CRM: The Next Generation, Enabling True Multi-Channel aCRM

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


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


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.”


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


Trends: New DataTrends: New Data80% of Data is Unstructured80% of Data is Unstructured

…well organizations do care about some of this data.


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…


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


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


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


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


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


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


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


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


Typical Client ScenarioTypical Client Scenario

Volumes of structured, well-organized behavioral and transactional data

Volumes of unorganized, unstructured data


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


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


Data quality: Linguistic tuningData quality: Linguistic tuning


Data quality: Advanced linguistic Data quality: Advanced linguistic settingssettings


Data Quality: Advanced Concept Data Quality: Advanced Concept SelectionSelection


Link AnalysisLink Analysis

Positive/Negative Extraction(built-in)

Categories Extraction(user types)

Patterns and dictionaries

Pattern example


ClassificationClassification


Classification and CategorizationClassification and Categorization


Concept Concept DiscoveryDiscovery


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


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


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


DeploymentDeployment

5

5. Automatic deployment for production


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


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


Case Study: Predictive Analytics for the Enterprise: how to achieve outstanding ROI

CRM: The Next Generation, Enabling True Multi-Channel aCRM

Business

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