A Primer on Analysis Overview

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 understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 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 TO STRATEGY DEVELOPMENT Key to strategy development is laying out “logic” to Understand what makes business work economics interactions across competitors, segments, time, . . . . . . Conceptually organize client goals Devise ways to achieve client’s goals Help client “make it happen” A tightly developed piece of this logic is analysis Reducing complex reality to a few salient points Isolating important economic elements

Key to strategy development is laying out “logic” to

Understand what makes business work

economics

interactions across competitors, segments, time, . . . . . .

Conceptually organize client goals

Devise ways to achieve client’s goals

Help client “make it happen”

A tightly developed piece of this logic is analysis

Reducing complex reality to a few salient points

Isolating 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

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 “ Everything 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

“ Everything 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 ARE IMPORTANT TRAITS FOR SUPERIOR 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, readily 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

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, readily 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 process 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 work Discussions with John Tang and others You are expected to locate knowledge on your own initiative

No document can teach you to be a great analyst

Answers look easy, but process 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 work

Discussions with John Tang and others

You are expected to locate knowledge on your own initiative

DON’T 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 can substitute for a new creative approach

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 can 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 understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 understanding

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 “nonquantitative” client Display one main idea per graph Make the point as directly as possible Demonstrate clear relevance to accompanying material and client's business Clearly label title, axes, and sources Tailor graph to its audience and purpose Exploration Persuasion Documentation

Graphs and charts should be easily understandable to a “nonquantitative” client

Display one main idea per graph

Make the point as directly as possible

Demonstrate clear relevance to accompanying material and client's business

Clearly label title, axes, and sources

Tailor graph to its audience and purpose

Exploration

Persuasion

Documentation

CHOOSE GRAPH SCALE THOUGHTFULLY 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

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 distance anywhere on the scale On a logarithmic scale, a given ratio of two values covers the same distance anywhere on the scale¹ 1 2 4 8 16 One Cycle Linear Log Log ¹The ratio of anything to zero is infinite. Zero cannot appear on a log scale.

DATA RELATIONSHIP DETERMINES SELECTION OF SCALE Three Scales Most 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 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 Market Share (%) Linear Graph Hardware Software

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

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) R²=.95 Semi-Log Graph

Semi-log graphs are generally used to illustrate constant growth rates, e.g.,

Volume of sales growth over time

WHEN SHOULD A LOG-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 Impressions) 78% Scale Slope R²=.636 1,000 100 10

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

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

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 EXAMPLE Category Growth Versus 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 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 curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 understanding

segmentation and “Discovery”

conjoint analysis

multi-dimensional scaling

Price-volume curves and elasticity

Demand forecasting

technology/substitution curves

Wrap-up

DEFLATORS CORRECT EFFECTS OF INFLATION Converts 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, 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

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, 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

SELECT APPROPRIATE DEFLATOR DEPENDING ON THE QUESTION YOU’RE TRYING TO ANSWER G.N.P. deflator is best for expressing dollars 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 converting 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

G.N.P. deflator is best for expressing dollars 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 converting 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 EXCHANGE RATES AND INFLATION ACROSS COUNTRIES First convert each country’s historical data to constant local currency E.g., Japan—1993 yen W. Germany—1993 DM U.S.A.—1993 dollars Then convert to single currency (dollars, for example) at fixed exchange rate

First convert each country’s historical data to constant local currency

E.g., Japan—1993 yen

W. Germany—1993 DM

U.S.A.—1993 dollars

Then convert to single currency (dollars, for example) at fixed exchange rate

EXAMPLE: AN INTEGRATED CIRCUIT MANUFACTURER Reported Sales G.N.P. Deflator Average I.C. Average I.C. Year ($M) (1987 = 1.00) Price ($) Transistor Price (¢) 1987 786 1.000 1.00 1.05 1988 595 1.033 .92 .72 1989 730 1.075 .99 .49 1990 833 1.119 .98 .34 1991 1,062 1.161 .90 .24 1992 1,423 1.193 .98 .18 1993 1,838 1.227 1.14 .16 Reported sales $ 15.2% "Real" sales $ 11.4% I.C. unit sales 8.9% "Transistor" sales 52.4% Growth Rates (per year)

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 understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 understanding

segmentation and “Discovery”

conjoint analysis

multi-dimensional scaling

Price-volume curves and elasticity

Demand forecasting

technology/substitution curves

Wrap-up

REGRESSION ANALYSIS IS A POWERFUL TOOL FOR UNDERSTANDING RELATIONSHIP BETWEEN TWO OR MORE VARIABLES Regression analysis: Explains variation in one variable (dependent) using variation in one or more other variables (independent) 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 “ lose forest for trees”

Regression analysis:

Explains variation in one variable (dependent) using variation in one or more other variables (independent)

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

“ lose forest for trees”

ANY RELATIONSHIP BETWEEN VARIABLES X AND Y? Used alone, graphical methods provide only qualitative and general inferences about relationships Percent ACV¹ 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 ¹Percent ACV is the volume weighted average percent of grocery stores which carry the category. Sources: ScanTrack; IRI Marketing Factbook; BCG Analysis

Used alone, graphical methods provide only qualitative and general inferences about relationships

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 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?

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 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?

WHAT IS RELATIONSHIP BETWEEN X AND Y? Regression fits a straight line to the data 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 Percent ACV Annual Number of Purchases by Customer R 2 = 0.69 Multiple R 0.83354 R Square (%) 69.48 Adjusted R Square (%) 68.35 Standard Error 0.10394 Observations 29 Regression Statistics Regression 1 0.66400 0.66400 61.464 1.98146E-08 Residual 27 0.29168 0.01080 Total 28 0.95568 Analysis of Variance df Sum of Squares Mean Square F Significant F Intercept (0.27901) 0.06286 (4.439) 0.00013 (0.40799) (0.15003) X1 0.26056 0.03324 7.840 1.5372E-08 0.19237 0.32876 Coefficients Standard Error t Statistic P-value Lower 95% Upper 95% Sources: Scantrack; IRI Marketing Factbook (1990); BCG Analysis Microsoft Excel Regression Output

Regression fits a straight line to the data 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

HOW STRONG IS RELATIONSHIP? ‘ t-statistic’ measures how well X explains Y Simply calculated as slope 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 relatively strong (i.e., roughly 85-95% confidence level). Under 1.5, relationship is weak. Multiple R 0.83354 R Square (%) 69.48 Adjusted R Square (%) 68.35 Standard Error 0.10394 Observations 29 Regression 1 0.66400 0.66400 61.464 1.98146E-08 Residual 27 0.29168 0.01080 Total 28 0.95568 Regression Statistics df Sum of Squares Mean Square F Significance F Intercept (0.27901) 0.06286 (4.439) 0.00013 (0.40799) (0.15003) x1 0.26056 0.03324 7.840 1.5372E-08 0.19237 0.32876 Coefficients Standard Error t Statistic P-value Lower 95% Upper 95% Analysis of Variance

‘ t-statistic’ measures how well X explains Y

Simply calculated as slope 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 relatively strong (i.e., roughly 85-95% confidence level). Under 1.5, relationship is weak.

HOW WELL DOES MY MODEL WORK OVERALL? R 2 measures proportion of variation in Y that 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 R 2 measured on scale of 0 to 100% 100% indicates perfect fit of regression line to the data points Low R 2 indicates current model does not fit the data well suggests there are other explanatory factors, besides X, that would help explain Y Multiple R 0.83354 R Square (%) 69.48 Adjusted R Square (%) 68.35 Standard Error 0.10394 Observations 29 Regression 1 0.66400 0.66400 61.464 1.98146E-08 Residual 27 0.29168 0.01080 Total 28 0.95568 Regression Statistics df Sum of Squares Mean Square F Significance F Intercept (0.27901) 0.06286 (4.439) 0.00013 (0.40799) (0.15003) x1 0.26056 0.03324 7.840 1.5372E-08 0.19237 0.32876 Coefficients Standard Error t Statistic P-value Lower 95% Upper 95% Analysis of Variance

R 2 measures proportion of variation in Y that 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

R 2 measured on scale of 0 to 100%

100% indicates perfect fit of regression line to the data points

Low R 2 indicates current model does not fit the data well

suggests there are other explanatory factors, besides X, that would help explain Y

USE MULTIPLE REGRESSION TO SORT OUT EFFECTS OF SEVERAL INFLUENCES Use When several factors have an impact simultaneously To help distinguish cause from correlation Don’t use as “fishing expedition”

Use

When several factors have an impact simultaneously

To help distinguish cause from correlation

Don’t use as “fishing expedition”

MULTIPLE REGRESSION CAN ENHANCE PREDICTIVE 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) Percent of Households Buying Annual Number of Purchases/Year R²=.67 R²=.51 R²=.69 R²=.87 Predicted % ACV with Features and/or Displays Actual % ACV with Features and/or Displays Brand Size, Reach, and Purchase Freqency Sources: Scantrack; IRI Marketing Factbook 1990; BCG Analysis

OTHER REGRESSION EXAMPLES Very Low R²* Percent ACV 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: Agricultural Statistics R²=.64 R²=.002 R²=.95

QUESTIONS TO ASK BEFORE RUNNING A REGRESSION Which variable is the predictive (or dependent) variable? Often straightforward but sometimes requires thought Consider direction of causation What explanatory variables do I believe are appropriate to include? Avoid spurious correlations—think independently about what factors are logical to include Avoid including explanatory variables that are highly correlated with each other Should 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 outliers Look for form of data (linear, exponential, power, etc.) Do I have enough observations? Rough rule of thumb: 10 observations for each explanatory variable

Which variable is the predictive (or dependent) variable?

Often straightforward but sometimes requires thought

Consider direction of causation

What explanatory variables do I believe are appropriate to include?

Avoid spurious correlations—think independently about what factors are logical to include

Avoid including explanatory variables that are highly correlated with each other

Should 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 outliers

Look 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 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 understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 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 Basis of advantage (profit levers) Relative strengths/weaknesses of competitors Barrier to new competitors Effect of changes over time (technology, scale) Predict effect of one firm’s actions on Competitors (short term, reaction) Profit and cash flow of client Not Cost systems Correcting average costing for its own sake WHY DO COST ANALYSIS?

Define relevant competitive environment

Basis of advantage (profit levers)

Relative strengths/weaknesses of competitors

Barrier to new competitors

Effect of changes over time (technology, scale)

Predict effect of one firm’s actions on

Competitors (short term, reaction)

Profit and cash flow of client

Not

Cost systems

Correcting average costing for its own sake

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 analysis involves Allocation of variances Allocation of expenses Capitalization of nonrecurring costs and expenses

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 analysis involves

Allocation of variances

Allocation of expenses

Capitalization of nonrecurring costs and expenses

IN MOST SUPPLY SIDE ANALYSIS, FIRST LAY OUT THE CLIENT’S COST STRUCTURE Focus on Key Cost Elements Profit Overhead Selling and Distribution Variable Manufacturing Raw Materials Fixed Manufacturing 8% 8% 16% 18% 40% 10% 8% 10% 35% 11% 18% 18% Gain Raw materials Selling and distribution Advantage • Backward integration • Related diversification to further Through use sales force? • Purchasing scale • Sales focus, tools

COST DATA CAN BE FOUND IN CLIENT ACCOUNTING SYSTEMS . . . Client accounting systems 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.

Client accounting systems 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 Materials 30 Manufacturing costs 40 Metalworking 15 Painting 8 Assembly 12 Overheads 5 Distribution costs 7 Logistics 5 Warehousing 2 Selling costs 9 Salesmen 6 After-sales 3 service Marketing costs 10 Advertising 3 Overheads 7 G&A 4 Total cost 100 Materials 30 Manufacturing costs 40 Direct 15 Indirect 10 Overheads 15 Commercial costs 30 Variable 10 Fixed 20 Total cost 100 Accounting System Strategic Cost Elements

Materials 30

Manufacturing costs 40

Metalworking 15

Painting 8

Assembly 12

Overheads 5

Distribution costs 7

Logistics 5

Warehousing 2

Selling costs 9

Salesmen 6

After-sales 3

service

Marketing costs 10

Advertising 3

Overheads 7

G&A 4

Total cost 100

Materials 30

Manufacturing costs 40

Direct 15

Indirect 10

Overheads 15

Commercial costs 30

Variable 10

Fixed 20

Total cost 100

MANY VARIABLES AFFECT COSTS Materials Volume Location of suppliers Design Manufacturing Plant output Technology Experience Design Run length Complexity Factor costs Logistics Volume Drop size Selling Volume Number of outlets Marketing Volume Volume/brand

Materials

Volume

Location of suppliers

Design

Manufacturing

Plant output

Technology

Experience

Design

Run length

Complexity

Factor costs

Logistics

Volume

Drop size

Selling

Volume

Number of outlets

Marketing

Volume

Volume/brand

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 understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 understanding

segmentation and “Discovery”

conjoint analysis

multi-dimensional scaling

Price-volume curves and elasticity

Demand forecasting

technology/substitution curves

Wrap-up

DESIGN DIFFERENCES CAN BE A MAJOR DRIVER OF PRODUCT COST DIFFERENCES Affect raw material costs as well as manufacturing value added Usually requires a “teardown” of competitor products to understand real differences Requires client involvement design engineers manufacturing engineers purchasing agents

Affect raw material costs as well as manufacturing value added

Usually requires a “teardown” of competitor products to understand real differences

Requires client involvement

design engineers

manufacturing engineers

purchasing agents

FIRST STEP IS TO IDENTIFY DESIGN DIFFERENCES - 1 Example: Design Analysis—Torque Converters • 29 blades, .77mm thick • E-beam weld hub to shell • Roll tabbed • 18 blades • Die casting • Roller clutch • 2 needle thrust bearing • 31 blades—longer and thinner • Roll tabbed and staked • Hub part of stamping • .82mm • 8 springs • 4 big, 4 medium (nested) • Close to center • 3 lugs welded • 245 MM • 23.0 lbs. • 27 blades, .82mm thick • Rivet hub to shell (10 rivets) • Roll tabbed • 15 blades • Plastic • Roller clutch • 31 blades—shorter and fatter • Roll tabbed • Hub part of stamping • 1.04mm • 12 springs • Attached directly to cover • 4 studs welded • 235 MM • 22.8 lbs. Misc Data Turbine Stator Pump Damper Cover Model A Model B Design differences translate into cost differences

• 29 blades, .77mm thick

• E-beam weld hub to shell

• Roll tabbed

• 18 blades

• Die casting

• Roller clutch

• 2 needle thrust bearing

• 31 blades—longer and thinner

• Roll tabbed and staked

• Hub part of stamping

• .82mm

• 8 springs

• 4 big, 4 medium (nested)

• Close to center

• 3 lugs welded

• 245 MM

• 23.0 lbs.

• 27 blades, .82mm thick

• Rivet hub to shell (10 rivets)

• Roll tabbed

• 15 blades

• Plastic

• Roller clutch

• 31 blades—shorter and fatter

• Roll tabbed

• Hub part of stamping

• 1.04mm

• 12 springs

• Attached directly to cover

• 4 studs welded

• 235 MM

• 22.8 lbs.

FIRST STEP IS TO IDENTIFY DESIGN DIFFERENCES - 2 Example: Digital Line Card Comparisons 8 ports 2 transformers 2 custom ICs (DCPFs) No standard TTL ICs 2 layer PWB 1 253 discretes SM/TH 2 Time-slot interchanging Conferencing Gain control Parallel—serial conversion Sanity scanning Control channel interface 16 ports 1 transformer No custom ICs 11 standard TTL ICs 2 layer PWB (foreign sourced) 150 discretes All TH "Off-board" (More centralized) 16 ports 1 transformer 1 hybrid IC 3 custom ICs 46 standard TTL ICs 6 layer PWB 210 discretes SM/TH Gold fingers attached to PWB (no separate connector) "Off-board" (More centralized) Port interface with terminals Control switching Board overhead Other “on-board” functionality 1 Printed wiring board 2 Surface mount and through hole Major Function Client Competitor X Competitor Y

8 ports

2 transformers

2 custom ICs (DCPFs)

No standard TTL ICs

2 layer PWB 1

253 discretes

SM/TH 2

Time-slot interchanging

Conferencing

Gain control

Parallel—serial conversion

Sanity scanning

Control channel interface

NEXT, WORK WITH CLIENT PURCHASING AGENTS TO DETERMINE MATERIAL COSTS Example: Client Material Costs per Digital Port Are High Port Control Overhead Additional functionality Total material cost per board Ports Total material cost per port Cost index Cost index excluding functionality Function Client (8/board) Competitor X (16/board) Competitor Y (16/board) ¹Additional functionality assumed ²Only 32.65/card if redesigned digital card is assumed 47.76 24.58 34.79 51.85² 158.98 8 19.87 100 100 68.60 4.94 39.49 — 113.03 16 7.07 36 53 96.28 63.82¹ 76.06 — 236.16 16 14.76 74 110

Port

Control

Overhead

Additional functionality

Total material cost per board

Ports

Total material cost per port

Cost index

Cost index excluding functionality

DESIGN DIFFERENCES MAY SUGGEST FOCUS FOR COST REDUCTION EFFORTS Example: Cost Reduction of Additional Opportunity Appears in Control Unit, Digital Line Control unit Trunk modules Analog line Digital line Switch Total Telsets Total System 5,235 1,321 1,080 2,160 9,796 5,441 15,237 Component Client Competitor 3,046 1,770 1,140 1,376 7,332 6,072 13,404 Cost ($/Component)

Control unit

Trunk modules

Analog line

Digital line

Switch Total

Telsets

Total System

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 understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 understanding

segmentation and “Discovery”

conjoint analysis

multi-dimensional scaling

Price-volume curves and elasticity

Demand forecasting

technology/substitution curves

Wrap-up

FACTOR COSTS USUALLY DON’T REQUIRE ANALYTICAL TOOLS, BUT CAN RESULT IN DIFFERENT COST POSITIONS Factor cost differences can affect most elements of the cost structure Raw materials Energy Labor (direct and overhead) Capital Factor cost differences are generally additive or multiplicative and can be incorporated directly into the cost analysis

Factor cost differences can affect most elements of the cost structure

Raw materials

Energy

Labor (direct and overhead)

Capital

Factor cost differences are generally additive or multiplicative and can be incorporated directly into the cost analysis

FACTOR COST EXAMPLE Forest Products Industry, 1981 United States 14.95 4.73 3.11 2.24 1.11 3.30 Canada 13.95 2.97 2.54 2.24 0.96 2.77 Sweden 11.51 4.81 — — — 4.81 France 10.51 4.59 5.21 — — 4.60 Brazil 5.50 4.54 4.54 2.72 2.27 3.73 Labor Rate ($/Hour) Oil Gas Coal Other Average Energy Prices ($/MMBTU)

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 understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 understanding

segmentation and “Discovery”

conjoint analysis

multi-dimensional scaling

Price-volume curves and elasticity

Demand forecasting

technology/substitution curves

Wrap-up

SCALE, EXPERIENCE, COMPLEXITY, AND UTILIZATION HAVE DISTINCT COST EFFECTS - 1 Scale, experience and utilization tend to be confused All are conceptually separate Scale Relates unit cost or price to production volume Generally applies to machines or facilities of different sizes at a point in time Experience Relates unit cost or price to cumulative production Best to think in terms of entire industry experience over long periods Arises for a variety of economic reasons Is used a lot less frequently than you may think

Scale, experience and utilization tend to be confused

All are conceptually separate

Scale

Relates unit cost or price to production volume

Generally applies to machines or facilities of different sizes at a point in time

Experience

Relates unit cost or price to cumulative production

Best to think in terms of entire industry experience over long periods

Arises for a variety of economic reasons

Is used a lot less frequently than you may think

Complexity Relates unit cost to some measure of complexity Either over time over different facilities at a point in time Utilization Relates unit cost or profitability to utilization as a percentage of capacity Applies to different volumes or output from given facilities over time SCALE, EXPERIENCE, COMPLEXITY, AND UTILIZATION HAVE DISTINCT COST EFFECTS - 2

Complexity

Relates unit cost to some measure of complexity

Either

over time

over different facilities at a point in time

Utilization

Relates unit cost or profitability to utilization as a percentage of capacity

Applies to different volumes or output from given facilities over time

BCG SLOPE DESCRIBES THE RELATIONSHIP BETWEEN UNIT COST AND VOLUME BCG Slope Equals Percent of Base Remaining When Independent Variable Doubled Scale Two similar facilities with comparable utilization, but one four times the production of the other Unit cost of smaller facility $10.00 and larger facility $8.10 “ Slope” = 90% Experience Cumulative output increases from 100K units to 200K units Unit cost falls from $1.20 to $.97 “ Slope” = 81% Utilization Output increases from 750 to 1,500 Amortizable fixed costs of $5M Fixed cost per unit falls from $6,667 to $3,333 “ Slope” = 50%

Scale

Two similar facilities with comparable utilization, but one four times the production of the other

Unit cost of smaller facility $10.00 and larger facility $8.10

“ Slope” = 90%

Experience

Cumulative output increases from 100K units to 200K units

Unit cost falls from $1.20 to $.97

“ Slope” = 81%

Utilization

Output increases from 750 to 1,500

Amortizable fixed costs of $5M

Fixed cost per unit falls from $6,667 to $3,333

“ Slope” = 50%

b is Slope (negative) of log Y = a - b Log X Elasticity (negative) of Y with respect to X BCG slope = 2 -b Therefore log BCG slope log 2 FROM MATHEMATICAL SLOPE TO BCG’S SLOPE AND VICE VERSA BCG Slope Value of b 90 .152 80 .322 70 .515 Note: You can use log (base 10) or ln (base e). Answers are unaffected. BCG Slope Takes Log-Log Form BCG Slope Mathematically Implies b Value -b =

b is

Slope (negative) of log Y = a - b Log X

Elasticity (negative) of Y with respect to X

BCG slope = 2 -b

Therefore

log BCG slope

log 2

-b =

CALCULATING NEW COST FROM OLD COST AND VOLUMES Example Old Cost BCG Slope Old Volume New Volume New Cost 100 70% 4 7 ? Or Y t + 1 = Y t X t + 1 X t log BCG Slope log 2 Y t + 1 = Y t X t + 1 X t ( -b )

CALCULATING BCG SLOPE FROM COSTS AND VOLUMES Example Old Cost 100 New Cost 60 Old   Volume 4 New  Volume 10 Slope ? BCG Slope = Antilog Yt + 1 Yt log 2 * Xt + 1 Xt log log

SCALE MEANS COST PER UNIT IS LOWER FOR LARGER SUPPLIERS Typically Charted on Log vs. Log Cost/Unit Volume 100% Slope 75% Slope 50% Slope Economies of Scale Observed in Most Cost Structure Elements Element Manufacturing scale Automated line Job shop Advertising Television Direct mail Selling Fragmented customers Concentrated customers Engineering Standard product Custom product Effect of Scale High Low High Low High Low High Low Example Engine blocks Assembled components Food Mail-order specialty apparel Books to bookstore Truck components Automobiles Hybrid microelectronics

Element

Manufacturing scale

Automated line

Job shop

Advertising

Television

Direct mail

Selling

Fragmented customers

Concentrated customers

Engineering

Standard product

Custom product

SCALE EXAMPLES Paper Machine Labor Advertising Costs Man-hours/ Ton 1.0 0.8 0.6 0.4 0.2 40 60 100 200 400 800 58% BCG Slope Advertising/ Sales 0.05 0.04 0.03 0.02 0.01 8 16 24 32 Machine Capacity (TPD) Sales ($M) 60% BCG Slope

LARGE SCALE PLANTS HAVE A SIGNIFICANT UNIT COST ADVANTAGE Overhead Cost/ 000 Equivalent 32’s ($) 25 20 15 10 7 500 1,000 2,000 4,000 Annual Capacity (000 Equivalent 32’s) Danville Old Saybrook Los Angeles Glasgow Mattoon 70% Slope

HOWEVER, THINK BEYOND THE DATA First Cut of Data Would Show Wide Dispersion Salaried and Indirect Employees/ Sales ($M) 10.0 5.0 3.0 2.0 1.0 0.5 2 5 10 20 50 100 200 100 30 Plant Sales/Product Family ($M) = Traditional Approach = Cost-based Management Approach = Time-based Management Approach Source: BCG Analysis 70 Automotive Component Suppliers

DIFFERENT VOLUME LEVELS CAN JUSTIFY SUPERIOR TECHNOLOGIES - 1 Hydraulic Component Casting Price/ Unit 40 20 10 8 6 4 50 100 500 1,000 5,000 Monthly Volume Note: Includes full amortization of tooling costs Slope across technologies 75% Slope within technologies 90-95% Sand Mold Gravity Die Cast/Single Die High Pressure Die Cast Twin Dies

DIFFERENT VOLUME LEVELS CAN JUSTIFY SUPERIOR TECHNOLOGIES - 2 Conventional Lathes by Mechanical Design Machining Cost/Piece Manual Universal Machine Manual Copy Lathe Manual Chucker Automatic Single Spindle Automatic Multispindle Lot Size

EXPERIENCE RELATES UNIT COST TO CUMULATIVE VOLUME - 1 Experience is an empirical observation about very long-term price behavior for manufactured goods and services Trend line around which there is significant deviation Driven by technology improvements and changes In both primary production processes and secondary processes (converting) Necessary to understand components of cost to project evolution of prices Indicator of competitive cost differences Experience and scale often interact, but are not the same Proper experience analysis should adjust for scale effects

Experience is an empirical observation about very long-term price behavior for manufactured goods and services

Trend line around which there is significant deviation

Driven by technology improvements and changes

In both primary production processes and secondary processes (converting)

Necessary to understand components of cost to project evolution of prices

Indicator of competitive cost differences

Experience and scale often interact, but are not the same

Proper experience analysis should adjust for scale effects

EXPERIENCE RELATES UNIT COST TO CUMULATIVE VOLUME - 2 Experience curves are most often modeled by a logarithmic relation: Log [UC] = b log [V] + a where V = cumulative volume UC = unit cost The “slope” of an experience curve is interpreted as “BCG slope” Calculated in a similar way to scale slope BCG slope = Antilog log UC UC * log 2 log V V 2 1 2 1

Experience curves are most often modeled by a logarithmic relation:

Log [UC] = b log [V] + a

where V = cumulative volume UC = unit cost

The “slope” of an experience curve is interpreted as “BCG slope”

Calculated in a similar way to scale slope

BCG slope =

WHY THE RELATIONSHIP WORKS Many factors work together to reduce real costs over time Increased purchasing scale (quantity discounts) Increased productivity Increased scale of facility Increased substitution of capital for labor Technology evolution Costs don’t just “come down,” they are managed down

Many factors work together to reduce real costs over time

Increased purchasing scale (quantity discounts)

Increased productivity

Increased scale of facility

Increased substitution of capital for labor

Technology evolution

Costs don’t just “come down,” they are managed down

IMPLICATIONS OF EXPERIENCE If prices decline, then costs must also decline over time The dynamic of constant change in business competition As a result of changing costs, different competitors will have different costs at any given time Different cost positions will generate different levels of profitability Also influenced by price realization Project competitive implications of above

If prices decline, then costs must also decline over time

The dynamic of constant change in business competition

As a result of changing costs, different competitors will have different costs at any given time

Different cost positions will generate different levels of profitability

Also influenced by price realization

Project competitive implications of above

WHOSE GROWTH DETERMINES COST/PRICE EVOLUTION: CHOICE OF EXPERIENCE BASES Price data can be plotted against different experience bases: The industry’s The technology’s The company’s The leader’s The fastest growing competitor’s Etc. Correct choice depends on: Economics of the business Competitive dynamics

Price data can be plotted against different experience bases:

The industry’s

The technology’s

The company’s

The leader’s

The fastest growing competitor’s

Etc.

Correct choice depends on:

Economics of the business

Competitive dynamics

EXPERIENCE EXAMPLE: LEARNING RESTRICTED WITHIN COMPANIES Direct Cost/ MW 380 340 300 260 5 15 50 Allis-Chalmers Westinghouse General Electric Firm Cumulative Megawatts (M) Direct Costs Per Megawatt Steam Turbine Generators 1946-1963

EXPERIENCE EXAMPLE: LEARNING IS SHARED BY ENTIRE INDUSTRY Crushed and Broken Limestone Prices 1.5 2 3 4 5 6 7 8 10 Industry Accumulated Experience (B Tons) 1929 1938 1945 1952 1971 80% Slope 2.50 2.00 1.50 Price/Ton ($ Constant)

IN CONSUMER ELECTRONICS, PRICE DECLINES 15-35% EACH TIME CUMULATIVE VOLUME DOUBLES Price Experience Curve Price (Constant 1989 $) 10,000 1,000 100 10 1 0.1 1 10 100 1,000 Slope* (%) Years Portable Color TV 76 68-89 VCR 83 76-89 Handheld Calculator 64 74-84 Digital Watch 75 74-84 Cellular Telephone 77 85-89 Answering Machine 78 75-89 Cumulative Volume (M) *For each doubling of cumulative volume, unit prices fall by (100 - slope)% Sources: Merchandising; 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 costs make comparison difficult Inflation must be eliminated Significant changes in product design must be taken into account Relevant experience unit not always obvious

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 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, 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 $1 per connection 2 1 0.5 5 10 2 10 45 4.5 50 1,225 24.5 100 4,950 49.5 COMPLEXITY COSTS ARISE FROM PROBLEMS AND COSTS INVOLVED IN COORDINATING MANY ACTIVITIES Number Number of Connections of People (~) [(N)(N-1)/2] Cost/Person ($)

Complexity gives rise to unit costs that increase with the scope of activity

Scope in manufacturing: parts, models, product lines, 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

2 1 0.5 5 10 2 10 45 4.5 50 1,225 24.5 100 4,950 49.5

COMPLEXITY ARISES IN INDUSTRY DUE TO MANY FACTORS Plant makes so many products that machines spend substantial time changing over between products Salesmen sell too many products to master any one of them properly Multiproduct plant has high administrative costs of coordination and tracking

Plant makes so many products that machines spend substantial time changing over between products

Salesmen sell too many products to master any one of them properly

Multiproduct plant has high administrative costs of coordination and tracking

COMPLEXITY EXAMPLES Machine Manufacturing Other Manufacturing Indirect Cost (% of Total Cost) 139% Indirect Cost (% of Total Cost) 30 20 10 5 5 10 20 40 50 30 20 10 4 5 6 8 10 15 20 30 40 50 # of Product Families Produced # of Models Source: BCG Interviews and Analysis 8 Factories

SCALE AND COMPLEXITY TYPICALLY WORK AGAINST EACH OTHER Significant Value in Learning How to Manage Complexity Overhead Cost/Unit Volume Combined Impact Complexity Impact Reduced Complexity Cost Scale Impact

UTILIZATION MEANS UNIT COSTS ARE LOWER WHEN CAPACITY IS FULL Utilization is important when Capital intensity is high Energy consumption is major part of costs Startup costs are high Labor force is not flexible Different from scale Frequently the two phenomena interact

Utilization is important when

Capital intensity is high

Energy consumption is major part of costs

Startup costs are high

Labor force is not flexible

Different from scale

Frequently the two phenomena interact

UTILIZATION EXAMPLES Health Care Services Printing Presses Cost/ Procedure ($) 140 40 80 60 100 20 16 12 2 3 4 5 20 Number of Professionals/Office Note: Assumes 2,000,000 Run Length Fully Loaded Cost ($/1,000 32s) Daily Procedures/ Professional 5 10 15 20 New Technology Standard Gravure Aggressive Gravure

UTILIZATION AND SCALE - 1 Glasswool Smelting Cost Index/t 300 200 150 100 75 3 5 10 20 50 100% 50 kt 100% 25 kt 50% 50% Capacity Capacity Utilization Annual Production (kt) Source: Client’s Database and Simulations

UTILIZATION AND SCALE - 2 Cost Added (¢/Lb) 50 40 30 25 20 15 10 1,000 2,000 3,000 4,000 5,000 6,000 10,000 8,000 Total Monthly Shipments (000 Lbs) Guelph 78% Columbiana 78% Stillwater 75% 90% Scale Slope Note: Costs adjusted for wage and energy factor cost differences

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 understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 understanding

segmentation and “Discovery”

conjoint analysis

multi-dimensional scaling

Price-volume curves and elasticity

Demand forecasting

technology/substitution curves

Wrap-up

SUPPLY CURVES DESCRIBE THE CASH COST POSITION OF COMPETITION IN COMMODITY INDUSTRIES Used to make predictions for an industry about Price Entry and exit Profitability Used mainly for commodity industries where all producers make the same product and there is one market price Display the sum of the components of competitive cost position across all competitors Vertical axis, displaying cash cost per unit of production versus Horizontal axis, laying out each competitor’s capacity, in some appropriate measure of capacity by plant by machine

Used to make predictions for an industry about

Price

Entry and exit

Profitability

Used mainly for commodity industries where all producers make the same product and there is one market price

Display the sum of the components of competitive cost position across all competitors

Vertical axis, displaying cash cost per unit of production versus

Horizontal axis, laying out each competitor’s capacity, in some appropriate measure of capacity

by plant

by machine

COMMENTS ON THE SUPPLY CURVE The return on assets is not necessarily the highest for the left-most facility Can often follow a humped pattern In using the supply curve to predict prices and exit upon the entry of new capacity, consider the following Marginal firms may resist exit because their assets are nontransferable to other industries The industry demand curve, often assumed to be inelastic in the short term, may be elastic in the long term BCG supply curve assumes Each firm operates at full capacity Marginal costs for a firm equal variable cash costs per unit of volume

The return on assets is not necessarily the highest for the left-most facility

Can often follow a humped pattern

In using the supply curve to predict prices and exit upon the entry of new capacity, consider the following

Marginal firms may resist exit because their assets are nontransferable to other industries

The industry demand curve, often assumed to be inelastic in the short term, may be elastic in the long term

BCG supply curve assumes

Each firm operates at full capacity

Marginal costs for a firm equal variable cash costs per unit of volume

BCG’S SUPPLY CURVE IS AKIN TO THE SUPPLY CURVE OF NEOCLASSICAL MICROECONOMICS Assumptions Each firm i maximizes profit by taking P as given and choosing V i so that MC i = P Industry supply curve is horizontal sum of each firm’s marginal cost curve V s = V 1 + . . . + V i + . . . + V n Price is determined by interaction of industry supply and demand curves Firm’s Marginal Cost Curves MC i MC i MC n V i V i V n Volume V s Industry Demand Curve Industry Supply Curve Price, Cost P

Assumptions

Each firm i maximizes profit by taking P as given and choosing V i so that MC i = P

Industry supply curve is horizontal sum of each firm’s marginal cost curve

V s = V 1 + . . . + V i + . . . + V n

Price is determined by interaction of industry supply and demand curves

PAPER INDUSTRY SUPPLY CURVE Cost ($/Ton) Annual Capacity

SUPPLY CURVE CAN BE USED TO EXPLAIN PROFITABILITY . . . Cost ($/Ton) Annual Capacity Price Industry Demand Contribution toward fixed costs and profit

. . . AND TO PREDICT PRICES AND ENTRY/EXIT BY FIRMS Industry Demand Curve Industry Demand Curve If new firm enters, price drops to P 1 New firm enters only if contribution A covers fixed costs + expected return Marginal firms drop out if new firm enters P 0 P 0 A P 1 New Entrant Quantity Q 0

If new firm enters, price drops to P 1

New firm enters only if contribution A covers fixed costs + expected return

Marginal firms drop out if new firm enters

HOWEVER, DON’T LET IT LEAD YOU TO NONSENSICAL RESULTS . . . Boston’s Most Prestigious Hospitals Are High Cost $/Patient Day 1,000 900 800 700 600 500 400 300 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Cumulative Beds (000) Sources: AHA; Local Interviews; BCG Analysis Will they be first to exit as market shrinks?

RATHER USE IT TO UNDERSTAND MARKET FURTHER Boston’s Hospital Segments Have Both High/Low Cost Suppliers $/Patient Day Cumulative Beds (000) Source: BCG Analysis Basic Secondary Aspirant Tertiary Tertiary

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 understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 understanding

segmentation and “Discovery”

conjoint analysis

multi-dimensional scaling

Price-volume curves and elasticity

Demand forecasting

technology/substitution curves

Wrap-up

FROM PRODUCT TO MARKET FOCUS: UNDERSTANDING THE CUSTOMER See the Market Through the Eyes of the Customer “ I wish,” “I want,” “I need,” “I hope,” answered with new products and services can substantially increase revenue, profit growth and organizational vitality Customer Need Implication Concept Screen Development Execution

“ I wish,” “I want,” “I need,” “I hope,” answered with new products and services can substantially increase revenue, profit growth and organizational vitality

WHAT KINDS OF INFORMATION ARE IMPORTANT FOR UNDERSTANDING CUSTOMERS? Customer profitability Customer economics Key drivers Client impact Attribute values and tradeoffs Satisfactions/dissatisfactions/product gaps Buying process Usage Switching costs and substitution Shifts in purchasing

Customer profitability

Customer economics

Key drivers

Client impact

Attribute values and tradeoffs

Satisfactions/dissatisfactions/product gaps

Buying process

Usage

Switching costs and substitution

Shifts in purchasing

Aggregate Cumulative customers/cumulative revenue Fully loaded cost to serve Cumulative customers/cumulative profit Value of attributes/tradeoffs Satisfactions/dissatisfactions End-user usage/dissatisfactions Overall product development requirements Major segment Segmentation dimensions Segment sizing Segment attractiveness Segment cost to serve Segment economics Segment product development requirements Segment of one Value of a customer Value of company to customer Key business drivers Opportunities for expansion Satisfactions/dissatisfactions/needs Relationship management Product development requirements CUSTOMERS CAN BE UNDERSTOOD ON THREE LEVELS Internal financial records Cost deaveraging, benchmarking, mapping Interviews, focus groups, structured surveys, conjoint Interviews, plant visits, usable tests, surveys, focus groups Interviews, focus groups, structured surveys D&B, trade associations, SIC code databases, interviews Internal financial/translation data, interviews, public financial data Interviews, perceptual mapping (MDS) Internal financial records, cost deaveraging, customer database Discovery, interviews, plant visits Interviews, customer database, Discovery Level Key Issues Source of Data Analysis often conducted concurrently at three levels

Aggregate Cumulative customers/cumulative revenue

Fully loaded cost to serve

Cumulative customers/cumulative profit

Value of attributes/tradeoffs

Satisfactions/dissatisfactions

End-user usage/dissatisfactions

Overall product development requirements

Major segment Segmentation dimensions

Segment sizing

Segment attractiveness

Segment cost to serve

Segment economics

Segment product development requirements

Segment of one Value of a customer

Value of company to customer

Key business drivers

Opportunities for expansion

Satisfactions/dissatisfactions/needs

Relationship management

Product development requirements

COST-TO-SERVE SCHEMATIC Margin Distributor Support Local Account Managers Technical Support Customer Service/Support District G&A Development COGS Industrial Sales

UNDERSTANDING THE CUSTOMER—AGGREGATE EXAMPLES Cumulative sales (%) Cumulative variable margin (%) Cumulative fully allocated profit (%) # of customers # of customers # of customers Satisfaction index (Top 2 Box) Five major drivers of customer satisfaction; Two are software related... ...with significant opportunity for improvement User-friendly Documentation Compatability Bug-free Relative importance (SBW) Major Dissatisfactions 100% FAP = $446K Reliability Durability Features/Functions Warranty Programming software dunc. 1-27-94/srd

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 understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 understanding

segmentation and “Discovery”

conjoint analysis

multi-dimensional scaling

Price-volume curves and elasticity

Demand forecasting

technology/substitution curves

Wrap-up

WHY DO SEGMENTATION? No such thing as a homogeneous market Different customer needs Different structural economics Find the most attractive customer sets Quantify and prioritize Develop an action plan for each targeted “cell” Find new “space” for successive product launches Use as the foundation for sustained, profitable growth Increasingly “granular” as a company builds its capability set

No such thing as a homogeneous market

Different customer needs

Different structural economics

Find the most attractive customer sets

Quantify and prioritize

Develop an action plan for each targeted “cell”

Find new “space” for successive product launches

Use as the foundation for sustained, profitable growth

SEGMENTATION GOAL IS TO MAXIMIZE COMPETITIVE ADVANTAGE Accounting systems and conventional industry classifications rarely provide sufficient quantifications of segment size, growth rate, share, etc. Good segmentation schemes explain client performance and competitive positioning Segmentation schemes that cannot be tied to a program of client actions are useless

Accounting systems and conventional industry classifications rarely provide sufficient quantifications of segment size, growth rate, share, etc.

Good segmentation schemes explain client performance and competitive positioning

Segmentation schemes that cannot be tied to a program of client actions are useless

MARKET SEGMENTATION EXAMPLE - 1 Purchase Related Local or national customer with local decision- making authority National customers with central/HQ decision-making authority Complex systems Simple systems Existing client site New site, or weak competitor Strong competitor site Existing client site New site, or weak competitor Existing client account Purchase Criteria: Vendor loyalty, product performance Product performance, price Vendor loyalty, product performance Vendor loyalty, price Price Vendor loyalty, price Vendor loyalty Vendor loyalty Other* Preferred vendor * Mixed sites, likely to be moving to one preferred vendor Strong competitor site Strong competitor account

MARKET SEGMENTATION EXAMPLE - 2 Customer Related Companies within an industry (across some) behave similarly and have essentially the same needs Application expense drives purchase Customers looking for full solutions Large customers require greater resources and concessions than small customers Differing purchase criteria OEMs have different, requirements, criteria, and needs than end users Centralized customers are more price and specification driven than decentralized customers Engineering-oriented customers are specification focused and value technical sales capability Nonengineering oriented rely on OEMs and Sis for specification Pull through versus push Easier/cheaper to sell to our customers Marketing and sales structure Channel strategy Packaging and promotions Product development Product development Pricing/bundling strategy Marketing structure (resource deployment) Channel deployment Pricing/POV strategy Product development Marketing structure Channel deployment Pricing strategy Channel deployment Pricing Channel deployment Support/marketing support Product packaging (full solutions) Pricing/deployment By Industry By Application Large versus Small OEM versus End User Centralized versus Decentralized High Internal Engineering Capability versus Limited Engineering Capability Our Customer versus Competitor's Traditional Nontraditional Segmentation Dimension Description Has Impact On

Companies within an industry (across some) behave similarly and have essentially the same needs

Application expense drives purchase

Customers looking for full solutions

Large customers require greater resources and concessions than small customers

Differing purchase criteria

OEMs have different, requirements, criteria, and needs than end users

Centralized customers are more price and specification driven than decentralized customers

Engineering-oriented customers are specification focused and value technical sales capability

Nonengineering oriented rely on OEMs and Sis for specification

Pull through versus push

Easier/cheaper to sell to our customers

Marketing and sales structure

Channel strategy

Packaging and promotions

Product development

Product development

Pricing/bundling strategy

Marketing structure (resource deployment)

Channel deployment

Pricing/POV strategy

Product development

Marketing structure

Channel deployment

Pricing strategy

Channel deployment

Pricing

Channel deployment

Support/marketing support

Product packaging (full solutions)

Pricing/deployment

By Industry

By Application

Large versus Small

OEM versus End User

Centralized versus Decentralized

High Internal Engineering Capability

versus Limited Engineering Capability

Our Customer versus Competitor's

MARKET SEGMENTATION EXAMPLE - 3 Cost-to-Serve Related High 25% 36% Low 25% 16% Low High Share by Segment Production Economics “ Cost to Manufacture” % Newsstand Distribution Economics “Cost-to-Serve” Annual Magazine Volume

SEGMENTS ARE FREQUENTLY THE COMPOSITE OF SEVERAL DIMENSIONS “Decision Tree” Segmentation Automotive Food Pharmaceutical Hydrocarbon Centralized Decentralized Centralized Decentralized Centralized Decentralized Centralized Decentralized High Low High Low High Low High Low High Low High Low High Low High Low Ours Theirs Our Theirs Ours Theirs Ours Theirs Ours Theirs Ours Theirs Ours Theirs Ours Theirs Ours Theirs Our Theirs Ours Theirs Ours Theirs Ours Theirs Ours Theirs Ours Theirs Ours Theirs Example: Centralized vs. High vs. Low Our Customer Industry Decentralized Eng. Capability vs. Competitor Dimension One Dimension Two Dimension Three Dimension Four

SEGMENTATION: BASIC DATA REQUIREMENTS - 1 What are the relevant market segments? Who are the key players within each market segment? For each segment and subsegment What is the attractiveness? size, growth rates, margins, current penetration levels, share, etc. What are the economics to serve (vis-a-vis competitors)? costs to sell, project win rates, life cycle economics What are the key success factors? key purchase criteria unmet needs—products, marketing, distribution, support, etc.

What are the relevant market segments?

Who are the key players within each market segment?

For each segment and subsegment

What is the attractiveness?

size, growth rates, margins, current penetration levels, share, etc.

What are the economics to serve (vis-a-vis competitors)?

costs to sell, project win rates, life cycle economics

What are the key success factors?

key purchase criteria

unmet needs—products, marketing, distribution, support, etc.

SEGMENTATION: BASIC DATA REQUIREMENTS - 2 The previous three dimensions of segment analysis (attractiveness, economics to serve, key success factors) can now be rolled together to create an overall segment prioritization High Segment Attractiveness Low Economics to Serve Poor Good Segment Prioritization 1. C 2. D 3. B 4. I 5. E 6. H 7. A 8. F 9. G Key Success Factor Requirements Most attractive Least attractive    A E B D C F G I H          Furthermore, beyond just prioritizing across market segments, there is now enough information to optimize performance within each segment via the levers of price, mix, share, and expenses

The previous three dimensions of segment analysis (attractiveness, economics to serve, key success factors) can now be rolled together to create an overall segment prioritization

SEGMENTATION IS ALSO A CREATIVE ART WITH MANY APPLICABLE FRAMEWORKS User target Psychographic (personality, behavior, socialization) Demographic (age, income, lifestyle, family structure) Geographic Customer attitudes Current behavior and beliefs Target substitutions Occasion Product attributes Real Perceptual Others Variety, ease of use, cost, system attributes

User target

Psychographic (personality, behavior, socialization)

Demographic (age, income, lifestyle, family structure)

Geographic

Customer attitudes

Current behavior and beliefs

Target substitutions

Occasion

Product attributes

Real

Perceptual

Others

Variety, ease of use, cost, system attributes

QUESTION How would you understand usage behavior and dissatisfactions for laundry products (stain remover)? Qualitative? Quantitative?

How would you understand usage behavior and dissatisfactions for laundry products (stain remover)?

Qualitative?

Quantitative?

ANSWER: 50 IN-HOME INTERVIEWS WATCHING CONSUMERS SORT, TREAT, WASH, AND DRY LAUNDRY Clothes Worn Stain Occurs SSR Place Items in Hamper or Closet Laundry Gathered, Taken to Laundry Room Check for Stains Dry Clean Set Machine Controls and Start Water Pretreat, Presoak Stained Items Add Detergent Add Clothes Check Clothes at End of Cycle Stain Gone? Dryer/ Clothes Line Back in Wash Check Stain Dryer/ Clothes Line Evaluated Results Social/ School ("1") Around the House/ Play ("2-3") "Rag Bag (> "4") Re-treat • Presoak • Special treatment • SSR Add Other Products Pretreat, Presoak Stained Items Presoak Treat Immedi- ately Sort Laundry Yes "My best clothes-something I really like or care about? Intervention points in current laundry protocol define the opportunity and provide options for stream of new products Occurs some of the time Occurs most of the time In-Homes Show Four Intervention Points in Current Laundry Protocol • Treat immediately  presoak  treat at beginning of wash cycle  retreat • Segment by load - by new highly valued - by wearer: husband, kids, own - by fabric - towels are separate - colors - jeans, lights, delicates • Perceptions vary about washing - which stains are tough - cottons/blends; cold/hot • Dry cleaner substitutes for pressing • Fine fabric stain removal • Home remedy acceptance • "Involved" are willing to scrub, use multiple products • Seek sanitizing benefit - bed wetters, underwear • Limited stain penetration outside of laundry • Repeated washings to get clean • Combination usage • "Special" treatments for specific stains • No stain HH: adults • With kids: many stains • Laundry involved; "neatnick" - multiple products - developed routine, home remedies • Convenience driven  deteriorating standards • Blue collar, C&D county • Heavy user • Skeptical about performance Stain awareness for involved Segmented products for involved Convenience for uninvolved Kids-involvement Heavy user program • Heavy duty presoak • Booster • Sanitizer • Fine fabric stain remover • Heavy duty stain remover - grease and oil - work clothes • "Stain seeker" • Product holder for stick • Kids stick promotion • Rejuvenator for "loved" clothes • In-home dry cleaning • Baby Shout • Non-chlorine bleach/ booster • 2 in 1 delivery • Gel with brush • Post-foaming . . . visual cues • "Scotch-guard" for predictable stains • Wash in cold water • For cottons • Retards mildew • Dual positioning  no waste Household Segmentation Washing Habits Product Opportunities A Number of Observations from In-Homes Complete List Yields segmentation by usage behavior

Re-treat

• Presoak

• Special treatment

• SSR

• Segment by load

- by new highly valued

- by wearer: husband, kids, own

- by fabric

- towels are separate

- colors

- jeans, lights, delicates

• Perceptions vary about washing

- which stains are tough

- cottons/blends; cold/hot

• Dry cleaner substitutes for pressing

• Fine fabric stain removal

• Home remedy acceptance

• "Involved" are willing to scrub, use multiple products

• Seek sanitizing benefit

- bed wetters, underwear

• Limited stain penetration outside of laundry

• Repeated washings to get clean

• Combination usage

• "Special" treatments for specific stains

• No stain HH: adults

• With kids: many stains

• Laundry involved; "neatnick"

- multiple products

- developed routine, home remedies

• Convenience driven  deteriorating standards

• Blue collar, C&D county

• Heavy user

• Skeptical about performance

Stain awareness for involved

Segmented products for involved

Convenience for uninvolved

Kids-involvement

Heavy user program

• Heavy duty presoak

• Booster

• Sanitizer

• Fine fabric stain remover

• Heavy duty stain remover

- grease and oil

- work clothes

• "Stain seeker"

• Product holder for stick

• Kids stick promotion

• Rejuvenator for "loved" clothes

• In-home dry cleaning

• Baby Shout

• Non-chlorine bleach/ booster

• 2 in 1 delivery

• Gel with brush

• Post-foaming . . . visual cues

• "Scotch-guard" for predictable stains

• Wash in cold water

• For cottons

• Retards mildew

• Dual positioning  no waste

DOMINO’S PIZZA SEGMENTED ON A TIME CLAIM # of Locations Sources: Chicago Tribune; D&B

CUSTOMER “DISCOVERY” MOVES A STEP BEYOND SEGMENTATION Discovery is a continuous, dynamic, analytical process of understanding the economics and capabilities of the customer's business and leveraging that knowledge into ideas, products, and services of mutual benefit The result is a defensible, win-win partnership Disciplined Analytical Understanding Capabilities Creativity Partnership Value and Growth + +

Discovery is a continuous, dynamic, analytical process of understanding the economics and capabilities of the customer's business and leveraging that knowledge into ideas, products, and services of mutual benefit

The result is a defensible, win-win partnership

DISCOVERY FINDS NEW MATCHES BETWEEN CUSTOMER NEEDS AND CAPABILITIES New Opportunities with a Customer New Opportunities Across Customers Customer Needs Company Capabilities New Opportunity Unmet Needs Customer #1 Customer #2 Customer #3 Customer Needs Company Capabilities

UNDERSTANDING THE CUSTOMER— DISCOVERY EXAMPLES - 1 Net Result Would be More Mailings Estimated Breakdown Company's Value Delivery System Frames New Ideas National gift registry program Promotion to counter the negative perception of jewelry quality Mother’s Day reminder laser letter to best customers MDA sponsor Radio/TV POS household database collection 20MM+ customers identify and address recency and frequency sales by SKU and merchan- dise category Promotional and response history payment method sales and margin per customer Scoring methods Directed program at locations where a competitor such as “Best” is closing a store, inviting their customers to switch Buyer reactivation covers for custom- ers who haven’t purchased recently Marriage compilation for target mailings Preprinted flyer sent immediately to new customers featuring merchan-dise attractive on their second visit to the showroom Grand opening zoned mailer Barcoded coupon promotion Ink jet message directing customer to nearest store Free jewelry cleaning for lifetime ROP for special store promotions Store location cylinder versions Drive thru service Silent Sam electronic ordering system Preferred jewelry customer mailings for big-ticket buyers Price versioning to meet competition in certain geo- graphic areas Efficient region- alization plan allowing different products in different geographic areas Hardlines conver- sion flyer to con- vert a low margin hardlines buyer to a high margin jewelry buyer Price elasticity testing and tracking Japanese market opportunities Mail order service Cycle time reduction July “Price Busters” sale flyer to reduce inventory Ink jet messages calling attention to “overstocks” Selectronic gathering to put “overstock” signature into certain customers’ flyers Computer inventory system connects all stores; customer is directed to nearest store in stock Archive perform. system will allow company to put an overstock flyer together quicker Cycle time reduction Advertising and Image Management Customer Capture and Retention Store Traffic Pricing and Merchandising Inventory Management Response Rate of House Customers (%) Marginal Response Rate Customer Ranking Cumulative Average Response Rate Single Event Breakeven Lifetime Value Breakeven Parts of File Not Mailed

National gift registry program

Promotion to counter the negative perception of jewelry quality

Mother’s Day reminder laser letter to best customers

MDA sponsor

Radio/TV

POS household database collection

20MM+ customers

identify and address

recency and frequency

sales by SKU and merchan- dise category

Promotional and response history

payment method

sales and margin per customer

Scoring methods

Directed program at locations where a competitor such as “Best” is closing a store, inviting their customers to switch

Buyer reactivation covers for custom- ers who haven’t purchased recently

Marriage compilation for target mailings

Preprinted flyer sent immediately to new customers featuring merchan-dise attractive on their second visit to the showroom

Grand opening zoned mailer

Barcoded coupon promotion

Ink jet message directing customer to nearest store

Free jewelry cleaning for lifetime

ROP for special store promotions

Store location cylinder versions

Drive thru service

Silent Sam electronic ordering system

Preferred jewelry customer mailings for big-ticket buyers

Price versioning to meet competition in certain geo- graphic areas

Efficient region- alization plan allowing different products in different geographic areas

Hardlines conver- sion flyer to con- vert a low margin hardlines buyer to a high margin jewelry buyer

Price elasticity testing and tracking

Japanese market opportunities

Mail order service

Cycle time reduction

July “Price Busters” sale flyer to reduce inventory

Ink jet messages calling attention to “overstocks”

Selectronic gathering to put “overstock” signature into certain customers’ flyers

Computer inventory system connects all stores; customer is directed to nearest store in stock

Archive perform. system will allow company to put an overstock flyer together quicker

Cycle time reduction

UNDERSTANDING THE CUSTOMER— DISCOVERY EXAMPLES - 2 Implementing the New Program Could Increase Profits by 15 Percent and Inventory Turns by 10 Percent

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 understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 understanding

segmentation and “Discovery”

conjoint analysis

multi-dimensional scaling

Price-volume curves and elasticity

Demand forecasting

technology/substitution curves

Wrap-up

WHAT IS CONJOINT ANALYSIS? Conjoint analysis is a market research technique which uses consumer preferences for product or service attributes to derive a “utility value” for the whole product offering Conjoint analysis begins with the premise that goods and services are “bundles” of attributes Each attribute has a certain importance to the consumer e.g., color and engine size of a car Attributes can be presented in two or more levels e.g., price of a car may be < $10,000, $10,001 - $15,000, $15,001 - $20,000 The relative importance of individual attributes and their levels determine consumer preference for the composite product or service

Conjoint analysis is a market research technique which uses consumer preferences for product or service attributes to derive a “utility value” for the whole product offering

Conjoint analysis begins with the premise that goods and services are “bundles” of attributes

Each attribute has a certain importance to the consumer

e.g., color and engine size of a car

Attributes can be presented in two or more levels

e.g., price of a car may be < $10,000, $10,001 - $15,000, $15,001 - $20,000

The relative importance of individual attributes and their levels determine consumer preference for the composite product or service

HOW IS CONJOINT ANALYSIS USED? - 1 Conjoint analysis allows users to focus on key attributes Companies’ beliefs about consumer preferences often off base Typically administer survey to management Results very helpful in developing a blueprint for organizational change Conjoint analysis improves segmentation efforts Most segmentation schemes based on demographic information “ Dissimilar” groups may behave similarly use conjoint to identify other segments e.g., purchasing behavior

Conjoint analysis allows users to focus on key attributes

Companies’ beliefs about consumer preferences often off base

Typically administer survey to management

Results very helpful in developing a blueprint for organizational change

Conjoint analysis improves segmentation efforts

Most segmentation schemes based on demographic information

“ Dissimilar” groups may behave similarly

use conjoint to identify other segments

e.g., purchasing behavior

HOW IS CONJOINT ANALYSIS USED? - 2 Conjoint analysis can be used for simulations Market share projections New product introductions Competitive actions Marketing promotions/programs

Conjoint analysis can be used for simulations

Market share projections

New product introductions

Competitive actions

Marketing promotions/programs

WHEN SHOULD YOU USE CONJOINT (Instead of Only Market Interviews) Need a quantitative understanding of the decision-making process Measure the tradeoffs The decision is a complex one, with many attributes to consider Not dominated by a single item Want to play “what-ifs” with new or existing products Can decompose products into individual features

Need a quantitative understanding of the decision-making process

Measure the tradeoffs

The decision is a complex one, with many attributes to consider

Not dominated by a single item

Want to play “what-ifs” with new or existing products

Can decompose products into individual features

THREE PHASES TO CONJOINT ANALYSIS Attribute selection Conjoint has practical limit of 10-12 attributes Choosing attributes most important in decision process—critical to achieving meaningful results Data collection Significant number of interviews required to ensure valid survey Requires outside interviewers (Research Pros, market research firms) Data analysis Transforming raw data into meaningful recommendations

Attribute selection

Conjoint has practical limit of 10-12 attributes

Choosing attributes most important in decision process—critical to achieving meaningful results

Data collection

Significant number of interviews required to ensure valid survey

Requires outside interviewers (Research Pros, market research firms)

Data analysis

Transforming raw data into meaningful recommendations

PHASE ONE: CHOOSING ATTRIBUTES Must Understand Buying Decision and Product’s Limits A few days of market interviews with current customers Understand major criteria in buying decision Start to develop potential segmentations make sure demographics to derive segmentations are included in questionnaire Internal interviews/meetings to understand potential products and ensure client buy-in After interviews started, cannot change questions

A few days of market interviews with current customers

Understand major criteria in buying decision

Start to develop potential segmentations

make sure demographics to derive segmentations are included in questionnaire

Internal interviews/meetings to understand potential products and ensure client buy-in

After interviews started, cannot change questions

CAREFUL ATTRIBUTE SELECTION KEY TO MAXIMIZING CONJOINT EFFECTIVENESS Include all major decision-making criteria, as well as product features and business-level attributes But keep list short 10 attributes is practical limit, 6 to 9 most desirable (corroborated in conversations with conjoint experts) assumes use of adaptive techniques Long surveys result in more incomplete surveys, less consistent answers as interviewees tire need more respondents Fewer questions per attribute lessens accuracy Since attribute list limited, must carefully choose most relevant attributes

Include all major decision-making criteria, as well as product features and business-level attributes

But keep list short

10 attributes is practical limit, 6 to 9 most desirable (corroborated in conversations with conjoint experts)

assumes use of adaptive techniques

Long surveys result in more incomplete surveys, less consistent answers as interviewees tire

need more respondents

Fewer questions per attribute lessens accuracy

Since attribute list limited, must carefully choose most relevant attributes

PHASE TWO: SURVEY ADMINISTRATION AND DATA COLLECTION Telephone surveys often used Easiest to set up and administer Rapid data collection Survey administration subcontracted to market research firm to control costs. Team oversees Trains research firm personnel Monitors progress to see that the sample size is sufficient Ensures quality control As data becomes available, team performs interim analyses to provide early insight into findings

Telephone surveys often used

Easiest to set up and administer

Rapid data collection

Survey administration subcontracted to market research firm to control costs. Team oversees

Trains research firm personnel

Monitors progress to see that the sample size is sufficient

Ensures quality control

As data becomes available, team performs interim analyses to provide early insight into findings

BCG USES SPECIALIZED SOFTWARE TO COLLECT AND ANALYZE CONJOINT DATA Several packages on market to perform conjoint ACA—used by BCG Others include Bretton-Clark and a specialized SSPS for doing full profile conjoints ACA must be used to conduct adaptive interviews Since tradeoff analysis is adaptive, computer must analyze previous responses real time ACA also computes utility value for attributes and performs market simulations Data normally is exported to databases and spreadsheets for further analysis

Several packages on market to perform conjoint

ACA—used by BCG

Others include Bretton-Clark and a specialized SSPS for doing full profile conjoints

ACA must be used to conduct adaptive interviews

Since tradeoff analysis is adaptive, computer must analyze previous responses real time

ACA also computes utility value for attributes and performs market simulations

Data normally is exported to databases and spreadsheets for further analysis

STATISTICAL FORMULAS DEFINE SAMPLE SIZE FOR EACH DEMOGRAPHIC GROUP The number of interviews required for each demographic group increases with the number of attributes selected and the level of confidence required Because conjoint is a statistical hybrid, true confidence intervals can only be determined after data has been gathered Given a fairly homogeneous group, and 6 to 9 attributes, estimated sample sizes would be as follows: 99 10,000 98 2,500 95 400 90 100 85 44 75 16 Confidence Interval (%) Number of Interviews per Segment

The number of interviews required for each demographic group increases with the number of attributes selected and the level of confidence required

Because conjoint is a statistical hybrid, true confidence intervals can only be determined after data has been gathered

Given a fairly homogeneous group, and 6 to 9 attributes, estimated sample sizes would be as follows:

PHASE THREE: DATA ANALYSIS AND INTERPRETATION Many ways to analyze conjoint data More than can ever be done Extremely important to prioritize and continually check the sense of analyses Several analyses almost always done Absolute importance of attributes Demographic segmentation Hypothetical market responses to various product offerings Other analyses also very powerful where appropriate Behavioral segmentation (cluster analysis) Comparison of internal responses to actual customers

Many ways to analyze conjoint data

More than can ever be done

Extremely important to prioritize and continually check the sense of analyses

Several analyses almost always done

Absolute importance of attributes

Demographic segmentation

Hypothetical market responses to various product offerings

Other analyses also very powerful where appropriate

Behavioral segmentation (cluster analysis)

Comparison of internal responses to actual customers

CONJOINT QUANTIFIES THE VALUE OF ATTRIBUTES The basic output of conjoint analysis is a numerical rating of each attribute’s value to customers Each respondent has an equal number of “utility points” in total, but divides them differently among the various attributes 70 for price, 30 for performance vs. 40 for price and 60 for performance Utility points are added together to calculate the total attractiveness of a specified hybrid

The basic output of conjoint analysis is a numerical rating of each attribute’s value to customers

Each respondent has an equal number of “utility points” in total, but divides them differently among the various attributes

70 for price, 30 for performance vs. 40 for price and 60 for performance

Utility points are added together to calculate the total attractiveness of a specified hybrid

SAMPLE OUTPUT FROM A CONJOINT ANALYSIS Example comes from a mid-sized long distance telephone company Unsuccessful in current efforts: losing customers and money Need to reposition “product,” refocus efforts Conjoint allowed them to determine What customers they should be serving fit with internal capabilities fit with cost to serve How they could best serve those customers by giving them what they want

Example comes from a mid-sized long distance telephone company

Unsuccessful in current efforts: losing customers and money

Need to reposition “product,” refocus efforts

Conjoint allowed them to determine

What customers they should be serving

fit with internal capabilities

fit with cost to serve

How they could best serve those customers by giving them what they want

USER SURVEY FOCUSED ON EIGHT PRODUCT AND CARRIER ATTRIBUTES Within 2 hours Within 4 hours Within 8 hours Custom Standard invoice Leading edge (video, ISDN, ANI) Advanced (dedicated access, virtual network, data lines) Basic (direct dial, 800, travel cards) Line problem response time Billing options Products 10% below 5% below Current 5% above Clear Noisy National Regional Reseller Same day Same week Same billing cycle Local team Local single person National center Price Line quality Size of carrier Billing problem response time Service type Attribute Levels Attribute Levels

CUSTOMERS VALUE QUALITY, RESPONSIVENESS AND SERVICE OVER PRICE Utility Points From: To: Noisy Clear 8 Hours 4 Hours 4 Hours 2 Hours Std. Custom 5% Above Current Nat’l Ctr Team Resell Reg. 5% Below 10% Below Week Day Current 5% Below Basic Adv. Adv. Leading Cycle Week Reg. Nat’l Person Team Change in Attribute Levels Sources; Analysis of 448 Interviews, SIC Weighted

CUSTOMERS SELECT CARRIERS BASED ON DIFFERENCES BETWEEN ATTRIBUTE LEVELS Price Line quality Size of carrier Line problem res. Bill problem res. Service contact Products Billing Total utility points Current High National Within 4 hours Same bill cycle Team Advanced Standard Current High Regional Within 4 hours Same bill cycle Team Advanced Standard Current High Regional Within 2 hours Same bill cycle Team Advanced Standard 40 77 47 56 8 44 19 0 291 40 77 39 56 8 44 19 0 283 40 77 39 102 8 44 19 0 329 Attribute Level Value Level Value Level Value Carrier 1 Carrier 2 Carrier 3 Carrier preference is determined by differential between packages Note: Based on average, SIC-weighted customer; not specific segment Source: Analysis of 448 Interviews

USER PREFERENCES DIFFER SIGNIFICANTLY BY INDUSTRY SEGMENT AND SIZE OF LONG DISTANCE BILL Great Opportunity in Medium/Small Users Segment Trades Service Manufacturing Public/Gov’t Small/medium user Service driven, Less price sensitive Indifferent to carrier size Small/medium user Price driven Local service Large users, value price, national carrier, advanced products Industry <1,000 1,000-15,000 >15,000 Long Distance Bill/($/Month)

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 understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 understanding

segmentation and “Discovery”

conjoint analysis

multi-dimensional scaling

Price-volume curves and elasticity

Demand forecasting

technology/substitution curves

Wrap-up

WHAT IS MULTI-DIMENSIONAL SCALING? Multi-dimensional scaling (MDS) is a method for identifying the characteristics which underlie product positioning Used to reposition products or develop new products based on market perceptions Should be used after segmentation is complete MDS generally uses market surveys to pairwise compare products to determine the degree of similarity for each pair Output is a map which displays similar products close to one another, and dissimilar products relatively far apart MDS is an alternative or supplementary technique to focus groups or a carefully structured program of market interviews

Multi-dimensional scaling (MDS) is a method for identifying the characteristics which underlie product positioning

Used to reposition products or develop new products based on market perceptions

Should be used after segmentation is complete

MDS generally uses market surveys to pairwise compare products to determine the degree of similarity for each pair

Output is a map which displays similar products close to one another, and dissimilar products relatively far apart

EXAMPLE OF MULTI-DIMENSIONAL SCALING Household Cleaners * * * * * * * * * Duck Vanish Tilex Soft Scrub Fantastix Clorox Comet Pine Sol Lysol

MDS MAPS SHOULD BE USED TO THINK ABOUT PRODUCT POSITIONING QUESTIONS Consumer Magazines What are the axes/dimensions which underlie the map? Which products compete with one another? How do I interpret open space? Is there value to repositioning any products? developing new products? Playboy Penthouse Omni Esquire Vogue Cosmo Glamour Mademoiselle US Savvy People Ladies’ Home Journal Family Circle Better Homes & Gardens TV Guide Scientific American Practical Mechanics Business Week Economist Sports Illustrated US News Discover Newsweek Time Life Fortune Forbes Money

MDS DESERVES SEVERAL CAUTIONARY NOTES MDS map axes have no necessary definition Defining axes is subjective Each segment should be analyzed separately Consumer perceptions may differ from nonconsumer perceptions Blended perceptions across heterogeneous groups dilute value of MDS Umbrella brands should be unbundled to avoid confusion (e.g., Coke becomes Diet Coke and Coca-Cola Classic) Consumer purchase decisions do not necessarily follow consumer perceptions Repositioning a product to fill a “hole” in the MDS map does not ensure profitability or even economic viability

MDS map axes have no necessary definition

Defining axes is subjective

Each segment should be analyzed separately

Consumer perceptions may differ from nonconsumer perceptions

Blended perceptions across heterogeneous groups dilute value of MDS

Umbrella brands should be unbundled to avoid confusion (e.g., Coke becomes Diet Coke and Coca-Cola Classic)

Consumer purchase decisions do not necessarily follow consumer perceptions

Repositioning a product to fill a “hole” in the MDS map does not ensure profitability or even economic viability

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 understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 understanding

segmentation and “Discovery”

conjoint analysis

multi-dimensional scaling

Price-volume curves and elasticity

Demand forecasting

technology/substitution curves

Wrap-up

V = V (E, P , P , . . . , P ) Where V = Quantity demand E = Real expenditures on this and competitive goods P = Price of this good P . . = Prices of n competitive goods P PRICE-VOLUME CURVE BASED ON MICROECONOMIC DEMAND CURVE 0 0 1 n 1 n Adjusts product demand for Own/other’s price Expenditure Estimates volume change when faced with movement on cost side Experience effects Scale effects Technological change

V = V (E, P , P , . . . , P )

Where

V = Quantity demand

E = Real expenditures on this

and competitive goods

P = Price of this good

P

.

. = Prices of n competitive goods

P

Adjusts product demand for

Own/other’s price

Expenditure

Estimates volume change when faced with movement on cost side

Experience effects

Scale effects

Technological change

PRICE-VOLUME CURVE IS USUALLY EXPRESSED IN ONE OF TWO FORMS 1. To express level of demand For general application 2. To express change (  or  ) in demand For emphasizing elasticities or implicit scale slope Where: All else constant E P P K V V E E V V V V P P V V P P E P P Total Expenditures Own Price Other ' s Price Constant Income Elasticity Own Elasticity Cross Elasticity           0 1 0 0 1 1 1 0          ( )  ( ) ( ) ( ) ( ) ( ) ( )

1.

To express level of demand

For general application

2.

To express change (  or  ) in demand

For emphasizing elasticities or implicit scale slope

PRICING DECISIONS SHOULD BE BASED ON EXPECTED CONTRIBUTION MARGIN FROM INCREMENTAL SALES Pricing (low) for volume in variable cost products is often counterproductive Volume gains don't help much Foregone margin can be enormous Pricing (low) for volume in fixed cost products is often advisable Volume gains do help Competitive position is maintained by amortizing fixed costs Pricing (low) for high experience curve products also advisable Preemptive

Pricing (low) for volume in variable cost products is often counterproductive

Volume gains don't help much

Foregone margin can be enormous

Pricing (low) for volume in fixed cost products is often advisable

Volume gains do help

Competitive position is maintained by amortizing fixed costs

Pricing (low) for high experience curve products also advisable

Preemptive

PRICE-VOLUME CURVE DEFINES AREAS OF ELASTICITY/INELASTICITY %  V %  P If > 1: elastic  lower price If < 1: inelastic  raise price If = 1: unit elasticity  no change in price will change revenue Price Inelastic Elastic Unit Elasticity Volume Caution: Do not confuse “inelastic buyers” with inelastic portion of the demand curve Elasticity

%  V

%  P

If > 1: elastic  lower price

If < 1: inelastic  raise price

If = 1: unit elasticity  no change in price will change revenue

PRICE ELASTICITY Does changing the price grow or shrink the market (unit volume)? To be price elastic, a market must Have buyers who change their purchase frequency due to price changes purchase cycle number kept on hand Contain products which substitute for one another the tradeover point between products is within relevant pricing range price changes (almost) alone will cause substitution

Does changing the price grow or shrink the market (unit volume)?

To be price elastic, a market must

Have buyers who change their purchase frequency due to price changes

purchase cycle

number kept on hand

Contain products which substitute for one another

the tradeover point between products is within relevant pricing range

price changes (almost) alone will cause substitution

MEASUREMENTS OF ELASTICITY Initial Price Life Cycle Costs Support Installation Delivery Customization  P   V Relative Volume Relative Price Win Rate Relative Price Level • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Attribute Conjoint Utility 

Initial Price

Life Cycle Costs

Support

Installation

Delivery

Customization

RELATIVE PRICE AND VOLUME OF GASOLINE: CHICAGO 1983 Relative Price (Client/ Competitor Group) Relative Volume (Client/Competitor Group) Sources: Lundberg Survey; Purchase Behavior Survey Company A Independents Majors March Nov. May Sept. Jan. March Jan. Sept. Nov. May May March Sept. Nov. Jan.

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 understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 understanding

segmentation and “Discovery”

conjoint analysis

multi-dimensional scaling

Price-volume curves and elasticity

Demand forecasting

technology/substitution curves

Wrap-up

BCG USES A VARIETY OF TOOLS FOR FORECASTING DEMAND Forecasting demand requires a clear understanding of the underlying demand drivers, e.g., population growth, fashion changes, etc. Forecasting demand for existing products is most often done by extending historical volume trends into the future Regression analysis most common approach Forecasting demand for new products or technologies may require other tools Substitution curves Conjoint analysis

Forecasting demand requires a clear understanding of the underlying demand drivers, e.g., population growth, fashion changes, etc.

Forecasting demand for existing products is most often done by extending historical volume trends into the future

Regression analysis most common approach

Forecasting demand for new products or technologies may require other tools

Substitution curves

Conjoint analysis

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 understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 understanding

segmentation and “Discovery”

conjoint analysis

multi-dimensional scaling

Price-volume curves and elasticity

Demand forecasting

technology/substitution curves

Wrap-up

TECHNOLOGY SUBSTITUTION MODEL Basic Assumptions “ Many technological advances can be considered as competitive substitutions of one method of satisfying a need for another.” “ If a substitution has progressed as far as a few percent, it will proceed to completion.” “ The rate of substitution of new for old is proportional to the remaining amount of the old left to be substituted.” Source: Fisher & Pry

“ Many technological advances can be considered as competitive substitutions of one method of satisfying a need for another.”

“ If a substitution has progressed as far as a few percent, it will proceed to completion.”

“ The rate of substitution of new for old is proportional to the remaining amount of the old left to be substituted.”

CHARACTERISTICS OF SUBSTITUTION CURVES An S curve will result if the rate of substitution is proportional to The amount substituted The potential market remaining S curves yield different growth rates for different stages of substitution Often more realistic than linear or exponential growth Should be used to model systems in disequilibrium Converging to new equilibrium

An S curve will result if the rate of substitution is proportional to

The amount substituted

The potential market remaining

S curves yield different growth rates for different stages of substitution

Often more realistic than linear or exponential growth

Should be used to model systems in disequilibrium

Converging to new equilibrium

TECHNOLOGY SUBSTITUTION MODEL Where P 1 = penetration at t 1 P 2 = penetration at t 2 T = time to go from P 1 to P 2 L = final penetration % Plot on semi-log graph • Time on x axis P 2 L-P 2 ln ( ) - P 1 L-P 1 ln ( ) = b T P L-P ( ) or share of new share of old ( ) on log(y) axis Formula Critical assumption: substitution will proceed to “completion” Graphical Displays *F = fraction of new P L-P

Where

P 1 = penetration at t 1

P 2 = penetration at t 2

T = time to go from P 1 to P 2

L = final penetration %

Plot on semi-log graph

•

Time on x axis

PRODUCT/TECHNOLOGY SUBSTITUTION EXAMPLES - 1 Share of New Share of Old Year 0.02 0.1 0.05 0.2 0.5 1 2 5 10 (%) 95 80 50 20 10 Diesel Class 8 Gas Class 8 Trona Soda Ash Solvay Soda Ash Southern Plywood Western Plywood Atmospheric Sil Nonatmospheric DuPont Chloride TiO 2 Non-DuPont BOPP Film Cellophane Film Monolithic Capacitors Disc Capacitors NC Machine Tools Non-NC Tools 1. 2. 3. 4. 5. 6. 7. 8. Source: BCG Analysis 1 2 3 4 5 6 7 8

PRODUCT/TECHNOLOGY SUBSTITUTION EXAMPLES - 2 Household Penetration Household Penetration Compact Disc Color TV VCR Nintendo Internet Growth in distribution channel Critical mass Source: BCG Analysis

MODEL CAN BE USED TO FORECAST FUTURE GROWTH 3-4 Years Historical Data Required CD/ROM Penetration in Libraries . . . . . . and the Scientific, Technical, and Medical Field Library Penetration (%) Penetration of STM Community (%)

TECHNIQUE MUST BE APPLIED CAREFULLY Advantages “ Neutral” basis for projection, requiring no qualitative judgment Powerful predictor, if all conditions underlying past trend remain constant Cautions Do not lose sight of fact that substitution is driven most often by relative economics if underlying economics change, so will rate of penetration Apply on a segment-by-segment basis Do not assume final penetration will be 100% of total market

Advantages

“ Neutral” basis for projection, requiring no qualitative judgment

Powerful predictor, if all conditions underlying past trend remain constant

Cautions

Do not lose sight of fact that substitution is driven most often by relative economics

if underlying economics change, so will rate of penetration

Apply on a segment-by-segment basis

Do not assume final penetration will be 100% of total market

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 understanding segmentation and “Discovery” conjoint analysis multi-dimensional scaling Price-volume curves and elasticity Demand forecasting technology/substitution curves Wrap-up

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 understanding

segmentation and “Discovery”

conjoint analysis

multi-dimensional scaling

Price-volume curves and elasticity

Demand forecasting

technology/substitution curves

Wrap-up

THREE FINAL POINTS BCG does not formally document its analytical techniques But there is an active “oral tradition” Use it Our clients need our judgments “yesterday” Don't get too involved with the elegance of what you do Learn to execute “quick cuts” We often must make something out of (almost) nothing Consider case team needs and available information in choosing an analytic technique Be flexible

BCG does not formally document its analytical techniques

But there is an active “oral tradition”

Use it

Our clients need our judgments “yesterday”

Don't get too involved with the elegance of what you do

Learn to execute “quick cuts”

We often must make something out of (almost) nothing

Consider case team needs and available information in choosing an analytic technique

Be flexible