Extract from various presentations: Owens, Telecom Lab, Aster Data … January 2013

Churn in Comm. Industry: a bottom line issue n Attracting thousands of new subscribers is worthless if an equal number are leaving n Minimizing customer churn provides a number of benefits, such as: –Minor investment in acquiring a new customer –Higher efficiency in network usage –Increase of added-value sales to long term customers –Decrease of expenditure on help desk –Decrease of exposure to frauds and bad debts –Higher confidence of investors

How can I effectively manage customer churn? Why are my customers churning? How do I identify key churn drivers across the customer lifecycle? How can I predict when my customers will churn? What kind of initiatives can I run to anticipate customer churn and address drivers of churn? How do I report on churn and retention initiatives? Churn: Why Customers Leave

Churn management: scoping the problem (1) n Churn can be defined and measured in different ways –“Absolute” Churn. number of subscribers disconnected, as a percentage of the subscriber base over a given period –“Line” or “Service” Churn. number of lines or services disconnected, as a percentage of the total amount of lines or services subscribed by the customers –“Primary Churn”. number of defections –“Secondary Churn”. drop in traffic volume, with respect to different typology of calls

Churn management: scoping the problem (2) n Measuring churn is getting more and more difficult –Growing tendency for Business users to split their business between several competing fixed network operators –Carrier selection enables Residential customers to make different kind of calls with different operators –Carrier pre-selection and Unbundling of the Local Loop makes it very difficult to profile customers according to their “telecommunication needs” n Other frequent questions for Fixed Network Services –What if a customer changes his type of subscription, but remains in the same telco? What if the name of a subscriber changes? What if he relocates?

The case: Churn Analysis for wireless services n The framework –A major network operator willing to establish a more effective process for implementing and measuring the performance of loyalty schemes n Objectives of the “churn management” project –Building a new corporate Customer Data Warehouse aimed to support Marketing and Customer Care areas in their initiatives –Developing a Churn Analysis system based upon data mining technology to analyze the customer database and predict churn

Business understanding n Sponsors –Marketing dept., IT applications, IT operations n Analysis target –Residential Customers, subscriptions n Churn measurement –Absolute, primary churn n Goal: –Predict churn/no churn situation of any particular customer given 5 months of historical data

Solution scope millions of residential customers millions of customers millions of business customers Usage patterns analysis of Voice Services by single subscriber line Usage patterns analysis of Voice Services by subscriber line, contract, company, etc. Usage patterns analysis of VAS by single subscriber line

Application framework Contracts Tariff plans Billing data Accounts data Fraud / Bad debts data Customer data Market data Sales data Customer service contacts Front-office Systems Marketing automation Service automation Sales automation Marketing Listener Loader... ETL Data Collection & Transformation Data Preprocessing Data Server Data Warehouse Analytical Applications Reporting OLAP Data Mining Decision Engine Back-office Systems Campaign Targets New product / services Loyalty schemes Performance analysis

Data understanding Customer Data Warehouse Input Data Customer demographics Basic customer information Service Profile Products/services purchased by each customer. Tariff plans Details of the tariff scheme in use Extra service information  Special plans / rates  Service bundles Call data aggregated by month Billing data aggregated by month Complaint information Fraud and bad debts data Customer service contacts Sales force contacts Market data xx operational systems More than 500 indicators per customer Loading: on a monthly basis Size: xTB

Modeling with Data Mining tool Main steps – Define Concepts, Attributes, Relationships … – Select Operators – Build the execution workflow

Concepts, Attributes, Relationships Demographic attributes Call data records Data about subscribed services Revenue data

Construction stage output 16 Raw attributes 45 Derived attributes Data ConstructionFeature Selection

Churn modeling chain Medium value customers are selected training set decision tree operator applied to fit predict the likelihood of a customer to become a churner in the month M6 Save output 4 Predictive models, one for each customer segment

Predictive performance

What Is Graph Analysis? Aster: MapReduce implementations for graph analysis Operates on Any Transaction or Interaction Data Identifies the individuals or nodes in a network Identifies the relationships or edges in a network In-Memory Graph Structure Allows for Graph Analytics MapReduce creates a graph object that can then be traversed for analysis Traversal of the graph is non-trivial even for simple graph analysis Output of Graph Analysis Is Flexible MapReduce used to dynamically bind structure to data on execution

Teradata Aster Graph Analysis Why this belongs on Aster Limitations in SQL Relational DBMSs -Set-based SQL is a poor programming construct for Graph problems -Every connection between 2 people is a self-join in SQL Aster Advantages -Read & transform data into in-memory graph structure -Perform standard SQL logic or MapReduce on the in-memory graph structure -Influencer Analytics: traverse the graph for single shortest path – six degrees of Kevin Bacon Aster Metrics: Social Graph Analysis Environment: 1 billion rows, 700GB, 11 workers Aster Response: 180 Seconds

Graph Analysis & Churn Prediction Graph Analysis constructs an influence propagation model - Given persons who churned (initial churners) - Diffuse their influence into their social environment - Thus, their friends are at a larger churn risk.. (The two C1s churn; N does not) - And this propagates to some of their friends’ friends (C2 affected due to indirect, cumulative influence) Output - List of predicted churners Business Value - Captures higher order social effects - Capture the effect of multiple churners on a subscriber - Does not require profile information - Once the model is created, it can be run quickly & often - Complements traditional churn models I C1 * N * * I C2 I Initial churners (known) C Predicted churners Influence spreading Indirect influence

Identify most frequent paths to early termination of service Analyze specific patterns of customer behavior Across multiple channels of customer engagement – web, retail, customer service Multi-Channel Path Analysis Understand customers paths to service cancellation

*nPath analysis Propensity-to-churn model call drop outs data drop outs in web (PDP) sessions level of call quality (voice and data speed) 3G to 2G drop down and length of time on 2G. sentiment analysis from call center records Customer experience score Propensity to churn Path to churn Good Score = Upsell opportunity Bad Score = Retention Activities Variation Score = Explanatory Message/Action *nPath - pre-packaged SQL-MapReduce function for finding sequences of events Customer Journey across Multiple Channels

Analysis across Diverse Sources & Data Types

Discover Specific Service Cancellation Paths

Big Data & Churn Prevention Enrich Traditional Churn Model Graph Analysis Multi-Channel Path Analysis Business impact With significantly less effort, know when customers are in the last mile of considering leaving Higher customer retention leading to lower costs and higher profitability Higher customer satisfaction Detect & Prevent Customer Churn