Group-9 Predictive Analytics

7/30/2019 Group-9 Predictive Analytics

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Group 9

Earle Prithviraj DM14116

Sankuru Anil Kumar DM14203

Maddula Mahendra Avinash DM14233


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EXPERTS VIEWS/DEFINITIONSOF ANALYTICS &

PREDICTIVE ANALYTICS:

Using analytics is like driving your car but watching traffic through the rear-view mirror, not seeing

whats ahead and thereby in danger of crashing

the application of computer technology,

operations research and statistics to solve

problems in business and industry. Analytics

is carried out within an information system.

the application of computer technology,

operations research and statistics to solve

problems in business and industry. Analytics

is carried out within an information system.

Tom Davenport

noted author

What is Analytics?


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WHATISPREDICTIVEANALYTICS?

Using predictive analytics is like driving your car and watching traffic

through the front windshield, anticipating traffic, making course corrections

to avoid traffic jams and getting there faster and safer

predictive models exploit patterns found in

historical and transactional data to identify risksand opportunities. Models capture relationships

among many factors to allow assessment of risk

or potential associated with a particular set of

conditions, guiding decision making for candidate

transactions.

Any solution that supports the identification of

meaningful patterns and correlations among

variables in complex, structured and

unstructured, historical, and potential future data

sets for the purposes of predicting future events

and assessing the attractiveness of variouscourses of action.


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WHATIS PREDICTIVE ANALYTICS?

A set of business intelligence technologies that uncovers

relationships and patterns within large volumes of data

that can be used to predict behavior and events

Predictive Analytics is forward looking, using past

events to anticipate the future


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WHATIS PREDICTIVE ANALYTICS?

Other BI technologies are

deducting in nature

validating their

hypotheses

Predictive Analytics is

Inductive in nature.

pull out meaningful

relationships and

patterns.


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HOW ANALYTICSAND PREDICTIVE ANALYTICS COMPARE

Predictive Analyticsare more sophisticatedanalytics that forward thinking in nature

Analyticsis the understanding of existing (retrospective)

data with the goal of understanding trends via comparison

Developing analytics is the first step towards derivingpredictive analytics

They used for gaining insights from mathematical and/or

financial modeling by enhancing understanding, interpretation

and judgment for the purpose of good decision making


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HOW ANALYTICSAND PREDICTIVE ANALYTICS

COMPARE

Attribute Analytics Predictive Analytics

Purpose Understand the past

Observe Trends

Catalyst for Decision

Gain Insights

Make Decisions

Take Action

View Historical and Current Future Oriented

Metrics type Lagging Indicators Leading Indicators

Data Used Raw & Compiled Information

Data Type Structured Structured and

Unstructured

Benefits Gaining anUnderstanding of data

Productivity

Improvement

Gaining Information &Insights

Process Improvement


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ANALYTICS


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ANALYTICAL TECHNIQUES

Descriptive Model Prediction Model Decision Model

Find clusters of data

elements with

similar characteristics

Focus on as many

variables as

possible

Examples: customer

segmentation

based on socio-

demographic

characteristics, life

cycle, profitability,

product preferences

Find causality,

relationships and

patterns between

explanatory variables

and dependent variables

Focus on specific

variables

Examples: next

customer preference,

fraud, credit worthiness,

system failure

Find optimal and most

certain

outcome for a specific

decision

Focus on a specific

decision

Examples: critical path,

network

planning, scheduling,

resource

optimization, simulation,

stochastic

modeling


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ANALYTICS MATURITY CYCLE


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PROCESSOF PREDICTIVE ANALYTICS

ProjectDefinition

Exploration

DataPreparation

ModelBuilding

Deployment

Model

Management


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Most process for creating predictive models incorporate the followingsteps

1. Project Definition / Business Understanding Define business objectives and desired outcomes

2. Exploration / Data Understanding

Analyze source data to determine appropriate data, model buildingapproach and scope

3. Data Preparation Select, extract and transform data to create models

4. Model Building Create, test and validate models, and evaluate them

5. Deployment Apply model results to business decisions or processes

6. Model Management Manage models to improve performance, accuracy, control access ,

promote reuse


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PREDICTIVE ANALYTICS METHODS

Analysts build models using differenttechniques: neural networks, decision trees,linear regression, nave Bayes, etc.

Skill in creating effective analytic model isknowing which models and algorithms to use

Many analytic workbenches now automaticallyapply multiple models to a problem to find the

combination that works best.Advances make it possible for non-specialists to

create fairly effective analytic models


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PREDICTIVE ANALYTICS TOOLS

Open Source Predictive Tools Commercial Tools

KNIME Oracle Data Mining (ODM)

R Minitab

WEKA SAS and SAS Enterprise Miner

Orange IBM SPSS Statistics and IBM SPSS

Modeller

Rapid Miner Oracle Data Mining (ODM)

Widely used tools are:


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APPLICATIONSOF PREDICTIVE ANALYTICS

Retail Marketing

FinancialServices

Online andSocial

advertising

Health Care andPharmaceuticals


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RETAIL ANALYTICS

Mark Down Optimization

For leading retailers, markdown optimization events providesan opportunity to maximize margin, sell-through, and inventoryvalue while improving velocity of product assortment.

Work Force Optimization Workforce optimization supports the business with key insights

into how its workforce is performing. In the closely related areaof workforce management, the emphasis is on improvingoperational efficiency and managing the workforce effectivelywhile keeping overall costs at a minimum

Supply Chain Optimization

A natural offshoot of Forecasting is optimizing vendor order andstore order quantitiesa trade-off between the inventory-holding and stock-out costs.


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MARKETING

Audience Segmentation

A brands audience can be represented and profiled at varyingdegree of detail. A basic overall profiling is the first step to get avery high level understanding of the audience, but it cannotanswer several questions.

Market Basket Analysis Retailers want to understand which products/brands sell together

(affinity) and which products/brands cannibalize each other.

Applying analytics to historical POS data at he basket-level, wecan track affinity and cannibalization relationships betweenvarious products/brands/categories across different

countries/regions/stores. We can quantify the financial impact ofthese relationships, and also recommend promotional and pricingstrategies specific to a product relationship.Competitive Analysis

With more and more marketing budgets moving from traditionalchannels to social media channels, there is a clear interest amongbrand managers to better understand what can social do for them.


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FINANCIAL SERVICES

Risk Management

Attract and grow lower-riskhighly profitable customersegments .

Tighten controls over creditquality, loss, pricing andexposure relative to reserveand funding requirements .

Expand and strengthen therange of risk factors to refineand reduce errors in lossforecasting

Examples: ApplicationScorecards, FraudManagement

Account andPortofolioManagement

Maximize account value,

minimize risks and makeoptimal portfolio-leveldecisions.

Collections:

Identify customers withtemporary setbacks whererefinancing can reduce lossesand build loyalty .

Assign treatment approachesand resource allocationrelative to repaymentpotential .

Determine which customersto negotiate settlement orrefer to agencies beforeexpending precious resourceson unproductive collectionattempts .


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ONLINEAND SOCIAL ADVERTISING

Bid Optimization

for OnlineAdvertising

All online ad exchanges function

through a dynamic auction marketwhere marketers compete for theaudience they would like to reach.

Deploying optimal biddingstrategies that are tailored to the

end-goal of the advertiser isparamount to succeed in these

markets

Example: A company which is afast-growing social media start-up,

could need a developed bidrecommendation engine that

leads to significant reductions incost-per-click (CPC) and delivers

higher post-click userengagement rates.

CustomerAcquisition

through SocialMedia

Social media provides uniqueability to target potential

customers based on variousdemographic and psychographic

dimensions.

Example: A company acquirecustomers for a major credit card

issuer through Facebookadvertising. Predictive modeling is

used to develop models thatminimizes the cost per customerand acquires across a complexproduct and offer mix spanning

major international markets.


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Waiting time analysis

Length of stay analysis and optimization

Procedure cost analysis and optimization

Patient-level costing

Staffing planning and optimization

Supply and demand planning

Patient satisfaction analysis, reporting and improvement

Clinical performance analysis

Healthcare

Applications


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TRENDSIN PREDICTIVE ANALYSIS

While there is substantial increase in interest forPredictive Analytics in the BI community, feworganizations have taken the plunge

A lot of companies want to do predictiveanalytics, but have yet to master basic reportingDeloitte Consultings Miller

Only about 1/3 of organizations say they have

implemented predictive analytics in a maturefashion that uses well defined processes andmeasures of success that enables them tocontinuously evaluate and improve theirmodeling efforts


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BENEFITSOF ANALYTICSAND PREDICTIVE ANALYTICS

Benefits of analytics: productivity gains throughimproved data-gathering processes results in less timerequired for producing reports and metrics

Takeaway:Both types of gains are beneficial butimprovements in analytics are NOT as scalable as tothe benefits in predictive analytics which arerepeatable, virtuous and scalable

Benefits of predictive analytics: process improvement gainsthrough improve revenue generation & cost structures leading toenhanced decision making


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BENEFITSANDPITFALLSOF PREDICTIVE

MODELING

Benefits of Predictive Modeling

Multivariate pricing has significant advantages over traditional techniques

Corrects methodological flaws

Does more with limited data

Provides better diagnostics to support decision-making

Eliminates time-consuming adjustments

Predictive modeling is being successfully applied within the insurance industry for

a wide variety of applications including

Pricing

UW

Marketing

Claims


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Pitfalls of Predictive Modeling

Reliable Data

IT Availability

Shortcutting the Process

Treating Predictive Modeling as a Black Box Senior Management Understanding/Buy-In


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PROSAND CONSOF PREDICTIVE ANALYTICS:

Impressive predictive

power if you know how to

use it;

Flexible(transformations,

interactions, any factors

number, clusters, )

Based on good math

theory;

Sometimes not so easy to

explain;

Some models are over

parameterized and have too many

weights

and over-fit the data if you are notfamiliar how to avoid this

danger;

Could be time consuming/require

computing resources;

Concerns about privacy andsecurity, regulatory issues,

bandwidth for moving data to the

cloud, and increased complexity


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BUSINESS PEOPLE (USERS) VS TECHNOLOGY PEOPLE

(TECHIES) IN PREDICTIVE ANALYTICS:

The purpose of predictive analytics is to help organizations see relationshipsbetween business elements so senior management may craft targeted business

strategies and exploit opportunities on a timely basis with a focus on the future

In order to benefit from predictive analytics, people across the organization must

communicate and understand with one another but language often becomes a

barrier BI professionals often think language is SQL (Structured Query Language) and

business people often think language is reports, metrics and meetings

IT & BI professionals need to understand the language of strategy, business

models and performance while solving business not technology problems

SQL vs
http://www.clker.com/clipart-70079.html


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EXAMPLE CONVERSATIONBETWEEN CEO AND TECHIE:

Need marketsegmentation report,

now!

OK, what are theparameters andhow do you want itrendered?

CEO/Business People BI People

Conversations @ Work


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CONTD.

Huh? What is heasking me?

Just need my report!

CEO/Business People

Huh? What is heasking me?

Market

Segmentation?

BI People

The Communication is very important aspect in Predictiveanalytics. The Perspective of Business people and Techiesare very different.


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PREDICTIVE ANALYTICS : MACROAND MICROLEVELS

Macro Level:

Strategic Planning

Financial Planning

Focusing on Priorities

Competitive Analyses

Achieving Profit and Revenue

Targets

Developing Competitive

Advantages and Differentiation

Micro Level:

Improving business processes

Doing more with less budget

(working smarter not harder!)

Allocating resources appropriately

Understanding correlations and

sensitivities with customer

segments

To ensure long term financial

resources are available to run the

business

Developing Competitive

Advantages and Differentiation

Predictive analytics can provide timely feedback to executives on their strategic initiativeswithout feedback

course corrections may be too late

Predictive analytics provide leading indicators and insight to assist in planning for answering the big question:

What should we do next?next quarter, next year etc.

Organizations fail to recognize and misunderstand the necessary and intangible elements of people, skills, and

corporate culture and tying these elements back to their analytics, business model and strategiesCaution: this is along-term fix


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THANK YOU

Documents

Group-9 Predictive Analytics