Social Network Analysis for Telecoms

The Social Effect: Predicting Telecom Customer Churn with Call Data
Michael E. Driscoll, Ph.D.
Principal, Founder
February 16, 2010

Social Network Analysis with Telecom Data
The following slides describe an initial project analyzing a N. American telecom’s call data on a dedicated analytics platform:
We describe the analysis of a slice of a telecom’s call history data from several million customers in the several major North American markets.
We demonstrate the performance gain achieved by having a dedicated analytics platform (computation of millions of relationships from tens of billions of events, spanning tens of TB of data, in less than one hour)
We show that social network influence is a powerful predictor of customer churn: subscribers who experience a Telecom cancellation in their frequent calling network are 2x more likely to cancel themselves.
We highlight one outbreak of cancellations in a metropolitan call network from May-June 2009.

Challenge: Customer Churn
Acquisition
Attrition

Key Data: Call Detail Records
A slice of several billion call detail records (CDRs) from several million subscribers drawn from three major North American markets, for May-August 2009.

Call Quality Analysis
No Relationship Between Dropped Calls and Customer Churn
No significant correlation found between:
inferred dropped calls (defined as consecutive calls to same number with < 20 s gap)
library(ggplot2)
qplot(Status, DroppedCalls,
data=CallHistory,
geom="boxplot“)
Box plot to shows log-normalized distributions of dropped call frequencies (drops per 100 calls) for 10k customers placed, faceted by active and cancelled subscribers.

What about social networks?

Social Network Analysis
Network is Generated from Call History Data
Call history logs were pulled from the Greenplum warehouse. These were parsed and outgoing numbers were associated with subscription ids. The result is a row of data for every caller-callee connection meeting a low threshold (> 1 call and > 60 s talk-time per month). The majority are between Telecom customers and other carriers (or land-lines).

Our Analytics Workflow
Three steps: 1. Pull from DB, 2. Analyze in R, 3. Visualize in R + Graphviz

Our Tool: The R Programming Language
Download R at http://www.r-project.org/

Getting Call Data Into R for Analysis
- from Files
> Calls <- read.csv(“CallHistory.csv”,header=TRUE)
from Databases
> con <- dbConnect(driver,user,password,host,dbname)
> Calls <- dbSendQuery(con, “SELECT * FROM call_history”)
from the Web
> con <- url('http://Telco.com/dump/CallHistory.csv')
> Calls <- read.csv(con, header=TRUE)
from previous R objects
> load(‘CallHistory.RData’)

Social Network Analysis
Millions of edges analyzed in minutes
Full analysis of a first-order outgoing call network for our slice (~ millions of customers, three months of call history) took less than one hour.
This could be further improved with further parallelization of R code (currently SQL queries run parallel on Greenplum, R is run on master node).

Results: People Have Small Call Networks (Three)
The median size of a caller’s network is three, while the mean size is five.

Results: Canceling Customers are 7x More Likely to be Linked
Types of Callers (Nodes)
active (A)
cancelled (C)
Types of Connections (Edges)
A-A
A-C or C-A
C-C
C-C edges are 7x more likely in call networks
than what is expected by chance

Results: A Customer With a Canceller in Their Network
Churns at Twice the Rate
Types of Connections (Edges)
May C-A
June C-C
In essence, we are asking whether being connected to another canceller has any effect on one’s rate of cancellation. It turns out that it does.
And if we only look at voluntary port-outs, we see that customers churn at 3x the rate.

From Data to Insights to Actions
If we had known two customers’ calling networks…
Could we have prevented four more from leaving?

The Emerging Analytics Stack
Actions
Apps
(Email, Ad Campaigns)
Analytics
(R, SPSS, SAS, SAP)
Insights
Big Data
(HDFS or Parallel RDBMS)
Data

References
Enhancing Customer Knowledge at Optus, Teradata Case-Study (September 2009).
IBM’s Analytics Tapped to Predict, Prevent Churn. Telephony Online (April 2009).
The Elements of Statistical Learning, Hastie, Tibshirani, Friedman. Springer. (February 2009).
Study Shows Obesity Can Be Contagious, Gina Kolata, The New York Times (July 25, 2007) [great example of homophily]
Contact
Michael E. Driscoll, Ph.D.
med@dataspora.com
Follow @datasporaon Twitter

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Social Network Analysis for Telecoms

by Dataspora on Apr 06, 2010

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Social Network Analysis for Telecoms — Presentation Transcript