granmetrics exp

8/9/2019 Granmetrics Exp

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Granular Metrics,Granular Metrics,

Feedback and ExperimentationFeedback and Experimentation

Arnoldo HaxAlfred P. Sloan Professor of Management


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Contributions of the Delta ModelContributions of the Delta Model

Opening theOpening the The BestThe Best Three Strategic Options:Three Strategic Options:Product doesProduct doesmindset to newmindset to new

zz Best ProductBest ProductThe TriangleThe Triangle not always winnot always winstrategicstrategiczz Total Customer SolutionsTotal Customer Solutionspositionpositionzz SySystem Lockstem Lock--inin

LinkingLinkingStrategy withStrategy withAdaptiveAdaptive ExecutionExecution

ProcessesProcesses

Execution isExecution isnot thenot theproblem,problem,linking tolinking tostrategy isstrategy is

Execution is captured through ThreeExecution is captured through ThreeAdaptive Processes:Adaptive Processes:

zz Operational EffectivenessOperational Effectiveness

zz Customer TargetingCustomer Targeting

zz IInnnovnovationation

whose roles need to change to achievewhose roles need to change to achieve

different Strategic Positionsdifferent Strategic Positions

MeasuringMeasuring GoodGoodsuccesssuccess financials dofinancials do

AggregateAggregate not alwaysnot alwaysMetricsMetrics lead to goodlead to good

resultsresults

Aggregate performance metrics need toAggregate performance metrics need toreflect each of the Adaptive Processesreflect each of the Adaptive Processesand their role based upon the strategicand their role based upon the strategicpositionposition

zz Product performanceProduct performance

zz Customer performanceCustomer performance

DiscoveringDiscoveringGranularGranularperformanceperformanceMetrics andMetrics anddriversdrivers

FeedbackFeedback

Managing byManaging byaveragesaveragesleads to belowleads to belowaverageaverage

performanceperformance

zz Complementor performanceComplementor performanceBusiness is nonBusiness is non--linear. Performance,linear. Performance,particularly bonding, is concentrated.particularly bonding, is concentrated.Granular metrics allow us to focus onGranular metrics allow us to focus onunderlying performance drivers, tounderlying performance drivers, to

detect variability, to explain, to learn, anddetect variability, to explain, to learn, andto act.to act.


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The Delta Model and Granular MetricsThe Delta Model and Granular MetricsSelect Performance Indicators

Explain Variability

Identify performancedrivers

Correlate drivers withvariability

Detect Variability

Drill down to detailedsegmentation Isolate variability

Define program Define structured tests Rollout

Hypothesize cause andeffect

Evaluation

Act from Variability

Learn from Variability


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Drivers of variability for selected performance indicatorsDrivers of variability for selected performance indicators

Drivers of variabilityPerformance indicatorsPerformance indicators Drivers of variability

Product cost and qualityProduct cost and quality zz ScaleScalezz DensityDensity--e.g. concentrae.g. concentrattion of serion of servvicicee

zz LLoocatiocationn

zz Labor productivityLabor productivity-- practices andpractices and trainintrainingg

zz EquipmeEquipmennt product producttivivityity-- desigdesignn

zz Process designProcess design

zz

AnAnd sod so oonnCustomer profitCustomer profit zz Customer sizeCustomer size

zz Customer revenueCustomer revenue

zz TenureTenure

zz Acquisition costAcquisition cost

zz ChanChannel mixnel mix

zz

Customer care supCustomer care supppoortrtzz Customer investments inCustomer investments in relatiorelationnshshiippss

zz AnAnd sod so oonn

zz Size of relevSize of relevaant complementor productsnt complementor productsComplementor contributionComplementor contribution

zz Complementor investment in bComplementor investment in buusinesssiness

zz Relative size in customer value chainRelative size in customer value chain

n t customer economicszz

Complementor contributiComplementor contributioon too customer economicszz ExclusivityExclusivity of relaof relationshiptionship

zz AnAnd sod so oonn

Business segment economicBusiness segment economic zz ROI (return oROI (return onn investment)investment) profitabilityprofitabilityvaluevalue zz RiskRisk-- volatilityvolatility and covariancand covariancee

zz Option valueOption value

zz

Investment baseInvestment basezz CashflowCashflowss


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Experimentation as the basis for effective changeExperimentation as the basis for effective change

LargeLarge

Size ofSize of

ChangeChange

SmallSmall

The middle road:The middle road:lower returns, orlower returns, or

unacceptable whenunacceptable when

first moverfirst moveradvantage is highadvantage is high

Unacceptable risk,Unacceptable risk,as a starting point.as a starting point.Highly desirable asHighly desirable as

endpointendpointIneffective: lowerIneffective: lower

returns, orreturns, orunacceptable whenunacceptable when

first moverfirst moveradvantage is highadvantage is high

Testing: theTesting: the

relevant area forrelevant area forexperimentsexperiments

leading to largeleading to largechangechange

SlowSlow FastFastSpeed ofSpeed of

ChangeChange


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Cost DeCost De--Averaging:Averaging:

Using Granular Metrics ToUsing Granular Metrics ToDrive PerformanceDrive Performance


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Most

Expensive

$100

$1,000

Least

Expensive 0

20

%o

fcosts

% of orders

40

60

80

100

20 40 60 80 100Orders

2. Individual Order Cost

Cost/order

Switching Billing Marketing Etc.Order

fulfillment

1. The Value Chain of the Local Business Data Circuit

Average Cost = $395/order

3. The 80-20 Cost Effect

Transmission

THE BEHAVIOR OF COST - THE CASE OF BUSINESS DATA SERVICES

Figure by MIT OCW.


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The drivers of the cost of orderThe drivers of the cost of order--fulfillmentfulfillment

Cost of orderfulfillment

EquipmentLocation ofwork centers

Activities oforder fulfillment

Manpowerproductivity


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1

-

}

/

/

THE BEHAVIOR OF COST BY LOCATION

1,000

70

Headcount

requiredtoproce

ss

1,0

00o

rderspermonth

2,000 5,000

Chicago

Competitor 1

San Francisco

Kansas City

Competitor 2

San JoseCompetitor 2

Indianapolis

Competitor 3

Jacksonville

Competitor 1

Denver

New York

Tulsa Charlotte

Competitor 1 - Chicago

Competitor 1 - Houston

39%

gap

Orders Month

Or der Fulfillment

12,000 27,000 60,000

Client performance

curve

Company

Competitors

Scale = 72%

Log log graph

Best-in-class

performance

Figure by MIT OCW.


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(

)

(

)

THE BEHAVIOR OF COST BY FIVE ACTIVITIES IN THE VALUE CHAIN

Order

Facilitiesavailable

Access

available Pass test

Valid design

Accessavailable

Accessnotavailable

Accessnotavailable

Facilitiesnotavailable

Automaticswitch map

Fastfacilities test

Pass accesstest

Customerready

$100 per

access leg

10x cost

difference

1MILLIONPOSSIBLEORDERPA

THS

$2,000 per

access leg

Skin test

Invalid design

Manualswitch map

Failfacilitiestest

Fail accesstest

Customernot ready

'Defect-free Path'

'Defect-pr one Path '

Figure by MIT OCW.


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The behavior of cost by type of defectsThe behavior of cost by type of defectsClientClient

Business DataService Orders

Business Data

Service Orders

Business Data

Service Orders

Defect-free

Average

71%71% $100$100 5 Days5 Days 18%18%

5.0%5.0% $1,800$1,800 45 Days45 Days 22%22%

24.0%24.0% $900$900 25 Days25 Days 60%60%

1 $395 12.3 Days

ordersorderse cyclee cycle

timetime

Cost/Cost/

orderorder costcost

Focus ofFocus ofprogramprogram

developmendevelopmen

tt

AveragAverag% of% of TotalTotal


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0

00

A

(%)

(%)

/

B C D E F G H I J K L M N

2

4

6

8

( )

25 100

80

100

Craftsmen

Cum

ulativecost

Cumulative craftsmen

Craftsmen Efficiency

The Need for

Granular Metrics

Totalhoursclearedfault

S:1

10

Fault not fixed correctly

& repeats

Pair throw: reroute around

fault

Request assistance

Refer fault to someone else

specialist

Fix fault

THE BEHAVIOR OF COST BY INDIVIDUAL WORKERS

Figure by MIT OCW.


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00

00

j

THE BEHAVIOR OF COST BY TYPE OF EQUIPMENT

20

20

40

60

80

100

40

% of Junctions

Line Fault Concentr ation

%o

fLineFaults

60 80 100 20

20

40

60

80

100

40

% of Equipment

Records Errors Concentration

%o

fRe

cordsErrors

60 80 100

100% of demand pair

reclamations are

concentrated in 17%

of unctions

100% of records

errors are concentrated

in 19% of equipment

Figure by MIT OCW.


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/

/

---

-

a b

c

/

---

-

OPERATIONAL EFFECTIVENESS: ADAPTIVE FEEDBACK USING GRANULAR METRICS

Cost Drivers

Individual Performance

Select

Associates

Best Pr actice Sessions

Supervisor

Benchmarks

Supervisor 'Supervisor Coaching'

Individual Reviews

Standard

Standard

Supervisor Review District Center Reviews

Director Process Reviews

Ind.

Total

Actual Target

a b

CostUnit

Supervisor

Total

Actual Target

Figure by MIT OCW.


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Profit DeProfit De--Averaging:Averaging:

Using Granular Metrics ToUsing Granular Metrics ToDrive PerformanceDrive Performance


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0

PROFIT MARGIN CONTRIBUTION

THE NEED FOR GRANULAR METRICS

-7x

-6x

-5x

-4x

ProfitMargin

-3x

-2x

-x

Cumulative percentage of customers1x

2x

3x

85% of Profits

46% of Profits

40% of Profits

21% of Profits

10 20 30 40

35

50 60 70 80 90 100

Figure by MIT OCW.


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The drivers of customer profitability in the credit card businesThe drivers of customer profitability in the credit card businesss

Profit by customer

Months oftenure

Revenue percustomer

Number ofcustomers

Balance levelper month


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$( )

/

/

$( )

(

)

(

)

VARIABILITY OF PROFITS BY USAGE LEVELS

20MonthlyProfit

Customer

$10-$100 $100-$200

Revenue Customer

$200-$400 $400+

10$0

$10

$20

$30

$40

$50$60

$70

75th Percentile +1.5* Box Size 'Upper

limit of connected data'

75th Percentile

Median

25th Percentile

25th Percentile -1.5*

Box Size 'Lower limitof connected data'

Figure by MIT OCW.


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(

)

0

/

0

FURTHER EX AMINATION OF PROFIT DRI VERS

5MM

15%

22%18%

10MM 20MM

Number of Customers

Company Scale Customer Share Customer Tenure

ProfitMargin

ROS

30MM $1000-50

-40

-30

-20

-10

10

20

30

$2000 $3000 $4000

Balance Level $ Month

10 20 30

-50

-40-30

-20

-10

10

20

30

Months of Tenure

Figure by MIT OCW.


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T H E E F F E C T S O F C U S TO M E R TA R G E T I N G

150,000

0

x

3.5x

Tailor offer s to individual customers

(i.e., increase response & profit with differentialoffers tailored to customer's preferences)

Offer one product to customers, based on

both r esponse & pr ofitability

(i.e., acquire customers likely to have good volume/

margin/lifetime characteristics)

Offer one pr oduct to those who will respond

(i.e., maximize response for a given offer, assuming average economics)

Offer one product to

everyone

Untargeted solicitations

yield a negative profit

5x

CumulativeProfit

Cumulative Number of Customers Solicited

300,000

}

}}}

}}}}

Figure by MIT OCW.


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CustomersCustomers targeting behaviorstargeting behaviorsBehaviorsBehaviors Typical competitorsTypical competitors Capital OneCapital One

zz OfferOffer 5,000 products5,000 products75 products75 products

zz Test/yTest/yearear 3030 15,00015,000

zz Service tierService tier 44 1313

zz KeKeyy metricmetric ROE, DelinquencyROE, Delinquency NPVNPV

zz TargetsTargets High response likelihoodHigh response likelihood High NPV potentialHigh NPV potential

zz Solicitations/ySolicitations/yearear 50 million50 million 400 million400 million

zz Segmentation frequencSegmentation frequencyy BiannualBiannual OngoingOngoing

zz Risk orientationRisk orientation Risk averseRisk averse Price for riskPrice for riskzz Data sourcesData sources 8080 50,00050,000

zz Staff analyStaff analysststs 2020 150150


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Capital OneCapital One-- achieving a Total Customer Solutions throughachieving a Total Customer Solutions through

Customer TargetingCustomer Targeting

FurtherFurtherimprovementimprovement

Variation 2

Variation 1

Test cells

SelectOffers

OfferCreation

IntereInterest rast ratestes FeesFees PromotionsPromotions

MassMassmarketingmarketing

Screen forprofitability

Full lifeFull life

cycle customercycle customer

profitabilityprofitability

Variation 2

Variation 1

Core offer

Collectcustomerresponse


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Success metricsSuccess metricsTypical competitorTypical competitor Capital OneCapital One

zz Customer NPVCustomer NPV

zz

Acquisition costAcquisition costzz ActivationActivation

zz PercenPercent witht with revorevolvinglving bbaalanlancesces

zz Fee income per accountFee income per account

zz ChargeCharge--off per accountoff per account

zz TenureTenure

$62$62

$77$7758%58%

25%25%

$55$55

$71$71

4 years4 years

$427$427

$67$6779%79%

40%40%

$73$73

$54$54

6 years6 years


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0

1)

2)

3)

4)

5)

0

5

i)

)

i)

)

i)

)

i)

)

% of Equity Investment

100% ofvalue

created

6 BUs contribute 100% of value created

16 more BUs contribute another 42% of value created

Those 22 BUs are worth $ 15.5 billion, on an equity investment of only $4.2 billion

44% of new capital investment and 53% of R&D spending over the next three years is going to BUs

earning economic profits

56% of new capital investment and 47% of R&D spending are going to BUs that will be generating

economic losses

-40

-20

-5

10

15

20

60

%o

fTotalCompan

yValueCreated

25

-21% of value created -21% of value created42% more value

created

Value Created by Business Unit

24% of

new

capital

21% ofR&D

ii

20% of new

capital

32% of R&Dii

36% of new capital

25% of R&Dii

50 75 100

SOURCES OF VALUE CREATION

20% of new capital

22% of R&Dii

Figure by MIT OCW.

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