thought Consider direction of causation What explanatory variables do I believe are appropriate to include? Avoid spurious correlationsthink independently about what factors are logical to include Avoid including explanatory variables that are highly correlated with each other Should the regre

48、ssion 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 outliers Look for form of data (linear, expon

49、ential, power, etc.) Do I have enough observations? Rough rule of thumb: 10 observations for each explanatory variable,TABLE OF CONTENTS,Introduction General analytical techniques Graphs Deflators Regression analysis Supply side analysis Cost structures Design differences Factor costs Scale, experie

50、nce, complexity and utilization Supply curves Demand side analysis Customer understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up,Define relevant competitive environment Basi

51、s of advantage (profit levers) Relative strengths/weaknesses of competitors Barrier to new competitors Effect of changes over time (technology, scale) Predict effect of one firms actions on Competitors (short term, reaction) Profit and cash flow of client Not Cost systems Correcting average costing

52、for its own sake,WHY DO COST ANALYSIS?,WHICH COSTS?,Competitive cost analysis Use actual costs, not standards Use fully absorbed costs, since expenses are often the most sensitive to scale/experience, etc. Identify costs and expenses with individual models/product lines Therefore, competitive cost a

53、nalysis involves Allocation of variances Allocation of expenses Capitalization of nonrecurring costs and expenses,IN MOST SUPPLY SIDE ANALYSIS, FIRSTLAY OUT THE CLIENTS COST STRUCTUREFocus on Key Cost Elements,Profit,Overhead,Selling and Distribution,Variable Manufacturing,Raw Materials,Fixed Manufa

54、cturing,8%,8%,16%,18%,40%,10%,8%,10%,35%,11%,18%,18%,GainRaw materialsSelling and distribution AdvantageBackward integrationRelated diversification to further Throughuse sales force? Purchasing scale Sales focus, tools,COST DATA CAN BE FOUND IN CLIENTACCOUNTING SYSTEMS . . .,Client accounting system

55、s good for Control/audit of short-term evolution Not for strategic analysis Generally broken down by type of cost Direct Indirect Overheads Emphasis is on efficiency, not on understanding long-term cost dynamics as a function of scale, run length, etc.,. . . BUT OFTEN REQUIRES RECASTING,Materials30

56、Manufacturing costs40 Direct15 Indirect10 Overheads15 Commercial costs30 Variable10 Fixed20 Total cost100,Materials30 Manufacturing costs40 Metalworking15 Painting8 Assembly12 Overheads5 Distribution costs7 Logistics5 Warehousing2 Selling costs9 Salesmen6 After-sales3 service Marketing costs10 Adver

57、tising3 Overheads7 G Dealerscope Merchandising; BCG Analysis,EXPERIENCE EFFECT CAN BE DIFFICULT TO MEASURE,Experience effect normally applies only to the value the firm adds to the product Cost allocation in multiproduct plant creates problems in measuring the experience effect Differences in factor

58、 costs make comparison difficult Inflation must be eliminated Significant changes in product design must be taken into account Relevant experience unit not always obvious,Complexity gives rise to unit costs that increase with the scope of activity Scope in manufacturing: parts, models, product lines

59、, etc. . . . Scope in administration: businesses, countries, etc. . . . Complexity often works against scale Example: the cost of connecting every two people in a communication network with a dedicated connection at $1 per connection 210.5510210454.5501,22524.51004,95049.5,COMPLEXITY COSTS ARISE FROM PROBLEMS ANDCOSTS INVOLVED IN COORDINATING MANY ACTIVITIES,NumberNumber of Connections of People ()(N)(N-1)/2Cost/Person ($),COMPLEXITY ARISES IN INDUSTRY DUE TOMANY FACTORS,Plant makes so ma