predictive dynamix software

Predictive Dynamix Software

Predictive DynamixTurning Business Experience into Better Decisions

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Predictive Dynamix – Company Profile

• Founded in 1999• Based in Houston, TX• Provides advanced predictive data mining

software solutions• Three primary focus areas:

– Predictive Suite: Software for commercial data mining analysis

– Predictive Engines: Software components for integrating predictive data mining functionality into vertical applications

– Professional services


Predictive Dynamix – About the FounderPaul Duke has over 15 years of commercial experience in developing advanced pattern recognition applications.

• Predictive Dynamix:– Chief product visionary and architect– Leads professional services

• Prior to Predictive Dynamix:– NeuroCorp: Vice President of Development

• Headed software development & consulting services• Developed methods for geospatial data mining, demographic

analysis, sales forecasting, and retail network optimization– Texaco: Artificial Intelligence Group

• Headed applied R&D and consulting• Pioneered a variety of engineering, geoscience, and marketing

applications of neural networks, rule-based systems, & other AI methods

• Instructed “Decision Support & Expert Systems” MBA course at Rice University


Predictive ModelingPredictive modeling maximizes the value of domain experience anddata in order to make better decisions.

PredictiveModel

Historical /Operational

Data

Pattern classification/scoringTrend forecastingEvent detectionOutlier detection

Contributing Disciplines:Statistics / Artificial Intelligence / Signal & Image ProcessingEconomics & Finance / Information Theory / Control Theory

Operations Research / Medicine / Social & Behavioral Sciences


Predictive Data Mining Applications

In Virtually Every Industry:– Agriculture – Automotive– Charities– Electronics– Energy– Finance– Insurance– Healthcare– Travel & Leisure– Pharmaceuticals– Retail – Telecommunications

• Customer Relationship Management & Marketing– Targeted Marketing– Churn Forecasting– Consumer Propensity Modeling– Personalized Content Management

• Site Selection• Price Elasticity Modeling• Category Management• Fraud Detection• Credit Scoring• Political Forecasting• Countless science & engineering applications


Knowing Your Customers

Historical Customer Sales

Data

GIS-based Data

Customer Information

Previous Purchase Categories

Size, Recency, & Frequency of Previous Purchases

Customer Demographics:- Age - Income- Children - Education- Home Ownership, etc.Geocode

Web-basedClickstream

Data

“Interest” Categories

Recency of Website Visits

Frequency of Website Visits

Regional Competitive Intensity

Local Commercial Makeup

Payment History

Other Locational Factors

Other Historical Sales Factors

Other Web Factors

Forecast likelihood of response to a product campaign

Predicting lifetime customer value

Forecast likelihood of a transaction being fraudulent

Scoring for credit worthiness

Forecast likelihood of a customer switching to a competitor


Knowing Your Stores

Detailed SiteData

GIS-based SiteData

Store Information(Client &

Competitor)

Historical Sales

Brand

Site Demographics:- Population - Income- Businesses - EducationGeocode

GIS-based Customer

Data

Site Proximity

Facility Factors- Capacity - Moderness- Layout - Signage- APC’s

Historical Purchases

Demographic Profile

Local Competitive Intensity

Local Commercial Makeup

Other Locational Factors

Operational Factors- Pricing - Staffing- Hours - Service- Upkeep - Promotions- Merchandising

Other Customer Factors

Forecast new site sales potential

Identify site rehab / upgrade / purchase candidates

Demand forecasting & inventorymanagement

Implement dynamic pricing

Identify category mix

Geocode

Target campaigns via profiles, purchase history, & proximity

Site Traffic


Value of Predictive Modeling

• Maximize the value of the corporate data infrastructure

• Maximize the value of your business experience

=>Better Decisions

• Predictive data mining provides the means to recognize important patterns and trends that exist across many variables

• Using predictive data mining to model business dynamics, many variables can be effectively assimilated to produce a forecast


Predictive Suite OverviewComprehensive workbench for analysis and model building

Data & Model Analysis:• Graphical, Crosstab, and

Statistical Analysis• Data Sampling• SQL Querying• Automated Variable Selection• ROC/Lift Analysis• Response Surface• What-if Analysis

Model Types:• Multiple Regression• Neural Networks• Self-Organizing Map• Dynamic Clustering• Decision Tree• Fuzzy Logic Rules


Predictive Engines – Software Components

Non-UI API Components

User Interface Components

Model Engines Clustering Neural NetworksFuzzy Logic RegressionDecision

Tree

OptimizationGenetic

Algorithms& Annealing

Model UI CMDynamix NNDynamixFPDynamix RGDynamixDTDynamix

Data Analysis InteractiveGraph

InteractiveCrosstab

InteractiveStatistics

Database DatabaseSQL

DatabaseBrowser

DatabaseSampler

Model AnalysisModel

Response Surface

Model Variable

Sensitivities

Interactive Model

Execution

Model Lift/ROC

Vertical

Applications


RGDynamix – Multiple Regression• Regression models use a linear

equation to approximate relationships

• General least squares algorithm minimizes error across the training dataset

• Single weight per input factor results in the simplest model type

• Best fit application types:– Substantial set of example data– Useful to understand model

equation/weights– Highest performance training and

prediction– Simple representation is sufficient

to model application dynamics

Y = Σ Ii * Wi + Constant


Regression Model

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NNDynamix – Neural Networks• Multi-layer perceptron models are

highly accurate for forecasting & classification

• Back propagation learning algorithm can discover complex, non-linear relationships from data

• Rigorous training methodology for building optimal models

• Best fit application types:– Substantial set of example data– Limited knowledge of first principles– Highest accuracy requirements – High performance requirements


Neural Network Model

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CMDynamx – Cluster Models• Cluster-based models for

forecasting, categorization, & exploratory data analysis

• Discovers complex, non-linear, relationships from data by grouping similar cases together into clusters

• Flexibility for forecasting, outlier detection, pattern completion, error estimation, & other apps

• Best fit application types:– Substantial set of example data– Benefit from qualitative

information about model forecasts– Model needs to be extensible to

different outputs


Cluster Model

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DTDynamix – Decision Trees• Decision tree models use IF-THEN

rules to generate classifications and predictions

• CHAID algorithm derives rules from data via recursive partitioning

• Variable selection is performed as the model is learning

• Best fit application types:– Substantial set of example data– Model explainability is more

important than statistical accuracy– Data is categorical


Decision Tree Model

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FPDynamix – Fuzzy Logic Rules• Fuzzy logic rules for describing

first principles relationships • Models can be discovered from

data or be specified by the model analyst

• Model weights can be constrained, preserving model dynamics while adapting to data

• Best fit application types:– Application dynamics are

generally understood– Limited example data– Need to audit decision logic– Model needs to adapt predictably

to new data

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Fuzzy Logic Model

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Genetic Algorithms• Stochastic optimization algorithm

based on Darwinian concepts of natural selection and survival of the fittest

• Population of solutions is generated and evaluated. Features of better solutions have a higher probability of surviving into future generations

• Best fit applications:– Many solution parameters to

search– Highly non-linear dynamics– No direct gradient information

available– No target information


Predictive Suite – Data AnalysisGraphical Analysis Crosstab Analysis

Statistical Analysis


Predictive Suite – Model AnalysisROC/Lift Analysis What-if Analysis

Model Response Surface Model Sensitivities


In Summary…

Predictive Engines

Components

Tailored Solutions

State-of-the-ArtPredictiveAlgorithms

Data MiningApplications

ExtensiveIndustry

Experience

predictive dynamix software

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