Mining A Stream of Transactions for Customer Patterns

Author: Diane Lambert

Advisor: Dr. Hsu

Graduate: Yan-cheng Lin

Abstract

Statistically principled approach designing short, accurate summaries or signatures of high dimensional customer behavior

customer behavior can be kept current with a stream of transaction

Outline

Motivation Objective Signature Design Initializing a signature Updating a Signature Experimental Results Conclusions Opinion

Motivation

Customer behavior is complex and not be evident in raw transaction records

More sophisticated analysis is necessary

Objective

Process a stream of transaction to build multidimensional summaries of customer behavior

Use customer profiling or providing approximate answers to queries current customer behavior

Signature Design

Signature is the approximation to P(X)

X = (X1, X2, …, Xk), where X1 might be call duration, X2 call timing,…

A single approximation to P(X) is describe a customer behavior

A Full P(X) = P(X1)P(X2|X1)…P(XM|X1,X2,…,XM-1)

Example of call records

Length(sec.) Call timing Since last call(min.) Incoming/Outgoing

145 2/1 12:35 245 Outgoing

210 2/1 13:35 60 Outgoing

82 2/2 10:30 1320 Outgoing X1 X2 X3 X4

P(length,CT,SLC)

Call records of one customer

Choosing Signature Components

Signature component can be thought of as a feature vector for customer

Use standard x2 test of independence Example: 50% of p-values below 0.05 and

10% of below 0.01

Initializing Signature

Transaction-by-transaction updating requires a starting point

Resemble customer segmentation, the customer may belong to many segments

A set of index variables Z that are computed from customer I’s first few or several transaction records

A vector of probabilities pi = (pi,1,…,pi,k) for customer I and initial pi depend on Z

Example

C1 P(X1) = P(length,L,I) C2C3 P(X2) = P(R, O)C4C5 P(X3) = P(length, R)C6C7 P(X4) = P(length, O)::

Updating Signature

Signature can be updated whenever customer make transactions

Pi,n+1 = (1-w)Pi,n + wZi,n+1 , where 0<w<1

Call n+1 is represented by a vector Zi,n+1

With larger w, calling pattern adapts more quickly

Experimental Results

Based on 18.23 million wireless call records, 95,893 customers who make least 30 calls

Signature use 3% of the space required by the raw Seven variables: Direction, Service Provider, Status,

Features, Day-of-Week, Hour-of-Day, Duration of completed calls

Queries about Calls and Callers

Queries about Calls and Callers

Simulated Mining

Call queries– Random quantile Q of ca

ll duration– Interval { 0.2 , 0.99 }

Customer Queries

Random ask for customer % in assign ranged queries

Conclusion

Signatures are appropriate for targeted analyses, such as detecting fraud or big spenders

The methods can be used with a variety of types of data and applications

Signatures for answering queries quickly and reliably, but only approximately

Mining in customer rather than as just the transaction level

Opinion

Experiment design is an importance thinking point