advanced analytics behind a smart planet - costa mesa 061010

8/2/2019 Advanced Analytics Behind a Smart Planet - Costa Mesa 061010

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2010 IBM Corporation

IBM Business Analytics and Optimization Services

The Advanced Analytics

Behind a Smarter Planet

David BrannJune 10, 2010

Costa Mesa, CA


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IBM Research

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Advanced Analyticsis the use of data and models to provide insight to guide decisions

Models

Analytics

Data

Informed Decisions

Data sources:Business automation

InstrumentationSensorsWeb 2.0

Expert knowledgereal world physics

Model:a mathematical or

algorithmicrepresentation of

reality intended to

explain or predictsome aspect of it

Decision executedautomatically or

by people


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Models Have Been Used for Decades in Engineering


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Some Advanced Analytics Have Become Mainstream

Digital Maps (i.e.,data)

User interface

Robust Algorithm

Sufficient computation power

To go mainstream, advancedanalytics needs fourcomponents:


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Why Are Advanced Analytics Important Now?

Models

Analytics

Data

Usage Trends

Technology TrendsBusiness Trends

Velocity ofdecisions

Manage and

trade-off broadsets of resources

Automation iscommon

Data and

computing poweris abundant

Competition on Analytics


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Advanced Analytics Provide Competitive Advantage

Sales Analytics for

IBM increasesrevenue byover $1B

Optimized generationsaves Red Elctrica deEspaa50,000 per day

CollectionOptimization will

increase NY revenueby $100M over 3 years

Customer

RelationshipAnalytics for MTNreduces customerchurn

Claims analytics saved

SSA over $2 billionand reduced the

average approval timeby 70 days


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Analytics Landscape & IBMs Software Footprint in These Areas

Degree of Complexity

CompetitiveAdvantage

Standard Reporting

Ad hoc reporting

Query/drill down

Alerts

Simulation

Forecasting

Predictive modeling

Optimization

What exactly is the problem?

What will happen next if ?

What if these trends continue?

What could happen. ?

What actions are needed?

How many, how often, where?

What happened?

Stochastic Optimization

Based on: Competing on Analytics, Davenport and Harris, 2007

Descriptive

Prescriptive

Predictive

How can we achieve the best outcome?

How can we achieve the best outcomeincluding the effects of variability?
http://www.spss.com/



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Why Now? Decision making has never been more challengingthan in todays business environment

new economics, globalization, increased risk interconnectedness

and an explosion of information

all demanding deep insight & predictive knowledge for smarter business outcomes

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to Predict & Act

Sense

& Respond

From Sense & Respond

ActCut Costs

Reduce Risk

Increase ProfitPredict

OptimizedBusiness

CompetitiveAdvantage

Predictive Analytics

Analyzes patterns in data to predict potential future outcomes



IBM Research

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Driving Smarter Business OutcomesEnabling customers to predict future events & proactively act upon that insight to drive smarterbusiness outcomes

Capture Predict Act

DataCollection

Deployment

TechnologiesPlatform

Pre-built Content

Statistics

Attract Up-sell Retain

TextMining

DataMining

Data Collection delivers anaccurate view of customer

attitudes and opinions

Predictive capabilities bring repeatability toongoing decision making, and drive confidence

in your results and decisions

Unique deployment technologies andmethodologies maximize the impact of

analytics in your operation


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Applying advanced analytics tostructured data is one thing

combining that with unstructured

data can be a true differentiator

Structured Data Unstructured Data

Case Study: Leveraging Structured and Unstructured Data


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Number of Recipients:

Total benefits:

Administrative costs:

12.4 Million

$105 Billion

$5 Billion

Background U.S. Social Security Administration (SSA)

Disability Benefits


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InitialApplication Reconsideration Hearing Appeals

FederalDistrict Court

Level 1 Level 3 Level 4 Level 5Level 2

Up to 5 years

738,000cases inbacklog

97 days on average

Historically - Lengthy Disability Approval Process

Thousands of clearly disabled peoplewere stuck in the process, creatingdissatisfaction and negative press


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AllApplications

ScoringModel:

Is Applicant

ClearlyDisabled?

QDD UnitDecision andbenefits in 11

days

Not clearlydisabled

goes throughexisting

process

NormalAdjudication

andapplication

levels

Decision: 97days on

average, upto 5 years

Request additional

medical records Request medical

examination

Quick Disability Determination (QDD) a new process

SSA conceived of a newprocess

Create a centralizedteam for expeditedreview of cases

Automatically sendworkload to this group

Clearlydisabled arefast tracked


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For example

Recipient enters into system

I have cancer. The cancer hasspread. It is stage four livercancr

Misspelled VocabularyCorrection (MVC)

Robust Risk Minimizer (RRM)

Not keywords, but sophisticatedunderstanding of context

Eliminates redundancies,mistakes, and other text issues


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Benefits and results for the Social Security Administration

Has reduced the cycle time to process10% of applications for disability from:

97 days to 20 days

Has used predictive modeling to save

$2 billion in disability benefit renewalcosts since inception

Business benefits of text analytics:

Reduces cycle time and costs Facilitates prioritization (and triage)

Increases consistency of decisions


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"The work we did with IBM

fundamentally changed our

business strategy and how

we looked at our own

brand".

..been the most successful

campaign for Vegemite todate, and that's saying

something over 85 years of

history.

- Simon Talbot Kraft Australia

IBMs Corporate Brand and Reputation Analysis (COBRA)solution gives new insights delivers breakthrough results

Business challengeKraft recognized that there was a whole segment of consumer feedback thatwas going un-monitored. That segment was user generated content on the web(often referred to as Web 2.0), in blogs, message boards and other socialnetworking sites. Seeking a better understanding of consumer sentiment aheadof a potential product update, Kraft worked with IBM Research to identify whatwas on the minds of its Vegemite customers worldwide.

SolutionIBM helped Kraft implement our Corporate Brand and Reputation Analysis(COBRA) solution to analyze unstructured data from social media to provideinsight and root cause analysis as well as statistical tracking and advancedalerts to events. The COBRA analysis has led to a change in Krafts marketing

strategy for Vegemite.

Benefits

COBRA helped Kraft Australia identify the strong personal identification thatits customers had for Vegemite

The marketing campaign resulted in a significant increase in sales withinthe first two months, which was the most successful campaign in 85years.


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Exponential Smoothing Moving average

Creates a stable (or smoothed) forecast

Relatively insensitive to larger shifts in

demand

Forecasts are based solely upon recent

historical demand

Responds to changes in demand in a

delayed fashion

Leveraging Predictive Modeling to Improve Forecast Accuracy

Causal Forecasting Linear Regression

Utilizes:

Known business practices

Customer demand cycles

Market driven events

Able to react to attributable shifts indemand

Forecasts incorporate recent and historical

demand


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How does optimization support decision making?

What-If Analysis

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Well-Documented Optimization ROI Cases using ILOG CPLEX

*Franz Edelman Competition Finalists, Science of Better, http://www.scienceofbetter.org, Published Case Studies

2 Chilean Forestry firms* Timber Harvesting $20M/yr + 30% fewer trucks

UPS* Air Network Design $40M/yr + 10% fewer planes

South African Defense* Force/Equip Planning $1.1B/yr

Motorola* Procurement Mgmt $100M-150M/yr

Samsung Electronics* Semiconductor Mfg 50% reduction in cycle times

SNCF (French RR)* Scheduling & Pricing $16M/yr rev + 2% lower op ex

Continental Airlines* Crew Re-scheduling $40M/yr

AT&T* Network Recovery 35% reduction spare capacity

Grant Mayo van Otterloo* Portfolio Optimization $4M/yr

Pepsi Bottling Group Production Sourcing $6M inv reduction + 2% fewer milesFonterra Dairy Distribution $15M annual savings

NA Brewing Company Mfg Sourcing + Distribution $150M/yr transportation savings

US Water Products Mfg Inventory Optimization $6.2M working capital reduction
http://www.scienceofbetter.org/http://www.scienceofbetter.org/


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Source of Growth: Your Customer

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Customer centricity is the hot topic of the day. But fundamental challenges of

understanding and action are preventing most from achieving real change.

Understanding and acting on customer insight is the key

Challenge #1: how do we stop guessing at who our customers are and what theyre

doing?

Challenge #2: how do we use customer insights to transform our business?


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Highly accurate 18-month forecast

Customized, company-specific

forecast of revenue

Foundational customer view, based on

many-dimensional behavior models

Customer-driven allocation

of resources

Personalized treatment of every

customer in every interaction

Advanced Customer Analytics Offerings

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IBMs Advanced Customer Analytics offerings are designed to deliver ever-increasing

business value and insight, achievable through evolutionary capability building.

Industry Revenue Index

Company Revenue Index

Feature Vectors &Action Clusters

Foresight Optimizations

Next Best Action


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Industry Revenue Index

Client-Specific Revenue Index

Feature Vectors &Action Clusters

ForesightOptimizations

NextBest

Action

Start With the Basics and Build

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Starting with basic capability and progressing upwards enables ever-increasing benefits

realization with an evolutionary build of capability.

$

$

$

$

$BenefitsRealizationCapability

Investment

Customer Experience Re-Design

Decision Integration into Business

Master Data Management

Service-Oriented Architecture

Real Time BI

Detailed Data Inputs

Stand-Alone Application

Manual Integration w/

Planning Processes

Ad Hoc Data

Extracts

Manual Process

Integration

Manual Historical

Revenue Data Inputs

None Improved Labor

and Inventory Planning

Enriched Customer

View

Relevance Drives Conversion

and Loyalty

Offerings

Optimized Allocation

of Scarce Resources

IBM R h


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Customer Foundation: Action Clusters

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Revolutionary customer segmentation approach tailored uniquely to each clients business

model, customer data, and operational practices, yielding highly actionable customer groups

Most segmentationapproaches have twoor three dimensions:

Transactions

Sa

les

Geography

Income

Typically not actionable becausecustomers are more complex

than 2 or 3 dimensions theseapproaches do not reflect

customer uniqueness

Action Clusters have 8-13 dimensions and

they are based onbehavioral responsevoting records to

aspects of the clients

value proposition

PreferredProduct

Categories PreferredChannel

Participationin LoyaltyProgram

Use of In-House Credit

Card

Use ofService

ProgramsReturn /ExchangeBehavior

Breadth ofCategoriesShopped

Length ofTime as

Customer

Recency +Frequency +

Value

Responseto Media

Time untilRepurchase inKey Categories

Action Clusters are:

Highly homogeneous it isdifficult to get into a cluster based

on 13 dimensions, ensuring that the

customers are very similar to one another Highly differentiated the AC process ensures

as much distance between clusters as possible

Highly actionable because the clusters arebased on the customers response to variousdimensions of the value proposition, they

facilitate highly-specific targeting

Other dimensions are carried

along as reporting variables,

including gender, age, income,

geography, ethnicity, etc.

IBM R h


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What we did: Leveraged Operations Research and BusinessAnalytics to optimize our sales resource strategy

UnderstandPerformance

Select TargetMarkets and Clients

Select Coverage,Deploy and Align

Resources

Enhance sales process by

directing sellers to bestsales leads Estimate probability that a

company will buy a specificIBM product brand

Understand sales productivity

and revenue performance bymodeling relationship betweensales capacity and revenue

Provide insights into howcapacity, deployment andtransactions translate intoperformance

Align sellers to the best client

opportunities and according toestablished guiding principles

Growth and Performance(GAP)

OnTarget Territory OptimizationProgram (TOP)

The IBM Sales Model provides an end-to-end process supported by data and tools to deliverbusiness results

Individual Sellers CFO office, Brand CFOs,Sales Executives

Sales Managers

2006 2008 2009

Develop forward-looking

view of opportunity by client Identify accounts where

there is significantuntapped revenue

What if all clients lookedlike our best clients?

IdentifyOpportunity

Market AlignmentProgram (MAP)

Sales Managers andExecutives

2007

IBM R h


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What we achieved so far

Estimated futurerevenue opportunity foreach client

Shifted 800 resources todifferent accountsegments

Estimated revenueimpact $600M(worldwide)

GAP TOPMAP Decision-support tool

delivered in web portal

Used by the CFO office andBrand CFOs worldwide to setand distribute revenue targetsand conduct performance

deep dives $140M worldwide revenue in

2008-2009 from GAP drivenperformance deep dives in 3business units

Web tool for sales managers todesign optimal territories

Currently supports 11K salesresources in key IBM brands,20K by the end of 2010.

Annual worldwide revenuebenefits for 2010 are estimatedat $600M for all brands

Quantile estimationClassification,probability estimation

Regression, time-seriesanalysis, causal modeling

Large scale mixedinteger programming

and looked into some challenging technical problems

OnTarget Delivered leads via web

portal

Close-rate up 2%

Estimated annual revenueimpact $468M (worldwide)

Note: IBM Benefit measurements consistent with external benchmarks: ZS Associates: Sales territory alignment projects can improve revenue performance by 2-7%* BCG: Based on prior consulting engagements, programs like the IBM Sales Model can have 5-12% revenue impact**

Sales Territory Alignment: An Overlooked Productivity Tool, Zoltners (Nothwestern Univ) & Lorimer (ZS Associates), Journal of Personal Selling & SalesManagement, Summer 2000** Boston Consulting Group

IBM Research


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SummaryAdvanced analytics extending from the domain of science &engineering to the world of business

Fueled by the availability of data, computationalpower and the need to make better choices

Increased adoption of advanced analyticsrequires:

the creative identification of challenges and opportunitiesnot fully addressed by descriptive analytics

the initiative and focus to compete not just on the power ofdata architectures and business intelligence, but on thepredictive and prescriptive power of advanced analytics

models

the effective inclusion of advanced analytics models intooperational and transaction systems.

Ask new questions

Models

Analytics

Data

Insight

IBM Research


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Questions

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advanced analytics behind a smart planet - costa mesa 061010

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