acm sf chapter presents 20091110l

7/29/2019 ACM SF Chapter Presents 20091110l

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THE DATA MINING AUTOMATION COMPANY TM

Implicit Social Networks and

their Use in Predictive ModelingKhosrow Hassibi, Ph.D.

November 10, 2009

Presented @ SFBAY ACM Chapter Meeting


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Agenda*

Evolution of Predictive Modeling Applications

Typical Attributes

Implicit Social Networks

Social Attributes (Focus on Telco)

Business Uses

Challenges Generalization

This presentation is based on the work & contribution

of many people at KXEN.


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EVOLUTION OF PREDICTIVEMODELING APPLICATIONS


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Evolution of Predictive Modeling Applications

Started to get attention in late 1980s and early 1990s

-Only pioneers

Predicting individual customer behavior B2C applications

With focus on financial applications

Score-based solutions based on

- Machine Learning (Neural Networks, )

AND- Traditional statistical modeling techniques

Risk, Fraud, Attrition (Churn), Targeted Marketing,


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Evolution of Predictive Modeling Applications 2

By late 1990s, these applications became more popular and got

more industry traction- Also in Telco, Retail, and eCommerce

Predictive Analytics market & tools significantly improved

Nature of applications

- Offline/Batch:

Examples: Targeted marketing, Churn/Attrition

- Real-time Transaction-based:

Credit card Fraud, Web


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TYPICAL ATTRIBUTES


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Typical Data Streams

Data Volume, Variety, Velocity, and Validity is

increasing

Extreme granular data available

- Many data sources

For a typical customer Static: Customer demographics, psychographics,

Geographical,

Time Series transactional data (Monetary/non-Monetary):

Financial: ATM, checking, savings, credit cards, bill

pay, email, brokerage, campaign, call center, web,

payments,

Telco mobile: Calls, SMS, MMS, data, payments,campaigns, call center, downloads, browsing,


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Example Raw Credit Card Transaction Data

NAME DESCRIPTION

TRANS_ACCOUNT Credit card account

TRANS_DATE Transaction date

TRANS_TIME Transaction time

TRANS_AMOUNT Transaction amount

TRANS_TYPE

Type of transaction - M for Merchandise and C

forCash,

TRANS_CRED_LINE Credit line at the time of transaction

TRANS_ITEM_CODE

Standard Industry Code used for item

category description by Visa and MasterCard

TRANS_ZIP5_CODE Five digit US zip code

TRANS_COUNTRY ISO Country code

TRANS_KEY_SWIPE S for Swiped or and K for Keyed,

Many more fields


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Customer Behavioral Attributes

Time series transactional data is flattened for

modeling use- Resulting in larger number of attributes to be derived/stored/analyzed

Grows exponentially considering Time series, segments, interactions,

Focused on the entity to be modeled- A customer, a household, an account, a connection,

Behavioral variables most often capture past behaviorof the entity in isolation:

- Short, medium, and longer term activity


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Customer Behavioral Attributes 2

More complex/customized approaches:

Capture the entity behavior within its peer group/segment computed based on oneor a variety of other attributes including interaction directly/indirectly with other

entities

Segmented modeling

Example:

Static attributes: Age, location, hobbies, Financial segment,

Behavioral attributes :

Number of non-cash credit card transactions in last 5 minutes

Total purchases in last two month on discretionary items

Deviation of average spend compared to average luxury traveler

Number of SMS sent/received received last 30 days

Typical customer behavior attributes range is in 100s


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Customer Model Record

The Customer Model Record, is a set of all attributes thatis used for modeling (could be database views).

ID FIELDS BEHAVIORALATTRIBUTES

STATIC ATTRIBUTES MODELSSCORES

CONTACT HISTORY

Model Description

Model ID

Model NameModel Date

Model Scores

Model ID (FK)

Individual ID (FK)

Score

Individuals in Household

Individual ID

Household ID (FK)Individual attribute fieldsPrimary Householder Flag

Campaign Type Ref

Campaign ID

DescriptionStart Date

Contact History Detail

Campaign ID (FK)Individual ID (FK)

Promotion CodeTreatment CodeContact Date

Contact History Summary

Individual ID (FK)

Number of Mail ContactsNumber of eMail Contacts

Number of Telephone Contacts

Household

Household ID

Street AddressCityStateZip

PhoneDemographics Lifestage Lifestage

Individual ID (FK)

Demographic Data Fields

Accounts

Account No

Account Information

Transactions

Transaction IDIndividual ID (FK)

Account No (FK)

Transaction Details

Transaction Timestamp

Products

Product ID

Product Details

Purchases

Transaction IDIndividual ID (FK)

Product ID (FK)

Purchase Details

CustomerModelRecord

CustomerData

Warehouse


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IMPLICIT SOCIAL NETWORKS


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Social Networks

Social network analysis (related to network theory*) has emerged

as a key technique in modern sociology.- It has received more attention since popularity of online social networks

A social network is a social structure made of individuals (ororganizations) called "nodes," which are tied (connected) by one

or more specific types of interdependency, such as friendship,kinship, financial exchange, dislike, sexual relationships, orrelationships of beliefs, knowledge or prestige.

*Network theory concerns itself with the study of graphs as a representation of either symmetric relations or,more generally, of asymmetric relations between discrete objects.

Source: Wikipedia


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Implicit Social Networks: Telco Call Detail Data

Main fields:

-A number (Origination)

- B number (Destination)

- Start Datetime

- Duration

- Call category

Voice, SMS, MMS,

Many other information:- rate information

- the result of the call (whether it was answered, busy etc)

- Revenue

- the number charged for the call

- additional digits on the B number used to correctly route or charge the call

- the route by which the call entered the exchange

- the route by which the call left the exchange

- any fault condition encountered

- any facilities used during the call, such as call waiting or call diversion,

A B


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Implicit Social Network 2

Many implicit social networks are hidden in thecall data

They can be extracted by defining the relationshipbased on:- Call categories

Voice Network (A calls B on voice)

SMS Network (A sends a SMS to B)

MMS Network (A sends a MMS to B)

All services Network (A calls or sends SMS or MMS to B)

- Intensity

At least 3 communications

Duration at least 1 minute

- Time Period

Call took place during this month, last 60days, - Direction of communication

Directed or Un-directed

- Other categories

Rate, roaming, revenue,


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Example Relationships

4 relationships defined

4 graphs (networks) can be extracted

A. Any voice call in between two nodes with direction

B. At least 3 voice calls in between the nodes with direction

C. At least 2 SMS in between nodes (direction does not matter)

D. Any MMS in between nodes with direction


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Example Extracted Graphs

A

D

B

C


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Historical Uses in Telco & Financial Industries

Mainly link analysis

- Security (Telco, Financial)

- Fraud (Telco, Financial)

Finding fraud rings

Establishing evidence

Point of compromise (counterfeit fraud)

-Money laundering (Financial)

Exploration

Investigation


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New Marketing Applications

Interactions of customers impact their decision making

Example- An influential customer leaves, purchases a product,

- After some time, some in his/her micro-community may follow

A customer behavior/decision could exploit network structure

to propagate (Diffusion)

Understanding the implicit community structure in Telco dataallows:

- To better understand customer behaviors

- To better target customers in their

micro-communities

- Identify opportunity/risk of propagation of

nodes behavior within its micro-community


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SOCIAL ATTRIBUTES


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Circle

analysis

Connection

analysis

Community

analysis

Influence

analysis

Types of Attributes

Centrality

analysis


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Example Social Attributes

Circle analysis (Level I Analysis)

-Neighbors (Links in or out)

- Off-net/on-net ratios per node

- Role (Sink, Source, Repeater)

Connection analysis (Profiling based on neighbors) Profile on continuous extra attributes

average of age of neighbors

Profile on discrete extra attributes

Example: ratio of churners in the first circle nodes

Ratio of those with iPhones in the first circle nodes

Centrality analysis- Number of triangles

- Betweenness

-


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Social Attributes 2

Community Analysis

Identify communities Roles in the community

Social, Passive, Social, Internal

Marginal, Outlier, Leader, Follower, Bridge

Influence Analysis

Identify influencers

- Analyze the propagation of a node influence


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Example of Community Assignment


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SN Attributes Computation Process

KSN


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Example List


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BUSINESS USES


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Marketing Use of Implicit Social Networks

Traditional

Targeted

Marketing

(Customer Behavior)

Viral Marketing(Diffusion by

Influence)?


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Marketing Use of Implicit Social Networks (2)

Traditional targeted

marketing

Targeted marketing +

SNA attributes Viral marketing

+ uses customer data:

demographic,

psychographic, Time

Series transactional

data (behavioral)

ignores social data

+ empirical

+ adds lift

+ uses customer data:

demographic,

psychographic, Time

Series transactional

data (behavioral)

+ uses social data

+ empirical

+ adds incremental lift

over customer data

alone

Mostly ignores

customer data

+ uses social network

data

+ Targets only

influencers,

Identifying influencers

is challenging &

domain specific

Effectiveness?


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1. Viral Marketing

The goal is to identify individuals with high Social Networking

Potential (SNP) and create Viral Messages that appeal to thissegment of the population and have a high probability of beingpassed along.

- To identify opportunity (or risk) of propagation of customers

behavior/decision within his or her community.

In its crude form

- Not empirical

- Influencer models?

Using detailed network data

- Empirical (churn Influence score, churn pressure score, )

- Experiments can be conducted

- Ignores detailed customers behavior


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2. Targeted Marketing Enhanced by SNA Data

Use traditional targeted marketing using predictive modeling

-Customer behavior data

- Demographics,

Enhance by implicit social network data


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2. Targeted Marketing Enhanced by SNA Data 2

Improved targeting

improved lift

Better understanding ofcustomer

Better messaging0

0,2

0,4

0,6

0,8

1

0% 10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Random Wizard Model

A

B

C

D


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3. Graph (Node) Pairing

Identify a node based on its

connections characteristics in thenetwork

Business use:

- Rotational churn

Wrongly inflates the marketing campaign

success (Profit)

Wrongly inflates churn rate (Loss)

- Identifying fraudsters


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Case Study 1 Predictive Modeling (KXEN)

Wireless/cable/media service provider.COMPANY

Relationship data hidden in call detail records is not leveraged.SITUATION

CHALLENGE

Augment the hundreds of typical behavioral attributes with hundreds of derivedsocial network attributes and measure the impact on lift for selected testcampaigns in acquisition and cross sell/up sell.

EXPERIMENT

Preliminary results positive. For a subset of test campaigns, lift could beimproved anywhere from 9% to 13%. Target overlap was anywhere from 65% to90%. Further experimentation is due.

RESULTS

With 7.5 million wireless subscribers and tens of millions of call detail recordsdaily, deriving and analyzing social network attributes is a monumental task.


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Case Study 2 BI Use (Other)

Wireless service provider.COMPANY

SN data hidden in call detail records is not leveraged.SITUATION

CHALLENGE

Extract communities from implicit social network in the call data and analyzeEXPERIMENT

700,000 communities derived . Average of 6 people in a community. Theyidentified key people in ethnic groups and offered a phone to text in thatlanguage. Phones were immediately sold.

RESULTS

4.2 million SIM-card customers


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Studies in Progress

1. Wireless operator (16.5 M nodes)

-Uplift on churn models

- Finding influencers

- Detect rotational churn

2. Fixed Line operator (7 M nodes)

-Uplift on churn models

- Detect communities and roles

- Detect propagation of churn

3. Wireless operator (90 M nodes)

- Scalability test (handling 30M subscribers with 90M nodes)

- Uplift on churn


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CHALLENGES


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Challenges

Call detail data is massive

Random sampling on nodes can not be used

Scalability is crucial

- Algorithms must be scalable


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Challenges 2

What is influence and ways to measure it

How much additional lift SNA can add to traditional churnmodels?

How can roles be defined or customized?

How much additional lift SNA can add to traditional marketingpropensity models?

Community detection in an arbitrary network is difficult

More studies on business value (ROI)


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GENERALIZATION


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Indirect Relations

Social networks could still be defined when there is an indirect

link between customers

Example:

- Retail: purchases: Customer Item relation

- Financial: credit /debit card data: Customer Merchant relation


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Other Applications

Financial

-Credit/debit card data: Customer Merchant buying patterns

- Small/Medium business payment data

- Online payment data such as PayPal

- Join accounts

- Anti-money laundering, investigating money transfers

-Anti-credit card fraud, using Merchant-Buyer patterns

Retail

- Improved recommendations, using Consumer-Product buying patterns

- Online Buyer-Seller communities

Social network sites

- Improved content, ads, using Friend-Friend interactions

Emails, forums, blogs

- Improving customer understanding using interactions


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Q/A


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One Prediction

Data providers and Bureaus

currently provide manyinformation on US households:

- Demographic

- Psychographic

- Interest

-Hobby

- Financial

- Risk (credit worthiness)

-

In the future, they could alsoprovide

- Social (relationship) metrics?

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