Operations & Digital Business

Nicolas van ZeebroeckMaster in Business Engineering – 2014-2015

Nicolas.van.Zeebroeck@ulb.ac.be

Competing on Analytics

Competing on�Analytics

How�a�belowͲaverage baseball�team�can makeit several years in�a�row into the�playoffs

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 3

How�Oakland�A’s competed on�analytics

• Oakland�A's�took�advantage�of�analysis�of�player�performance�to�field�a�team�that�could�better�compete�against�richer�competitors�in�Major�League�Baseball�(MLB)

• Came�up�with�new�(easy�to�obtain)�measures�of�player�performance:�onͲbase�percentage�and�slugging�percentage

• Statistics�used�in�2�ways:• For�recruitment:�identify�undervalued�players�on�the�market• On�the�field:�to�select�the�best�players�to�play�at�any�point�in�a�game

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 4

Lewis�(2003)

How�Boston�Red Sox competed on�analytics

• Boston�Red�Sox�invested�in�analytics�after�Oakland�A’s• In�5th game�v.�NY�Yankees�(ALCS�2003),�something�happened

• Pedro�Martinez�was�pitching• Analytics�had�shown�Martinez�much�easier�to�hit�after�7�innings• Coach�Grady�Little�refused�to�hear�about�the�statistics• After�7th inning,�Martinez�got�shelled�by�the�Yankees• Yankees�won�the�game�and�the�series• Little�lost�his�job

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 5

Davenport�&�Harris�(2007)

Two key�lessons

• Analytics�can�be�a�powerful�way�to�outperform�the�competition

• But�need�to�spread�everywhere�within�an�organisation

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 6

Competing on Analytics

What does it mean?

Analytics defined

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Davenport�&�Harris�(2007)

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 8

Why competing on�analytics?

A�necessity

• Classical sources�of�differenciation are�more�difficult to�sustain:• Product�and�service�differenciation vanishes• Technology is commoditized• Protective�regulation is gone• Geographical advantage is irrelevant with

Internet

• What’s left to�compete on?• Business�process performance:

• Execute your business�with maximum�efficiency• Make smartest business�decisions possible

An�opportunity

• Technology is now there to�provide• Granular (transaction)�data,�available in�quasiͲ

real�time�from ERP,�PoS and�Web�systems• Algorithms to�extract insights�from data• Computing power�to�run them

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GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 9Davenport�&�Harris�(2007)

LargeͲscale ambition

• Aspiration�to�achieve�largeͲscale�results

LargeͲscale ambition

• Aspiration�to�achieve�largeͲscale�results

EnterpriseͲlevel approach

• Data�management�&�analytics�managed�at�organisation level

EnterpriseͲlevel approach

• Data�management�&�analytics�managed�at�organisation level

Support�of�strategic,�distinctive�capability

• Explore�&�exploit�new�measures

Support�of�strategic,�distinctive�capability

• Explore�&�exploit�new�measures

Senior�Management�Commitment

• Organisational change�is�huge

Senior�Management�Commitment

• Organisational change�is�huge

ANALYTICALCOMPETITION

10

• Royal�Bank of�Canada:�customer data�centralizedin�the�1970’s

• Harrah’s:�CEO�first�had all�property managers�report�directly to�him

• Beracha (CEO Sara�Lee):�In�God we trust,�all�others bring data• Loveman (CEO�Harrah’s):�Do�we think or�do�we know?• Barclay’s:�5�years to�put�informationͲbased customer strategy in�place�Î Adjust every aspect�of�consumer�business:Clean�and�integrate data�on�13�million�customers,�How�to�charge�interest rates,�How�to�underwrite risk and�set�credit limits,�How�to�control�fraud,�How�to�crossͲsell other products

• Walmart:�supplyͲchain• Harrah’s:�customer loyalty and�service• Oakland�A’s:�human resources• Netflix &�Amazon:�customer preferences• Marriott:�optimal�room�pricing

Davenport�&�Harris�(2007)

Importance�of�analytical orientation

Davenport�&�Harris�(2007)

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 11

Where to�competing on�analytics?

Internalanalytics

Externalanalytics

• Human resources• Financial�management• Research &�Development• Manufacturing &�logistics

• Customers• Suppliers

Davenport�&�Harris�(2007)

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 12

Competing on Analytics

Internal processes

Financial�analytics:�revenue�forecasting

• Prediction of�future�sales�based on�sales�in�previous (most recent)�periods

• System�yielded optimistic sales• 2001:�Sales�down�by�32%

• Predictive models anticipatedslowdown in�sales

• Preemptive actions�on�prices and�products:�cut costs,�slash�prices

• 2001:�Sales�down�only 2.3%• Boost in�market share after

downturn

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 14

Davenport�&�Harris�(2007)

Manufacturing analytics:�quality control

• Early warning�system• Honda�dealers�record�any warranty service�request in�central�

database with categorized quality problem and�free�text• Additional textual data�is recorded from calls�of�mechanics

to�HQ�experts�and�customer call�centers• Text mining algorithms analyze data�to�identify potential quality issues• Potentially serious problems are�flagged for�human investigation• Reports�are�sent�to�HQ

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 15

Davenport�&�Harris�(2007)

Manufacturing analytics:�quality control

• Human quality control�(postͲproduction)�binary• If�not�perfect,�glass�is destroyed

• New�analytics system�(classificationͲbased)• Digital�camera�over�end�of�production�line• Each product is captured by�camera• Image�is analysed automatically by�AI�software• Potential defects are�analyzed and�categorized• According to�severity of�imperfection,�glass�is dispatched to�alternative�uses

• Results:• Considerable drop�in�wasted glass

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 16

Competing on Analytics

External processes

Competing on�analytics with external processes

• Identify most profitable�customers and�most atͲrisk of�switchingto�competition using predictive modeling

• Improve customer understanding by�integrating data�inͲhouse�with external data

• Optimize supply chains

• Establish prices in�real�time�to�get highest yield per�transaction

• Optimize advertizing and�marketing�strategies

Davenport�&�Harris�(2007)

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 18

Customer�analytics:�realͲtime�marketing

• Customers use�loyalty card• Capture�data�on�their behaviour

• Data�is used in�realͲtime�by�marketing�&�operations• Optimize yield• Set�prices for�slots�and�hotel rooms• Design�optimal�traffic flow�within casino

• If�customer loses too much money�too fast• Problem identified in�real�time• Message�is sent�(electronically or�through local�service�representative)• « Looks�like you are�having a�tough�day at�the�slots.�It�might be a�good�time�to�visit the�

buffet.�Here’s a�$20�coupon�you can use�in�the�next hour. »

Davenport�&�Harris�(2007)

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 19

Customer�analytics:�personalized marketing

• Clubcard (loyalty card)• Every online�or�offline�purchase is recorded in�client

history

• Data�is used to�profile�customers and�target promotions

• Results• 7�million�variations�of�product coupons�issued per�year• Redemption rate�highest in�the�retail industry worldwide:�20Ͳ50%�

• Average in�EU/US�retail industry:�2%�redemption rate• CrossͲselling on�Tesco�online• Tesco�now largest retailer in�the�UK

Davenport�&�Harris�(2007)

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 20

Customer�analytics:�revenue�management

• Revenue�Management:�« Predict highest price that would stilllead�to�full�occupancy »

• System�entirely automates�the�pricing optimization process• Rooms,�restaurants,�meeting�spaces,�etc.Î « Total�hotel optimization »

• Results• 2%�increase in�revenue• 17%�increase in�operating�income• Extra�$86�millions�in�profit

Davenport�&�Harris�(2007)

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 21

Customer�analytics:�price optimization

• Price�Management�&�Price�Optimization (PMPO)�solutions• Retail industry:

• 5Ͳ10%�increase in�gross margin from PMPO

• Downside:�Amazon’s example• Loyal�customers are�ready to�pay higher price than fickle customersÎ Smaller price elasticity

• Amazon�priced DVDs higher to�people�identified as�more�loyal• Public�outcry forced Amazon�to�back�off

Davenport�&�Harris�(2007)

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 22

Connecting suppliers &�customers

• What do�you do�with 583Tb�of�customer,�sales�and�inventory data?

• 17,400�suppliers from 80�countries�use�Retail Link�system• Track their products at�Walmart’s• Get info�on�sales,�shipments,�purchase orders,�invoices,�claims,�returns,�forecasts,�etc.

• Walmart managers�use�the�system�to�optimize product assortment• Ensure customers have�the�products they want• E.g.�Kellog’s strawberry pop�tarts just before a�hurricane�hits�a�region

Davenport�&�Harris�(2007)

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 23

Supplier�analytics

• Business�very sensitive�to�(volatile)�cocoa prices• Prediction of�price fluctuations�a�necessity to�hedge risks

• Mars�invested in�own earth observation�satellite�network• Satellites�monitor�weather conditions�in�main�cocoa production�regions• Data�mining algorithms use�current and�historical data�to�predict production�

levels per�region and�future�prices• Mars�optimizes its purchasing based on�price expectations

• Results:• Key�competitive advantage from excellence�in�procurement and�risk

management

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 24

Logistics analytics

• Fedex�and�UPS�are�heavy analytical competitors• Route�optimization

• « It’s vital�that we manage�our networks�around the�world�the�best�way we can.�Whenthings don’t go�exactly the�way we expected because volume�changes�or�weather gets in�the�way,�we have�to�think of�the�best�ways to�recover and�still keep our service�levels. »�Mike�Eskew,�CEO�UPS

• CEMEX�optimizes routes�of�its delivery trucks�in�realͲtime• GPS�chips�+�traffic monitoring• Cut�delivery time�from 3�hours to�20�minutes�in�Mexico

Davenport�&�Harris�(2007)

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 25

Competing on Analytics

What does it take to become an analytical competitor?

Foundations for�analytical competition

1Ͳ2ͲALL

• From the�examples seen in�class,�what are,�according to�you,�the�main�elements needed for�an�organisation�to�take advantage of�analytics?• Think of�different layers if�it helps

• Think alone,�then in�groups�of�2�or�3

• Write�down�your ideas on�a�piece of�paper with your names

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 27

Foundations for�analytical competition

• Strategy and�focus�to�determine most relevant�use�of�analytics

• Transactional systems to�generate accurate/relevant/granular data

• Processes to�get insights�and�act upon them

• People who embrace analytics• IT�experts,�business�analysts &�data�scientists,�decisionͲmakers,�employees

• Governance to�share the�data�and�foster factͲbased decision

• Software�technology to�extract and�analyze the�data

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People:�the�beer and�diaper legend

• Analysts found interesting pattern�in�retail:• Men�coming to�buy beer for�the�weekend�also tend�to�remember that their

wives had asked them to�buy diapers• So�they put�both products in�their carts

• Retail store�managers�decide to�place�diapers next to�beers• Sales�explode

Davenport�&�Harris�(2007)

GESTS482�– Operations�and�Digital�Business�– N.�van�Zeebroeck�– 2014Ͳ2015 29

People:�the�beer and�diaper legend

• Data�mining can identify patterns

• Humans are�still needed to�interpret themÎ Analysts

• Other humans are�still needed to�act upon themÎ Executives

• AnalyticsͲbased decisions can only be as�good�as�the�quality of�the�data�they rely on

Competing on Analytics

Key take aways

Competing on�analytics:�key�take aways

• Analytical competition can really change�the�rules of�the�game• No�industry is safe

• Competing on�analytics requires• To�support�a�strategic,�distinctive�capability• An�entepriseͲwide�approach• SeniorͲmanagement�commitment• LargeͲscale ambition�(not�local�optimization)

• Can�serve�both internal and�external processes• Analytics infrastructure�is made�of

• Data�(transactional,�granular,�high�quality)• Technology (never selfͲsufficient)• Processes• People�(to�analyze and�to�decide)• Governance

Your assignments

Assignments on�Analytics

• Read�the�technical�note�on�typical�applications�of�analytics• Read�the�two�reference�articles

• Davenport:�Competing�on�Analytics• McAfee�&�Brynjolfsson:�Big�Data:�the�management�revolution

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