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Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 1
CRM: The Next GenerationEnabling True Multi-Channel aCRM
Olivier JouveVice President, Product MarketingData &Text MiningSPSS Inc
Text Mining Summit7-8 June, Boston
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 2
SPSSSPSS
Software companyNASDAQ-listed35+ year heritage in analytic technologiesTop 25 software companyOperations in over 60 countries
LeadershipMarket leader in predictive analyticsRecognized as leader by Forbes, BusinessWeek, IntelligentEnterprise, InfoWorld, CRM Magazine, and others
Proven track recordOver 120,000 customersMore than 95% of Fortune 1000 are SPSS customers
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 3
Enabling The Predictive EnterpriseEnabling The Predictive Enterprise
“SPSS enables your organization to become a Predictive Enterprise by directing, optimizing and automating specific decision processes.
Our software examines data on past circumstances, present events, and projected future actions using advanced analytic techniques to address specific business issues. Our software then delivers the recommendations to the people and systems that can take effective action.”
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 4
Behavioral data- Orders- Transactions- Payment history- Usage history
Descriptive data- Attributes- Characteristics- Self-declared info- (Geo)demographics
Data at the heart of theData at the heart of thePredictive EnterprisePredictive Enterprise
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 5
Trends: New DataTrends: New Data80% of Data is Unstructured80% of Data is Unstructured
…well organizations do care about some of this data.
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 6
ReferencesReferences
More than 1000 companies use SPSS Text Mining softwares, including most of the top 500 Fortune Companies:
Telco/ISPPharma/Life SciencesMediaFinance, Bank, InsurancePublic SectorRetailManufacturing…
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 7
Behavioral data- Orders- Transactions- Payment history- Usage history
Descriptive data- Attributes- Characteristics- Self-declared info- (Geo)demographics
Attitudinal data- Opinions- Preferences- Needs- Desires
Interaction data- Offers- Results- Context- Click streams- Notes
Data at the heart of theData at the heart of thePredictive EnterprisePredictive Enterprise
10-40%improvement
10-50%improvement
10-30%improvement
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 8
Business goals Business goals -- CRMCRMUnderstand customer preferences in detail by analyzing notes fields in call center applications
Improve their ability to predict which customers are likely to defect or churn, and take appropriate action to prevent it
Predict the offers customers are most likely to accept, increasing up-selling and cross-selling results whether in person, in the call center, or online
Identify customer issues and measure the preferences that are expressed in open-ended survey responses
Deepen their understanding of competitors and of market conditions by scanning news feeds, online databases such as patent applications, as well as competitor and industry Web sites
Accelerate R&D and shorten time-to-market by prioritizing efforts based on insight gained from analyzing journal articles and research reports
Predict when product components may fail or production equipment need maintenance, and better control both product quality and operating costs
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 9
Business goals Business goals –– Public Sector, Public Sector, R&DR&D……
Better serve constituents by developing a deeper understanding of their needs, attitudes, and preferences
Predict what types of fraud, waste, and abuse are likely to occur, and where, by analyzing textual information such as notes fields and e-mails
Protect public safety and security more effectively by using predictive text analysis text to improve models of potential threats by individuals and groups
….
Enable research teams to stay current in their specialized fields efficiently and cost effectively
Maximize research resources by predicting which efforts are most likely to be productive
Monitor research trends, including the actions and relationships of colleagues in organizations doing similar research
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 10
Enabling MultiEnabling Multi--Channel aCRMChannel aCRM
Multi-Channel aCRMAll about analyzing customer data across several interaction channels
But most interaction channels have little structured, behavioral data
Call centerRetailEmail + Blogs + chat + discussion forums
Predictive Text Analytics transforms and integrates unstructured data, enabling traditional analytics
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 11
ItIt’’s an Unstructured Worlds an Unstructured World80% of all business data is unstructured text
Challenge: transforming this data into tangible business value
Call Center note fields“Dog ate his product manuals. Wants to know nearest srvc center and would like to talk to a mngr asap.”“Husband is mad that he cannot watch one show while rec’ing another.”“Mutual fund acct has shown mad appreciation over past 3 months. Trying to transfr other accts in mutual funds.”
Contact management software“Finally reached p-roll supervisor after leaving half a dozen messages. She promised issue will be resolvd by COB next Friday.”
Increased use of email surveys and web formsEnhanced customer contactAllows customers to provide feedback in their own words
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 12
Search by Query
Concept-Based
NLP-Based
Keyword-Based
Search by Navigation
Ad-Hoc Hyperlinks
Taxonomies
Ontologies
Visual Maps
Others
Parametric
Alerting “Agents”
Linear Browsing
Skills
Text Mining &Visualization
What’s Related?
Personalization
Summarization
Categorization
Clustering
Information Extraction
Trending & More
People
Documents
Copyright © 2001
Gartner view of Unstructured Gartner view of Unstructured Data ManagementData Management
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 13
Text Mining 101: Text Mining 101: Linguistic Concept ExtractionLinguistic Concept Extraction
Bag of « Words »extraction
Expressionsextraction
Named Entitiesextraction
Events/SentimentExtraction
Combinedwith structured data
70’s 80’s 90’s Now
cstmrcustomer
Yellowinc
happynot
SwitchCell
phone
cstmrcustomerYellow inc
switchCell phoneNot happy
customer -> CRM termCstmr?
Yellow inc -> Telco Company (not the color)Cell Phone -> Telco term
Not Happyswitch
Customer (cstmr) -> cell phone -> unhappy (Negative)Switch to (Negative Predicate) -> yellow inc (Competition)
Decision makingChurner
-> special offer
Cstmr not happy with his cell phone – customer wants to switch to Yellow inc
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 14
Predictive Text Analytics Predictive Text Analytics Customer AdoptionCustomer Adoption
Customer Example: Major Mobile Telecommunications Provider
Business painReduce the number of profitable customers who defect to competitors
SPSS solutionAdded text mining to data mining to increase the lift of predictive model
Business benefitsIncreased the number of potential churners with a 40% improvement in lift
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 15
Typical Client ScenarioTypical Client Scenario
Volumes of structured, well-organized behavioral and transactional data
Volumes of unorganized, unstructured data
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 16
Core ProcessCore Process
Free form notes entries
Linguistic Text Mining:1. Language analysis2. Concept identification3. Process types,
frequencies, & patterns
Integrated structured and unstructured data ready for Predictive Text Analytics
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 17
Importance of Importance of linguisticslinguistics ……No dictionaries
Fuzzy grouping, permuted formsNo dictionaries
No advanced grouping options
custom-er customerCustomers customer
cstmr customercusatomer customercustoemer customercustoemr customercustom-er customer
cust customer
All options : dictionaries (CRM, IT, Custom dictionary)
Fuzzy grouping, permuted forms
All options : dictionaries (CRM, IT,)
Fuzzy grouping, permuted forms
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 18
Data quality: Linguistic tuningData quality: Linguistic tuning
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 19
Data quality: Advanced linguistic Data quality: Advanced linguistic settingssettings
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 20
Data Quality: Advanced Concept Data Quality: Advanced Concept SelectionSelection
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 21
Link AnalysisLink Analysis
Positive/Negative Extraction(built-in)
Categories Extraction(user types)
Patterns and dictionaries
Pattern example
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 22
ClassificationClassification
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 23
Classification and CategorizationClassification and Categorization
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 24
Concept Concept DiscoveryDiscovery
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CHURNTechnical Support
New Phone ASAP
New Phone
Nearest Store Location
Minute Charges Manager ASAPHelp Learning
Handset
Customer Care
Change Rate
Integrated Data VisualizationIntegrated Data VisualizationThicker Lines = Stronger Associations
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 26
Integrated Data PredictionIntegrated Data Prediction
1
1. Text mining integrated directly into analytic process
3. Data is run through the models
3
4
4. Model performance is compared
2. Models are constructed both with and without concepts
2
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 27
Measurable, BottomMeasurable, Bottom--Line Line ImprovementImprovement
1
1. Lift associated with traditional churn model2. 10 to 50% incremental lift attributable inclusion of concepts from text mining
2
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 28
DeploymentDeployment
5
5. Automatic deployment for production
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 29
Julie
Chesson
Predictive Call Center Integration Predictive Call Center Integration ExampleExample
Does not have enough minutes so is getting charged penalties. Also, phone is outdated. Would like a new phone asap.
New plan request
1. CSR enters live customer comments
1
minute_charges, new_phone
2. When saved, comments are analyzed using Predictive Text Analytics
2
RET -600 Midwest minutes - $19/mo Voluntary Churn 0.965 HIGH 3 Matching Offers
3. Customer churn score is generated and displayed in real-time
3As a valued customer, we would like to offer you a special promotion of 600 Midwest minutes for only $19/mo. Can I switch you to your personal plan now?
4. CSR script responds dynamically4
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 30
Predictive Text Analytics: SummaryPredictive Text Analytics: SummarySPSS Predictive Text Analytics offering
Accurate on “short-string” CRM dataCustomizable to extract common CRM patterns such as likes and dislikes—without requiring a linguistDiscovery-oriented
As opposed to search-orientation typical of knowledge management applications
Scales to CRM volumes
Predictive Text Analytics enables true multi-channel aCRMTransforms and integrates unstructured data, enabling traditional analyticsCompliant with data mining methodology including CRISP-DM
Provides measurable, bottom-line returns in real-world customer environments
Copyright 2005 SPSS Inc. Copyright 2005 SPSS Inc. 31
Case Study: Predictive Analytics for the Enterprise: how to achieve outstanding ROI