© 2018 Sicap Schweiz AG1.

PREDICT AND PREVENT CUSTOMER CHURN

The Solution Guide for Artificial Intelligence Enabled Churn Prediction and Prevention

© 2018 Sicap Schweiz AG2.

INTRODUCTIONGlobal mobile operators collectively lose tens of billions of euros annually, to customer churn. The impact of churn on a mobile operator’s profitability is of consequence. Operators typically spend astronomical amounts on getting customers, even though the cost of retai-ning an existing customer could be as much as 50 times lower than acquiring a new client. Subscriber churn is a financial challenge in the telecom industry, and if ignored, churn can result in a significant loss of revenue and it becomes a major hindrance for the growth of mobile operators.

CHURN DRAINS MONEYChurn impacts the operator’s financials in several ways. Firstly, operators lose the future revenue that a churned customer can provide. Secondly, all the marketing investments and resources used to acquire the customer are lost.

PREVENT CHURN INSTEAD OF CHASING NEW CUSTOMERSProactive churn prevention has a 50 times higher Return on Investment (ROI) compared to customer acquisition. This is a vast, yet relatively unexplored and potentially profitable oppor-tunity for mobile operators. Churn prevention requires predicting and behavioral modelling based on a vast amount of data living in several locations on the network, and gets conti-nuously updated. This needs dedicated resources and investments in big data analytics and machine learning, which would be able to detect low signals and see correlations that normal rule engines or humans cannot see.

Could Artificial Intelligence and Big Data be applied in an economical way to help operators turn around their churning customers?

This guide describes a churn prediction and prevention solution which uses big data, mach-ine learning and customer engagement automation functionalities to detect churn- prone subscribers and automating targeted, personalized offers, incentives and information to them aiming at keeping the customers longer.

SAC ($)

Increases Subscriber Acquisition Cost

Revenue ($)

Loses Future Revenue

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PREDICTING CHURNERSOften customers leave operators because of personal motives or individual, human reactions. Research has shown that the reasons for churn fall into a limited number of clearly defined categories.

The reasons for churn lies in the data but it varies from service provider to another, based on how they treat their customers, for example.

To enable a real-time prediction capability, required for proactive churn prevention, computers will be taught to detect the signs for churn based on any available, opera-tor- specific historical data. The optimal classification and learning technique for each service provider must be first defined for this purpose.

After the training programme, the computer will be able to deduce the subscribers’ likelihood to churn within a certain confidence interval, based on data collected in real-time. Each subscriber is classified by an up-to-date churn score and the higher the churn score, the more prone to churn the subscriber can be. To maintain the prediction accuracy, the algorithm can be retrained.

CONVINCING CHURNERS TO STAYAfter the churn-prone subscribers are identified through an up-to-date churn score, subscribers can proactively be approached via personalised offers, incentive programmes and similar campaigns, aimed to convince these customers to stay.

This gives the ability to accurately address only the subscribers with a high churn score, with more relevant offers compared versus non-targeted customer engagement. Acting on pre-dicted subscriber behaviour allows the operators to proactively prevent churn, instead of running reactive win-back campaigns to address the already churned customers.

Excessive drop call rateRepeated outagesBad encounters with customer service Slow data speedsPoor coverageHigh pricesChanging life statusLocation of residenceAgeThe service that has become unnecessary. Etc.

The reasons for churn fall into a limited number of clearly defined categories...

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HIGH-LEVEL SOLUTION

THE END-TO-END CHURN PREVENTION PROCESSTo deploy a system for a real-time churn prediction and prevention requires a tight integrati-on of multiple components, which all can reside in a mobile operator domain, or partly provi-ded from an external domain through a cloud-based software as a service model.

The big data typically resides in several locations such as billing system or CRM. An ETL- process, extract – transform – load, is required to feed data into the Artificial Intelligence Engine in a consolidated way. The AI Engine computes and updates the churn score for all subscribers in real-time and delivers the data to Sicap’s TargetMe solution. Crucial device de-tection data is provided in to the process by a device management system. TargetMe triggers the pre-orchestrated customer engagement campaigns and notifications to systems such as CRM and BSS/OSS, whenever a subscriber with a high churn score is detected.

Big Data - including operator specific information related to subscribers, usage, service performance data

Sicap AI Engine - Artificial intelligence enabler, which can be integrated with any data source, telecom and enterprise applicati-on and database

Sicap TargetMe - Customer Insight and Engagement Automation Solution

© 2018 Sicap Schweiz AG5.

THE AI ENGINE SOLUTIONRunning a real-time Machine Learning process at the scale of a typical mobile operator re-quires a vast amount of data computing and management capacity with an ability to operate without blocking regular network operations.

The Event Management layer is responsible of acquiring data. It expo-ses a standard API to push data into the system. A custom-made Extract, Transform and Load (ETL) process are required to feed data from the data sources. Based on a data streaming architecture, this layer is capable of hand-ling hundreds of thousands of events per second.

The Processing layer implements the Machine Learning algorithms.• The Data Warehouse is a NoSQL database storing data from various sources and delivering it to the other internal components.• The data is normalized and transformed onto the Aggregate unit. The way data is aggregated is defined during each solution deployment separately.• The Train module is dedicated for training and implementing the MachineLearning processes using the data on the Data Warehouse.• Compute Scores module is responsible of calculating churn scores based on the data and the learned statistical prediction model.

Integration APIs are used for providing the computed results, such as churn scores in this case, to other applications including TargetMe, CRM, BSS, etc. The Integration module applies standard, customizable API technologies.

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The Sicap AI Engine is organized in three layers:

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THE INPUT DATAThe input data used for a Machine Learning process is critical. A general rule of thumb is that the more data there is, the better the results are. Based on several tests, Sicap has identi-fied and selected various dataset, which have been discovered useful for a powerful Machine Learning process. As an example, automatic device detection solutions such as Sicap Device Management Centre (DMC) are capable of providing plenty of useful datapoints as a conti-nuous real-time stream.

THE CUSTOMER ENGAGEMENT AUTOMATION SOLUTIONSicap TargetMe is a context-aware Customer Engagement solution enabling mobile operators to engage with the subscribers with high churn score through personalized messages such as offers, incentives, loyalty rewards, or other information messages. The messages are delivered to the churn-prone customers automatically when a churn score exceeded a threshold level.

Additionally, TargetMe performs external system notifications upon when a subscriber with a high churn score is detected. The external system calls are used to update billing system or CRM with an up to date churn score.

© 2018 Sicap Schweiz AG7.

KEEP CUSTOMERS LONGER, INCREASE PROFITABILITY!Since its launch in 2014 Sicap’s TargetMe has generated revenue growth, faster return on investment, higher customer satisfaction and saved customer care costs for several mobile operators.

CONCLUSIONSThe results gathered from the first artificial intelligence-powered churn reduction proof of concepts are convincing. Using predictive and adaptive data models, the subscribers who are likely to churn were identified with 85% precision. Several demographics were identified as having a higher-than-average churn probability, for example: youngsters, married people, subscribers with a higher call-drop rate and more customer care complaints, and those who do not subscribe to additional services.

The identification of the right set of models and parameters for prediction is dependent on the available data. There are numerous models for predicting customer churn, which vary, in terms of statistical techniques and variable selection methods. Each mobile operator is uni-que and detailed analysis is required to identify the best possible techniques for each operator.

When properly adapted with a mobile operator’s device base, consumption information, CRM and other data, proactive churn prevention has the potential to save mobile operators millions of euro annually by targeting the right customers, with the right incentives, at the right moments.

Grow RevenueIncrease customer life-time value by keeping them longer.

Increase Marketing ROI Retaining existing customers costs up to 50 times less than acquiring new.

Improve Customer Satisfaction Share relevant information and offers at the right moments.

Reduce Subscriber Acquisition CostsInvest in retaining customers, save in SAC!

© 2018 Sicap Schweiz AG8.

ABOUT SICAP

Sicap is a global telecommunication and IoT solution provider for Mobile Network Operators and Mobile Virtual Network Operators delivering an all-encompassing customer experience to make the mobile world more profitable, manageable and secure. Founded 20 years ago as a Swisscom spin-off, Sicap now delivers solutions to 80 mobile service providers in 76 countries. Its international team are located in nine locations to ensure excellent customer service worldwide. Sicap solutions include IoT service enablement, device and SIM management, customer insights and engagement and security solutions. Sicap’s Cloud-based solution combines managed service delivery with a pay-as- you-grow business model to give customers a fast service launch and cost-efficient solution ownership. For more information, please visit www.sicap.com

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