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Social Network Analysis for Telecoms
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Social Network Analysis for Telecoms

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A major North American telecom sought to identify factors driving customer churn. We applied social network analysis over several billion call records. We found that customers with a cancellation in ...

A major North American telecom sought to identify factors driving customer churn. We applied social network analysis over several billion call records. We found that customers with a cancellation in their frequent calling network churned at twice the monthly rate.

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  • Full Name Full Name Comment goes here.
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  • Hello Dataspora,
    Thank you for your Post, it is relay interesting , Now you can share your posts with more than 45,000 telecom pro world wide on TelecomYou! Community and jobs 'For Free' .
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  • sorry I am a complete statistics clueless, on slide 12 how is '3' the median?
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  • Just the beginning of using data to target telecom users
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  • Great presentation; I wish I could have been there live. Two follow-on questions:
    -armed with predictions on likely churn, what is the next step? How successful can telcos become to retain these customers? After all, if they can predict churn, but still not retain customers, the exercise is academic.
    -How prevalent are SNA analysis methods @ major telcos today? My impression talking to Tim Manns of Optus is that these techniques are still emergent. Your opinion?
    John Held
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  • Most telcos lose 1-2% of their customers every month.It’s 7x more expensive to acquire a customer, than to retain.
  • Birds of a feather flock together; cancellers clump together, so do active users. Like vinegar and water, we see enrichment for “like-like” edges in our network, and dilution of “dissimilar” edges (the A-C or C-A). Upshot: people cancellationQuestion: is this all an artifact of family plans – where a bunch of subscribers quits together? In part yes, but the trends hold up even when we do a temporal analysis.
  • Key take-home point here is that this analysis , looking at the May to June transition, removes
  • The stack is loosely coupled: right tool for the right job. The need for a dedicated analytics RDBMS

Social Network Analysis for Telecoms Social Network Analysis for Telecoms Presentation Transcript

  • 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