Transcript SPSS presentation

The SPSS Portfolio
Tim Daciuk
Services Manager, Canada
SPSS Inc.
April 12, 2016
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SPSS At A Glance
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Leadership
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Stability
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30+ year heritage in analytic technologies
Proven track record
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Market leader in Predictive Analytics
Focus on online & offline customer data acquisition and analysis
250,000+ customers worldwide
NASDAQ: SPSS
Analytics standard
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80% of Fortune 500 are SPSS customers
80% plus market share in Survey & Market Research sector
Ranked #1 Data Mining solution by KD Nuggets
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Public Sector Customers
Centers for Disease Control
Internal Revenue Service
DHHS Office of Inspector General
TX Comptroller of Public Accounts
NY Department of Public Health
UK Gov’t Communications Bureau
Canada Revenue Agency
Department of Justice
Centers for Medicare & Medicaid
USAF Leaders Project
Florida Department of Revenue
US Department of State
HM Customs & Excise
US Army Recruiting Command
New York City Human Resources
US Joint Forces Command
US Dept. of Agriculture
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Predictive Analytics: Defined
Predictive analysis helps connect data to effective
action by drawing reliable conclusions about
current conditions and future events.
— Gareth Herschel, research director, Gartner group
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Core Technologies
1.
2.
3.
4.
5.
6.
Statistical Analysis
Data Mining
Text Mining
Web Analytics
Data Collection
Deployment
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Statistical Analysis
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Descriptive Analysis
Analytic software:
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Data displays
(e.g., frequency
distributions)
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Graphic displays of data
(e.g. histogram)
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Measures of central
tendency (e.g., mean,
median)
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Estimates of variance
(e.g., standard deviation)
Satisfaction with service 1-10
80
60
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Frequency
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20
Std. Dev = 1.65
Mean = 8.3
N = 248.00
0
3.0
SPSS product: SPSS Base
4.0
5.0
6.0
7.0
8.0
9.0
10.0
Satisfaction with service 1-10
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Inferential Analysis
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Predicting numerical or
categorical outcomes
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Linear regression
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GLM Multivariate/Repeated
Measures
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Non-linear regression
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Weighted least squares
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Two Stage Least Squares
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Survival Analysis/Cox regression
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Structural Equation Modeling
SPSS products: SPSS Base,
Regression Models, Advanced
Models & AMOS
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Reporting
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Graphical software
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Visually communicate your
results
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Create more visually
compelling information
SPSS products: SPSS Base
(and Trinity)
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Powerful Data Management
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Control Panel for
OMS
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Allows users to turn
output into:
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XML
 HTML
 Text
 SPSS data file
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Data Mining
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Where does Data Mining fit?
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Three classes
of data mining
algorithms
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Prediction
Association
Clustering
Cluster
Group cases that
exhibit similar
characteristics.
What events occur
together? Given a
series of actions; what
action is likely to occur
next?
Data
Mining
Predict
Associate
Predict who is likely to
exhibit specific
behavior in the future.
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Profile and Predict: Supervised Learning
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Build a predictive profile of
the historical outcome
using a collection of
potential input fields.
Credit ranking (1=default)
Cat.
%
Bad 52.01
Good 47.99
Total (100.00)
n
168
155
323
Paid Weekly/Monthly
P-value=0.0000, Chi-square=179.6665, df=1
Weekly pay
Monthly salary
Cat.
%
n
Bad 86.67 143
Good 13.33 22
Total (51.08) 165
Cat.
%
n
Bad 15.82 25
Good 84.18 133
Total (48.92) 158
Age Categorical
P-value=0.0000, Chi-square=30.1113, df=1
Age Categorical
P-value=0.0000, Chi-square=58.7255, df=1
Young (< 25);Middle (25-35)
Cat.
%
n
Bad 90.51 143
Good 9.49
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Total (48.92) 158
Old ( > 35)
Cat.
%
Bad
0.00
Good 100.00
Total (2.17)
n
0
7
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Young (< 25)
Middle (25-35);Old ( > 35)
Cat.
%
n
Bad 48.98 24
Good 51.02 25
Total (15.17) 49
Cat.
%
n
Bad
0.92
1
Good 99.08 108
Total (33.75) 109
Social Class
P-value=0.0016, Chi-square=12.0388, df=1
Management;Clerical
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Explores all combinations,
interactions and
contingencies.
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Use this profile to
understand and predict
future cases.
Cat.
%
Bad
0.00
Good 100.00
Total (2.48)
n
0
8
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Professional
Cat.
%
n
Bad 58.54 24
Good 41.46 17
Total (12.69) 41
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Cluster and Associate: Unsupervised
Learning
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Find emerging
patterns and unusual
cases.
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Use data mining to examine
the differences and shifts
across all dimensions of the
data.
Select large groups to identify
common patterns. Select
small groups to identify
unusual patterns.
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The Product: Clementine
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Read your data in…
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Define your Target and Predictors
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Build a Rule-based Predictive Model
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Text Mining
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From Concepts to Predictive Analytics
Components
LexiQuest
Mine
Discover
concepts,
relationships
and trends
LexiQuest
Categorize
Linguistic
Terminology
Extractor
Understand
documents
and assign in
pre-defined
categories
Text Mining for
Clementine
Add text fields to
data mining for
better prediction
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Underlying Technology is Linguistic
based
Text is:
Unstructured
Ambiguous
Language dependent
Linguistic Approach
Does not treat a document as a bag of words
Removes ambiguity by extracting structured
concepts
Concepts are the DNA of text
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Core Technology…
Linguistics Based Search Technology
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SPSS LexiMine
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Concept Extraction and Query Building
Classification Document Management
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SPSS Categorize
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Supervised Learning in Existing Systems
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Text Mining for Clementine
Text Mining for Clementine consists of three
nodes:
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Text Mining Source - uses LexiQuest Mine to automatically extract
concepts, categories and frequencies from a set of documents
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Text Mining Process - uses LexiQuest Mine to automatically extract
concepts, categories and frequencies from text data stored in a
database, and links these results to structured data
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Document Viewer - displays the document or documents selected from
the Text Mining Source node
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Web Analytics
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Web Measurement Continuum
Insight
Value
ROI
•# Users
•# Visits
•# Page Views
•Top Pages
•Top Referrers
•# Errors
•Recency
•Frequency
•Average Visit Streak
•Campaign Sales
•Eventstream
•Sectionstream
•Predict Likelihood to Respond
•Automatic User Segments
•Content Clustering
•Significant Activity Sequences
•Content & Activity Associations
•Textbook Visits
•Homepage Bouncing
PREDICTIVE
WEB
ANALYTICS
WEB
STATS
Activity Counts
WEB
ANALYTICS
WEB
ANALYTICS
WEB
STATS
WEB
STATS
Business Insight
Customer Intimacy
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Web Mining for Clementine (WM4C)
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Takes web data preparation directly into
Clementine – removes the need for NetGenesis
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Turns huge volumes of web logs into business
events data
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Allows for very fast deployment of data mining on
top of web data
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Data Collection
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Dimensions Capabilities
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Dimensions Objectives
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Software and Development Platform, not
just a set of applications
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Design Once use Many.
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Powerful: handles the most complex
survey designs and multimodal
deployment.
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Centralized: defines and translates
metadata that drives all downstream
processes
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Dimensions Solutions
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Survey Design
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Interview Builder – web based (included w/ mrInterview)
Data Collection Methods
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mrInterview data collection engine
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web-based and/or Call-Center functions available
mrPaper – create paper surveys within MS Word
mrPaper/mrScan – scan solution with Readsoft EHF
CATI – call center solution software
mrDialer – call center automated/predictive dialers
Palm PDA –data collection with Techneos EntryWare
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Dimensions Solutions
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Analysis/ Reporting/ Publishing
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mrTables – web based reporting and publishing
mrStudio – desktop and script based - automate processes
Dimensions Component Pack – server processing
Interview Reporter for real-time reporting of web data
mrTranslate : Managing Multiple Languages
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Easy to use tool that does not require research knowledge to use
Writes directly to the questionnaire metadata
Supports all single and double byte character sets
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European (Spanish, French, etc.)
 Double Byte (Japanese, Mandarin, Korean, etc.)
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Deploying SPSS Models
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Deployment Solutions
Data Collection
Reporting/Analysis
Deployment
Existing Data
SPSS Web Deployment
Framework
Survey Data
Web Behavior
Text Extraction
Text Mining for
Clementine
Web Mining for
Clementine
Web Based
Applications
Predictive Marketing
Predictive Call Center
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Summary
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Predictive analytics
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Wide range of products
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Collect data
Analyze data
Mine data
Score and handle data
Wide range of applications
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Predict
Group
Associate
Anomalies
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Questions
?
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Contacts
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Tim Daciuk
Services Manager, Canada
416-410-7921
[email protected]
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Angie Mohr
 SPSS Sales, Canada
 613-599-3377
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[email protected]
www.spss.com
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