WIN WITH ADVANCEDBUSINESS ANALYTICS

CREATING BUSINESS VALUE FROM YOUR DATA

BY JEAN PAUL ISSON AND JESSE HARRIOTT

2

Contents

Exhibit 2.1 4 Analytics Recipe Matrix 5 Exhibit 4.1 7 Exhibit 4.2 8 Exhibit 4.3 9 Exhibit 5.1 10 Exhibit 6.1 11 Exhibit 6.2 11 Exhibit 6.3 12 Exhibit 6.4 12 Exhibit 6.5 13 Exhibit 6.6 13 Exhibit 6.7 14 Exhibit 6.8 15 Exhibit 6.9 16 Exhibit 6.10 17 Exhibit 6.11 18 Exhibit 7.1 19 Exhibit 7.2 20 Exhibit 8.1 21 Exhibit 8.2 22 Exhibit 8.3 23 Exhibit 8.4 24 Exhibit 9.1 25 Exhibit 9.2 26 Visitor-Tool Table 27 Exhibit 10.1 29 Exhibit 10.2 30 Exhibit 10.3 31 Exhibit 10.4 32 Exhibit 10.5 33 Exhibit 11.1 34 Exhibit 11.2 35

3

HR Process Management Model 36 Exhibit 11.3 37 Exhibit 11.4 38 Exhibit 14.1 39 Exhibit 15.1 40 Exhibit 15.2 41 Exhibit 15.3 42 Exhibit 15.4 43 Exhibit 16.1 44 Exhibit 16.2 45 Exhibit 16.3 46 Exhibit 16.4 47 Exhibit 16.5 48 Exhibit 16.6 49 Exhibit 17.1 50 Exhibit 18.1 51 Exhibit 18.2 52 Exhibit 18.3 53

Success Pillars

Busi

ness

Chal

leng

es

Insi

ght

Dist

ribut

edKn

owle

dge

Inno

vatio

n

Business Analytics

Exec

utio

n an

dM

easu

rem

ent

Data

Fou

ndat

ion

Anal

ytic

sIm

plem

enta

tion

Exhibit 2.1 BASP Framework

c02 1 September 2012; 13:14:51

4

Business Challenge Analytics Solutions Benefits

Acquire new customers. Target Response Model Bring in more customers forthe same costs.

Retain your profitablecustomers.

Customer Churn/AtRisk Model

Increase customer walletshare and overallprofitability.

Up-sell and cross newand existing customers.

Customer LifetimeValue Model

Identify long-termprofitability.

Avoid high-risk customers. Risk and ApprovalModel

Identify credit risk amongcredit applicants. Detect andminimize the effect offraudulent claims ortransactions.

Increase sales. Acquisition Up-Sell andRetention Models

Increase market share andcustomer profitability.

Win back your lostcustomers.

Win Back Models Increase sales andprofitability.

c03 1 September 2012; 13:16:19

Analytics Recipe Matrix

5

Business Challenge Analytics Solutions Benefits

Increase customersatisfaction.

Market Research andCustomer-Profiling Models

Deepen your understandingof current and prospectivecustomers through surveyresearch.

Recruit new talent costeffectively.

Predictive HR AnalyticsModel

Talent and personnelmanagement.

Increase employeeretention.

Employee SatisfactionSurvey and PredictiveHR Analytics

Manage personnel turnoverand retain valuable talent.

Increase conversion. Seeker and VisitorsSegmentation

Increase the usability andstrategic value of your Webproperties.

Expand in new markets. Global AcquisitionModel

Global footprintdiversification of revenue.

Understand thecharacteristics of yourcustomers.

Customer Profiling andCustomer Segmentation

Improve customerprofitability and CRMoptimization.

Manage and anticipatecompetitors and gear up forcompetition coming fromuncharted territories.

Proactive CompetitiveIntelligence Analytics

Win against the competition.

Streamline your pricing. Pricing Optimization Models Increase revenue and grossmargin.

c03 1 September 2012; 13:16:20

6

BrandExperience

ServiceExperience

CustomerDemographics

Motivations forPurchase

PurchasingBehaviors

Sophistication

CompetitiveProduct Use

Word ofMouth

Industry

Economy

CompetitiveAlternatives

ProductExperience

SalesExperience

PurchaseExperience

InformationGatheringExperience

Customer Drivers

Company Drivers

Indirect Drivers

CustomerCustomer

Exhibit 4.1 Customer Knowledge Framework

c04 1 September 2012; 13:20:7

7

Current Customer Data Customer Data Before

InteractionIn person (point of sales)Voice (over the phone)E-mailInternetSocial mediaSmartphone

InteractionIn person (point of sales)Voice (over the phone)

PreferencesBuild up landing pageNewsletterChoice

Preferences

FeedbackIn personCustomer service repE-mailSocial network

FeedbackIn personCustomer services rep

Site BehaviorPage viewLanding pageVisit durationExit pageEcom ordersNumber of visits

Site Behavior

Social Media Social Media

Services Services

UsageVoiceDataVideo/picturesTextInternet

Exhibit 4.2 Customer Data Sources, Telecommunications

c04 1 September 2012; 13:20:7

8

Web traffic Mobile traffic Sales operations data (talktime, number of calls,performance by rep, etc.)

Customer loyalty Market share and walletshare

Web search keywords

Website satisfaction Competitive intelligence E-mail open rate andconversion

Product performance New and returningcustomers: # and $

Product satisfaction

Sales Economic trends Market size/opportunity

Win-loss by channel Customer focus groupresults

Media mentions/sentiment

Brand awareness and equity Customer lifetime value Revenue

Website and productusability metrics

Advertising copy testing Concept test results

Customer demographics Satisfaction with service Employee satisfaction

Media mix performance data Customer survey data Shopping behavior data

HR metrics (turnover, exitinterviews, etc.)

Customer e-mails and calltranscripts

Customer satisfaction withcompetitors’ products

Exhibit 4.3 Example of Data That Can Be Valuable for Business Analytics

c04 1 September 2012; 13:20:8

9

Identify theQuestions

Master theData

TrackOutcomes

CommunicateInsights

Provide theMeaning

ActionableRecommendations

P

M

I

T

C

A

Exhibit 5.1 The IMPACT Cycle

c05 3 September 2012; 12:21:14

10

1985 1986 1987 1988 1989 1990

SOTHEBY’S

Market Share Analysis With Buyer’s Premium

CHRISTIE’S

44%56%

58%42%

44% 56%

59%41%

40%60%

58%42%

Exhibit 6.1 Sotheby’s/Christie’s Worldwide Sales: Market Share Analysis

200

Cost

of t

he a

vera

ge s

peed

ing

ticke

t (do

llars

)

160

120

80

40

0

10 20 30 40 50 60Automobile speed (kph)

70 80 90 100

Exhibit 6.2 Dependence of Traffic Ticket Cost on Automobile Speed

c06 3 September 2012; 12:23:37

11

Exhibit 6.3 The Relationship between Wheat Prices and Worker Wages

100.0

80.0

60.0

40.0

Perc

ent

20.0

0.012–1 a.m. 4–5 a.m. 12–1 p.m. 4–5 p.m. 8–9 p.m.8–9 a.m.

Sleeping Household activitiesLeisure and sportsWorking and work-related activities

Exhibit 6.4 Percent of Employed Persons Who Did Selected Activities on Workdays ByHour of the DaySource: Bureau of Labor Statistics, American Time Use Survey

c06 3 September 2012; 12:23:38

12

Exhibit 6.5 Word Cloud

Exhibit 6.6 Web Page Heat Map

c06 3 September 2012; 12:23:40

13

Exhibit 6.7 The Crescive CowSource: From How to Lie with Statistics, by Darrell Huff, illustrated by Irving Geis, Copyright1954 and renewed ª 1982 by Darrell Huff and Irving Geis. Used by permission of W. W.Norton & Company, Inc.

c06 3 September 2012; 12:23:40

14

This line, representing 18 miles pergallon in 1978, is 0.6 inches long.

18 191978’79

’80’81

’82

’83

’84

’85

2022

24

26

27

Set by Congress and supplemented by the TransportationDepartment, In miles per gallon.

This line, representing 27.5 miles pergallon in 1985, is 5.3 inches long.

New York Times, August 9, 1978, p. 0–2.

27½

Exhibit 6.8 Fuel Economy Standards for AutosSource: New York Times, August 9, 1978, p. D2

c06 3 September 2012; 12:23:41

15

Sphere Size Indicates Level of Usage60%

50%

40%

30%

20%

10%

0%0% 10% 20% 30% 40% 50% 60% 70%

Degree of Difficulty

Leve

l of E

ffect

iven

ess

80% 90%

Title tags

Meta descriptiontags

Internallinking

URLstructure

Key word andkey phraseresearch

Contentcreation

SEO landingpages External line

buildingBlogging

Digitalasset

optimization

Socialmedia

integration

Competitorbenchmarking

Exhibit 6.9 Effectiveness of Search Engine Optimization Tactics

c06 3 September 2012; 12:23:41

16

8%

2%

TRIALWe have no process or

guidelines for performing SEO.

TRANSITIONWe have an informal process

with a few guidelines wesporadically perform.

STRATEGICWe have a formal process

with thorough guidelines weroutinely perform.

0%

2%

4%

6%

8%

10%

12%

0%

5%

10%

15%

20%

25%

30%

% Organizations Rating Leads from Organic Search as Highest QualityMedian Conversion Rate on Organic TrafficAverage Conversion Rate on Organic Traffic

3%

9%

4%

24% 27%

5%

10%

Exhibit 6.10 Effectiveness of Search Engine Optimization StrategiesSource: MarketingSherpa

c06 3 September 2012; 12:23:41

17

FALL IN VALUES, PERCENTAGE CHANGE.

X

Y

Z

FALL IN VALUES, TOTAL, CURRENT, PROJECTED.

INDEX VALUES

A. Projected fall peak to trend = 62%B. Current fall from peak = 30%C. Loss from today to bottom = 45%

A. Total loss = x-y =68B. Current loss = x-z = 140C. Loss from today forward = y-z = 72

X = Bubble high (June 2006) = 226Y = Index today = 159Z = Trend = index at trend = 86

Case-Shiller 10 City IndexTrend Based on 1987 to 1997

23030% Down. 45% To Go.

210

190

170

150

130

110

90

70

501987 1991 1993 1987 1997

23 Years of Data by Case-Shiller 10-City Index. 1/87 to 9/09.

1999 2001 2003 2005 2007 20091989

Exhibit 6.11 Residential Property ValuesSource: Graph by NewObservations.net

c06 3 September 2012; 12:23:41

18

Vision andMandate

Metrics andMeasurement

StrategyOrganizationCollaboration

ChangeManagement

Information

HumanCapital

CustomerExperience

IntegratedProcesses

Technologyand Tools

Drivers

FacilitatorsFacilitators

Enablers

Business Analytics Success Pillars

Busi

ness

Chal

leng

es

Data

Foun

datio

n

Anal

ytic

sIm

plem

enta

tion

Insi

ght

Exec

utio

n an

dM

easu

rem

ent

Dist

ribut

edKn

owle

dge

Inno

vatio

n

Exhibit 7.1 Analytics Implementation Model

c07 3 September 2012; 12:25:44

19

Project Description:

Value Proposition

Target Customer & Segment: Dependencies:

Competition:

Risks:

Documentation:

Est. Revenue:Est. Total Market Opportunity:

Year 1 Year 2 Year 3 Yrs 1–3 CAGR

Est. Cost Savings:Est. Customer Satisfaction Impact:

Target Company Size:

Benefit:

First Customers:

Exhibit 7.2 Business Analytics Project: Executive Summary

c07 3 September 2012; 12:25:45

20

Listen,Monitor

Process Data

Evaluating Success Action

Plannin

g

Find InsightsAnalyze,Interpret,Integrate

Actions Takenby Organization

RecommendActions

Responsibilities of the Research and Analytics Team

Responsibilities across the Organization: Marketing, Product, Pricing, Sales, Service

Exhibit 8.1 Voice-of-the-Customer Program Paradigm

c08 3 September 2012; 12:6:38

21

Actions Taken by Functional Teams Based on VOC Insights

Product, Pricing Product Marketing Sales Service

-Satisfaction, Loyalty-Complaint Issues-Issue Resolution

-Root Cause-Quality, Speed, Ease

-Agent Quality-Knowledgeability

-Value Proposition-Needs Fulfillment

-CompetitivePositioning-Win/Loss

-Lapsed Customer-Agent Quality

-Satisfaction, Loyalty

-Perceived Benefits-Value Proposition

-Value Proposition-Advertising Strategy

and Execution-Shoppers Insights

-CompetitivePositioning

-Needs and Wants-Product

Development-Innovation,

Enhancements-Pricing Strategy

-Satisfaction, Loyalty-Packaging, Naming

Marketing

-Brand Awareness-Brand Image

-Messaging andCommunication-Ad Reactions-Media Habits-Social Media

-Shopper Insights

Exhibit 8.2 VOC Strategy and Elements by Organizational Functional Areas

c08 3 September 2012; 12:6:39

22

Organizational Factors:- Brand, Marketing- Communication- Product- Sales- Customer Service

External Factors:- Competition- Industry- Current Events, Economy- Word-of-Mouth, Peers

Needs andWants

Assessment

Perceptionsand Attitudes

Tracking

Usage and ExperienceMonitoring

Customers(Current,Lapsed,Future

Prospects)

Satisfactionand Loyalty

Tracking

Exhibit 8.3 VOC Strategy Based on the Customer’s Experience Stage

c08 3 September 2012; 12:6:39

23

Product Design and DevelopmentConcept Testing, User Research→ Product Concepts, Prototype Testing, User Experience, Feature PrioritizationProduct Concepts, Prototype Testing, User Experience, Feature Prioritization

Beta Testing w/Select Customers and Marketing Materials DevelopementUser Feedback Research→ → Product Reaction, Future Use, Efficacy, Ease of Use, Compare to CurrentProduct Reaction, Future Use, Efficacy, Ease of Use, Compare to Current

Formal Product Launch via MarketingAd Testing for Message and Execution→ TV, Print, Online, Event Marketing, PRTV, Print, Online, Event Marketing, PR

Brand and Market PositioningNaming Research→ In-Person, Online ResearchIn-Person, Online Research→ Competitive LandscapeCompetitive Landscape

Qualitative Research: Focus Groups, In-Depth Interviews→ Benefits, Value Proposition, Concerns/Questions, Communication MaterialsBenefits, Value Proposition, Concerns/Questions, Communication Materials

Post-Purchase EvaluationSatisfaction and Loyalty Research→ Satisfaction, Future Use, Unresolved Issues,Satisfaction, Future Use, Unresolved Issues,Pricing, Training Needs, Product Enhancements,Pricing, Training Needs, Product Enhancements,Feature Prioritization, Value PropositionFeature Prioritization, Value Proposition

Quantitative Research, Conjoint Survey→ Pricing, Market Share, Feature EvaluationPricing, Market Share, Feature Evaluation→ Quantify the New Product Benefit over CurrentQuantify the New Product Benefit over Current→ Marketing Materials Testing and EvaluationMarketing Materials Testing and Evaluation

Soft Product Launch, Trial→ → User FeedbackUser Feedback

MONTH:MONTH: 1 2 3 4 5 6 7 8 9 10 11 12 13 +

Exhibit 8.4 New Product Launch Research Program for Innovative OnlineB2B Product

c08 3 September 2012; 12:6:41

24

PastPast

INFORMATION

What happened?

(Data mining andreporting)

How and why did ithappen?

(Data modeling andexperimental design)

What’s the next bestaction?

(Recommendation)

What’s the best andworst that can happen?

(Prediction, simulation)

How do we leveragewhat we already know?

(Dynamicinteraction/profiling)

How do we dynamicallymodify the site in realtime?

(Detection)

How can we apply datato the future?

(Ongoing optimization)

What is happeningnow?(Alerts)

What will happen?

(Trending,extrapolation)

INSIGHT

ACTION

PresentPresent FutureFuture

Exhibit 9.1 Effective Digital Analytics

c09 3 September 2012; 12:27:40

25

MarketingMarketing

Landing pageoptimization

Lifetime value/RFM/customersegmentation

Search engineoptimization

Sales readiness Scorecarding Site usagefor capacityplanningDisasterrecovery

Custom researchSales collateralDemo/GEO/firmagraphicanalysis

Search enginemarketing

Ad and mediaplan optimization

Social mediaoptimization

Application andproductperformance

Funnel and flowoptimization

RFP and RFI

Customer usageinformation

Financialperformance

Competitiveintelligence

Infrastructureenhancements

Tagging and QA

Behavioralanalysis

Customer value Dashboarding Performancemonitoring

ProductProduct Sales ExecutiveExecutive IT

Exhibit 9.2 The Ways Digital Analytics Can Help Functional Teams

c09 3 September 2012; 12:27:41

26

Tool How a Unique Visitor Is DefinedExampleof Tool

DigitalAnalytics

A count of deduplicated cookies during the time period. Inother words, if you stay at the beach house for all 30 daysduring an entire month. Then you are 1 “monthly uniquevisitor.” If the owner of the rental property was asked byhis accountant, “How many people stayed at the house?”the correct answer is 1. In that same scenario, you stayedevery day, correct? If the manager asks how many “peoplestayed at the house each day, you could say “30 dailyvisitors” stayed during the month—because you did stay30 times, once per day. You were “30 daily uniquevisitors” but only “1 monthly unique visitor.” This scenarioillustrates the potential for confusion between “monthlyunique visitors” and “daily unique visitors.”

GoogleAnalytics,Omniture/Adobe,Webtrends

AudienceMeasurement

Audience measurement uses black box data collectionmethods that are not transparent, but a few companies arein the process of being partially audited by the MRC.Similar to the algorithms within analytical software,audience measurement tools often combine data collecteddirectly from sites with data collected from a panel ofpeople who choose to have their digital behavior (such asWeb surfing) monitored in exchange for some incentive,such as free software. Some audience measurement firmsrefer to the joining of site analytics data and audiencemeasurement data as “unifying” the data.

Audience measurement companies take these datacollected about digital behavior, such as website visitation

comScore,Nielsen,Compete,Quantcast,Google

(Continued)

c09 3 September 2012; 12:27:41

Visitor-Tool Table

27

Tool How a Unique Visitor Is DefinedExampleof Tool

(and much more) and use proprietary statistical methodsto estimate the size of an audience to a website or anotherdigital asset.

Since audience measurement companies have changedtheir core methodology at the most fundamental levelaround 2010 to bring together panel data with site data,then I question entirely whether the panel-based estimateswere ever actually accurate historically for unique visitors.In fact, the disdain for audience measurement data fromthe most vocal critics and savvy analysts points atdramatic changes in unique visitor totals as direct proofhistoric panel only�based estimates site traffic were notonly wrong for years, but also based on an inherently datedand archaic model. If combining panel data with site datagives better estimates, then it is logical to conclude thatthe previous estimate was inaccurate.

c09 3 September 2012; 12:27:41

28

Unlock complexpatterns andtrends

Predict theoutcome andforecast thetrend

Optimize and acton the predictionand uncoveredpatterns

UUnlocking stage PPrediction stage OOptimization stage

Exhibit 10.1 Advanced Predictive Business Analytics

c10 3 September 2012; 12:30:1

29

Universe of US Companies by Employee Size Universe of US Companies by Employee Size US Companies % of Total Market Opportunities by sizeUS Companies % of Total Market Opportunities by size

500�EE26K

0.15%

50-499EE272K1.6%

10-49 EE1.3M7.3%

1-9 EE15.8M91%

35%

Key:

15.8M : 15,800,0001.3M:1,300,000272K: 272,00026K:26,000

30%

25%

15%

EE : employees

Exhibit 10.2 Business Opportunity Visualization

c10 3 September 2012; 12:30:2

30

Identify theQuestions

Master theData

TrackOutcomes

CommunicateInsights

Provide theMeaning

ActionableRecommendations

A

P

M

I

T

C

Exhibit 10.3 IMPACT Cycle: Analyst Guide for Creating for High-Impact Analytics

c10 3 September 2012; 12:30:3

31

BusinessBusinessChallengeChallenge

• Reducecustomerattrition

• Seniormanagementsponsor• Skilledanalyticalresources• Collaborationfrom othergroups• Customerexperience• Changemanagement andtraining• Metrics

• Internal andexternal datasources

• Transactionaldata• Usage• Service• Plan• Socio-demographic• Credit bureau• Macroeconomicdata

CustomerAttritionPredictiveModel toproactivelytarget at-riskcustomers.The attritionmodel provideslikelihood toattrite score toevery existingcustomer

• The attritionmodel’s insightsare distributedby roles andresponsibilities:PowerPointpresentation tothe executiveteam andmanagers.Meeting andWeb demo tothe customertouch points

• Point of salesvia Webapplicationavailable tocustomer

• Proactiveretentionactivities basedon customerattrition scoresegment

Measurement:• % decrease incustomerattrition• % increase inlow-valuesegment

• Integration ofdifferent types ofintelligences toharnesscustomeracquisition,retention, andup-sell• Launch of thefirst price planwith freeincoming callsin Canada• Visualization ofthe combinedintelligence atevery point ofsales

AnalyticsAnalyticsImplementationVision Data InsightsInsights

DistributeDistributeKnowledgeKnowledge

ExecutionExecutionMeasurementMeasurement InnovationInnovation

Exhibit 10.4 BASP Application in Telecoms: Customer Attrition, Fido Case Study

c10 3 September 2012; 12:30:4

32

Purpose

Goal

Inputs Factors:Internal andExternal Sources

AdvancedBusinessAnalytics

OutputsScoreandSegments

OutputsImplications

Acquisition retention and up-sell prioritization

Define SegmentsHigh, Medium, Low

Score 0 to 100

Predictive Models

FirmGraphic

Data

MacroEconomic

Data

TransactionalData

Online andOfflinePosting

Data

Seekerand Traffic

Data

Score and segment the universe of companies in US, Canada, and Europe

Identify best customers for growth and equip sales with product and marketing support to capturehigh-value potential

Exhibit 10.5 Business-to-Business Scoring and Segmentation Using Different Types of Data

c10 3 September 2012; 12:30:4

33

ORGANIZATIONALORGANIZATIONALCAPITALCAPITAL

EXTERNAL STATELabor Supply Changes

Economy SlowingGlobalizationTechnology AdvancesCompetitor ActionsNew Regulations

INTERNAL STATECEO Vision

Leadership QualityCultureBrandCapabilities

Finances

HUMANHUMAN STRUCTURAL RELATIONALRELATIONAL

Exhibit 11.1 Situational Assessment

c11 3 September 2012; 12:31:38

34

Competencies for TodayCompetencies for Today Capabilities for TomorrowCapabilities for TomorrowSkills: technical and interpersonalKnowledge: technical and behavioralMotivation: willingness to workCommitment: belief in the companyEngagement: emotional involvementCreativity: ability to innovatePotential: ability to growFlexibility: deal with changeLeading: bring out the best

Skills: technical and interpersonalKnowledge: technical and behavioralMotivation: willingness to workCommitment: belief in the companyEngagement: emotional involvementCreativity: ability to innovatePotential: ability to growFlexibility: deal with changeLeading: bring out the best

How will they differ if change comes from...Technology vs. Regulations vs. Economics vs. Labor Demographics or Other Forces?

Exhibit 11.2 Current Competencies to Future Capabilities

c11 3 September 2012; 12:31:38

35

Job Group Sourcing Selection Results

Applicants Newspaper advertising Personal interviews Performance

Job boards, referrals, Group interviews Potential rating

Professional journal ads Tests, assessment Pay progression

On-boarding Retention

c11 3 September 2012; 12:31:38

HR Process Management Model

36

Best that can happen?-----------------------------------------------------------Optimization

Most likely to happen?----------------------------------------Predictive Modeling

Will trend continue?-----------------------------------------Forecasting

Why is it happening?-----------------------Statistical Analysis

How much/often?----------------Ad Hoc Reporting

Degree of Intelligence

Degr

ee o

f Ana

lysi

s

What happened?-----Standard Reporting

Exhibit 11.3 Report Value Hierarchy

c11 3 September 2012; 12:31:39

37

STRATEGICSTRATEGICLabor Cost – Productivity – Attrition

Customer Attraction – Conversion – SpendGross Margins – Profitability – Market Share

OPERATIONALOPERATIONALProcess Costs – Cycle Times

Production Output—Service QualityEmployee Productivity

LEADINGLEADINGLeadership – Engagement

Readiness – L & D InvestmentMission Critical Retention

CultureIMPROVEMENT

IMPROVEMENT

PROJECTPROJECT

EFFECTSEFFECTS

Exhibit 11.4 Integrated Reporting

c11 3 September 2012; 12:31:39

38

45%

40%

35%

30%

25%

20%

15%

10%

5%

0%

TV a

nd V

ideo

Inte

rnet

Radi

o

Mob

ile

News

pape

rs

Mag

azin

es

Othe

r

% Ad Spend per Channel% Time Spent per Channel

Exhibit 14.1 Time and Ad Spend for Various MediaSources: www.emarketer.com/blog/index.php./numbers-major-media-ad-spending/; www.emarketer.com/PressRelease.aspx?R=1008732.

c14 3 September 2012; 12:38:2

39

Cumulative percentage of churners from randomly selected sample

Cumulative percentage of churners from model score

1009080706050403020100

00

1020

3040

5060

7080

9010010010098989590

80

65

50

10 20 30 40 50 60 70 80 90 100

Target Population SizeTarget Population Size

Perc

enta

ge o

f Chu

rnPe

rcen

tage

of C

hurn

Exhibit 15.1 Cumulative Gain Chart: Model Score vs. Random Selection

c15 3 September 2012; 12:41:42

40

Solutions Audiences Channel Tools Activities

Scoring C-Level Executive Navigator j Executive PowerPoint Presentationj Executive Toolsj Executive Dashboard or Web Application

j Meetingsj Executive One-Pagerj Newsletter

Manager Navigator j Manager PowerPoint Presentationj CRM System, Where the Info Is Availablej Sales Manager Dashboard

j Regular Meetingsj Project Plan

Sales and ServiceRepresentatives

Navigator j PowerPoint Presentationj CRM System, Where the Info Is Availablej Sales Dashboard

j Regular Meetingsj Project Plan

Exhibit 15.2 Delivery Tools and Activities for Customer Scoring

c15 3 September 2012; 12:41:42

41

Executive Sales Services Marketing Product

Ecom PR Employees Wall Street Analysis

Sender Message

Feedback

Receiver

Analytics Team Dedicated Navigator

Finance Consumer Product Employer Product

Business Partners

Exhibit 15.3 Analytics Team Engagement Model: Communication via the Navigator

c15 3 September 2012; 12:41:42

42

Get feedback: formal & Informal

Normalize delivery schedule

Identify your key resources

Locate delivery channels & tools

Lay out your key message

Evaluate your target audience

Set clear goals

7

6

5

4

3

2

1

Exhibit 15.4 The SELLING Strategy for Effective Analytics Communication

c15 3 September 2012; 12:41:43

43

Step 1 Step 2Step 2 Step 3Step 3

AnalyticsAnalyticsFundamentalFundamentalQuestionsQuestions

This steprefers to thebusinessanalyticsquestionsthatanalytics aimsto address

This step isabout howto putanalyticsintoexecution

This step willprovide adefinition ofperformancetracking

This step willoutline themainreasons whyone needs totrackbusinessperformance

This stepprovidesperformancetrackingexamples formultipledepartments inany givenorganization

AnalyticsAnalyticsExecution

What IsWhat IsPerformancePerformanceTracking?Tracking?

Why TrackWhy TrackBusinessBusinessPerformance?Performance?

PerformancePerformanceTracking CasesTracking Cases

Step 4Step 4 Step 5

Exhibit 16.1 Business Performance Five-Step Process

c16 10 September 2012; 17:57:3

44

Functions Past Present Future

Analytics Team What happened? What is happening,and why is thishappening?

What will happen?

Executive Team How did we do? How are we doing? What should we do?

Exhibit 16.2 Advanced Business Analytics Questions

c16 10 September 2012; 17:57:4

45

CRM Status

Reach

Prospect Candidate Customer Inactive/Churn

Reach

Acqusition

Acquire

Conversion

Convert

Retention

Retain Win Back

CLC Stage

CLC Objective

Goal

Solutions

Action/Execution

Metrics/Data Elements

Benchmark/Measurement

Time Frame

Goal

Solutions

Action/Execution

Metrics/Data Elements

Benchmark/Measurement

Time Frame

Functional Team(*)

(*)Functional Team includes: Marketing, Sales, Customer Service, IT, Finance, HR, Product, Operations

Analytics Team

CRM/LCM

Exhibit 16.3 Customer Experience One-Pager

c16 10 September 2012; 17:57:4

46

6 8HM

CLTV

L

HM

The Y-axis is the CLTV: Customer Lifetime ValueThe Y-axis is the CLTV: Customer Lifetime Value

The X-axis is the Conversion: Propensity to ConvertThe X-axis is the Conversion: Propensity to Convert

The letters H M L stand for

H: High Segment 9: High Conversion and High CLTV

Segment 8: Medium Conversion and High CLTV

Segment 7: High Conversion and Medium CLTV

M: Medium

L: Low

L

9

753

1 2

Conversion

4

Exhibit 16.4 Customer Prospects Segmentation Grid

c16 10 September 2012; 17:57:4

47

6 8HM

CLTV

L

HM

The Y-axis is the CLTV: Customer Lifetime ValueThe Y-axis is the CLTV: Customer Lifetime Value

The X-axis is the Churn: Propensity to ChurnThe X-axis is the Churn: Propensity to Churn

The letters H M L stand for

H: High Segment 9: High Churn and High CLTV

Segment 8: Medium Churn and High CLTV

Segment 7: High Churn and Medium CLTV

M: Medium

L: Low

L

9

753

1 2

Churn

4

Exhibit 16.5 Customer Churn Segmentation Grid

c16 10 September 2012; 17:57:4

48

DepartmentBusinessChallenge

AnalyticsSolutions

Execution/Actions Measurements

Advanced Analytics

Marketing

Sales

Customer Services Reduce Customer Churn Leverage at-risk customerscoring as described ontable to prioritize proactiveretention activities.

Prioritize daily activities toproactively reach out tothose customers basedon the scoring andsegmentation; leveragingpredefined offering andmessaging for inbound calls.

-Number of outreaches to at-risk customers-Number of at-risk satisfied customers-Overall customersatisfaction

-Intensifying touches onhighly scored account.

-Renewal customer-Renewal amounts-Renewal rate and loyalty

Prioritize daily activities toreach out to at-riskcustomers based onscoring and segmentation.Optimize the coveragemodel.

Leverage churn predictivemodel to prioritizeactivities on high CLTVand high likelihood tochurn customers in highsegment.

Reduce Customer Churn

Reduce Customer Churn

Reduce Customer Churn Build churn predictivemodels to identify who arecustomers who will churn.Why and when?

Provide the target list ofmost likely to churncustomers prioritized byother variables.

-Model performance-Increase customer retention.

Track:-Response rate-Renewal rate-Increase in customer

retention and loyalty

Send target marketingCRM campaigns to everycustomer’s segment (at-riskto churn).

Leverage churn predictivemodel’s findings to developsome proactive retentionoffering and messaging forevery segment.

Exhibit 16.6 Outreach Execution Table: Customer Churn

c16 10 September 2012; 17:57:5

49

What Happened?What's Happening andWhy Is This Happening?What Will Happen?

AdditionSubtraction

InnovationAnalytics

-Customer base-Product-Services-Technology-Market-Competition

Intersection-Innovation

Exhibit 17.1 Analytics and Innovation Intersection

c17 3 September 2012; 12:47:35

50

Documents,Résumés, Job

Description

Structured DataStructured DataCall Center LogCall Center Log

Unstruc-turedData

SocialMedia,Blog,NewsFeed

•LinguisticStatistics•MachineLearningTechniques

ExtractionAnalysisModeling

Key ConceptsSentimentsRelationship

StructureFormat

PredictiveModels

Exhibit 18.1 Unstructured Data Analytics Process

c18 3 September 2012; 12:49:25

51

Firstknownuse of

table dataarrangedin rows

andcolumus

Two-

dim

ensl

onal

gra

phwa

s in

vent

ed b

y De

scar

tes

Mod

em S

tatis

ties:

(A. F

ishe

r and

C. S

pear

man

)Fa

ctor

Ana

lysi

s an

d Pr

inci

pal

Com

pom

ent A

naly

sis

Line graphBar chartPie chartLine chart

wereinvented byW. Playfair

HP Luhndefined

BI as TextAnalytics:

basisstats

analysis ontext terms:idea of text

summarizationemerged

Data

base

tech

nolo

gies

: Rel

atio

nal D

atab

ase

Netw

ork

hier

arch

ical

Dat

abas

e

Text

anal

ytic

s +

Dat

a m

inin

g:Al

ta V

ista

Ter

agra

m

BI emergedas softwarecategoriesand field of

expertise, but thefocus is onnumerical

and structureddata

2nd CenturyEgypt

17th

Century1786–1801

1958

Business Intelligence and Text Analytic Timeline

Busi

ness

Inte

llige

nce

and

Text

Anal

ytic

s Hi

ghlig

hts

1960–1980 1980–19901990–Present

Today/Future

987654321

18th 19th

Century

- Monster (PRS)

- Apple (SIRI)

- Microsoft (FAST)

- HP (Autonomy)

-Oracle (Endeca)

Era of AdvancedText Analytics andSemantic Search

Exhibit 18.2 Evolution of Unstructured Data Analytics

c18 3 September 2012; 12:49:26

52

Semantic Concepts Context

Searches on the meaning of words Can distinguish the context in which a wordis used.

Understands differences between relatedconcepts such as job skills, industries, andeducation

For instance, when parsing a resume with ajob seeker named Harry Ford

Understands the hierarchy of concepts Worked for Ford Company and went to FordBusiness School

Exhibit 18.3 Semantic Analytics of Résumés

c18 3 September 2012; 12:49:26

53