© 2018 TM Forum | 1

Kobi Shitrit:

Revenue Assurance & Fraud Department, HOT

Dr. Gadi Solotorevsky:

Revenue Guard, Amdocs, & chair of TMF’s revenue assurance team.

HOT Case Study

How to harness machine learning for improved business performance

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HOT, a subsidiary of Altice N.V, is a leading communications group in Israel, that

offers customers a great variety of communication services over its advanced

HFC cable network, including:

• Multi-channel television broadcasts for subscribers

• In-country landline communications services

• Cellular communications services

• Internet provider services (ISP services)

• The provision of international communications services

HOT’S COMPANY PROFILE…

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OUR BRANDS: 0.95 billioneuros of turnover (2016)

Nearly 1 MillionHouseholds and businesses subscribing to high

speed internet (Q1 2017)

nº 1The most powerful very high speed internet

network in the country – average of 75 Mbps

per customer (Q1 2017)

Leading

operatorIn terms of creation, with production of

dozens of original TV series every year

Key figures

Nearly 1.5

millionMobile customers (Q1 2017) out of a

total population of 10 million total

population

180

channelsAn unmatched offer and more

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A UNIQUE GROWTH STORY IN THE CABLE SECTOR AND A MAJOR US GROWTH PLATFORM

20082009

20102011

20122013

20072005

20062003

Cable

Câble

2002

(Dominican Republic)

(French Overseas Territories)

20142015

2016

Altice Media Group

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• Optimize sales and service operations:

• Cut operational costs

• Reduce the number of customer care representative agents

• Improve customer experience

• Reduce the number of repetitive customer requests (‘First Time Right’ approach)

• Reduce the average time for a call

• Automatically redirect an incoming request to the right division

• Identify internal procedures that aren’t being followed correctly

• Identify fraud, abuse, and bad intention:

• Both external (customers, suppliers), and internal (CSR, sales, technicians)…

• Identify suspicious relations between different entities (customers-CSRs- sales- technicians)

• Identity unknown risks, fraud, and abuse related to HOT’s sales & service operations

THE BUSINESS NEEDS!

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• Gain increased visibility on HOT’s internal sales and service operations.

• Proactively predict the nature of each call to the call center and the operational risks associated with the call/caller, and treat them accordingly:• Churners

• Price erosion

• ‘Callers on behalf’

• Systematic credit/compensation seekers

• Proactively identify anomalies in various sales and service KPIs to detect:• Fraud, abuse, and bad intention by internal and external parties

• Processes and policies which are not followed

HOT’S EXPECTATIONS…

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• Reactive approach

• Distributed monitoring practices / no single monitoring platform• Some of the controls utilize statistical analysis of traditional sales & service KPIs

• Other were done on HOT’s fraud management system

• Coverage – the existing controls rely on known use cases/KPIs (‘known knowns’ detection)

• Slow processes for acquiring and incorporating knowledge into systems

TRADITIONAL CONTROL METHODS…

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Machine-learning based service which consolidates various sales & service related operational KPIs in order to:

• Improve operational efficiency

• Reduce fraud, abuse and revenue losses

• Streamline customer sales and service engagement

HOT CASE STUDY

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Objectives:

• Utilize machine-learning technology to identify and predict unknown operational risks, fraud, and abuse across HOT’s sales and service operations

• Test new features and feature combinations

• Optimize current operation

• Detect unknown risks, fraud, and bad intention events/ patterns

Use-case definition/problem statement:• Discover operational risks occurring in HOT’s sales and service operations

• Identity complex fraudulent schemes perpetrated against HOT by different internal and external entities involved in the sales & services processes (sales, customer care, technicians, customers, etc.)

THE AMDOCS PROJECT…

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A TYPICAL MACHINE-LEARNING PROJECT

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SERVICE FLOW

Call Center callsCRM codes

Work orders

CustomerDetails

Activations/Cancellations

Collectionfiles

Financialinformation

Refunds&adjustments

EmployeesInformation

EquipmentDistribution

Promotions

Credit controlinformation

Installations Payments

ETL ORM Analytical platform Predictive analytics

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SIGNIFICANT COST REDUCTION & IMPROVED QUALITY

• The ability to PROACTIVELY predict the associated risks with each caller enables us to equip our CSRs with new, innovative tools to handle the calls much better (concrete handling instructions, collection & retention tips, etc.)

Overall improvement of the call center efficiency, examples:• Reduction of # incoming calls• Reduction of avg. call duration• Reduction of calls dealing with subjects

dealt in the past

• Reduction of operational costs

• Slows down the revenue erosion trend

14%

15%

10%

16%

2%

24%

20%

Internal fraud Manipulative customer

Calls on behalf Collection issues

Fraudulent callers Revoving door

Other

Risk Distribution by Category

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BUSINESS BENEFITS

Identify and recover monetary loses and leakages

Significant organizational operational efficiency improvement

Consolidate all operational KPIs to get a “birds eye view” of your sales & service operations

Improve customer journey, achieve higher customer satisfaction

Define sales & services processes and procedures

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MACHINE LEARNING FOR THE BENEFIT OF ALL

• Reveal hidden patterns and detect new unknown scenarios

• Bring value to other divisions ( on top of customer care):

– Collection

– Customer retention/Churn prevention

– Sales

• New and innovative business KPIs resulting in cost reduction, improved efficiency, and better quality

• In the digital era, making clever use of data is KEY for success!

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Following the success of the project, HOT is now evaluating:

• Adopting the new approach in production operational processes:– Generation of a real-time, on-screen pop-up notification which will

provide the CSR with the predicted risks associated with a customer/caller.

– Utilizing Amdocs’ new predictive model as a monitoring, investigation and forensic tool for fraud manager/internal auditing and the sales/customer care managers.

• Other areas for applying machine-learning technology in routine operations to improve business performance

NEXT STEPS

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Thank You