© 2012 – PROPRIETARY AND CONFIDENTIAL INFORMATION OF CVIDYA

Using Big Data and Analytics Insight to Improve Customers' Retention and Value

Gadi Dr. Gadi Solotorevsky

CTO – cVidya Networks

Ambassador, Distinguished Fellow – TM Forum

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A leading supplier of Revenue Analytics solutions to communications and digital service providers

Founded: 2001

300 employees in 15 locations worldwide

Deployed at 7 out of the 10 largest operators in the world

150 customers in 64 countries

Globally processing 150 Billion xDRs per day, 55 Trillion xDRs per year

Contributing over $12 Billion to providers annual revenue

Partnering with world leading vendors

What You Should Know ABOUT US

Africa

APAC

CALA

North America

East & Central Europe West Europe 64 Countries 64 Countries 150 Customers Worldwide

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USA AT&T, Sprint, Alaska Telecom, Sovernet, ATN, Commnet, Alltel, Islandcom, CellularOne, Choice Canada

Bell Canada

South Africa – MTN, Vodacom Uganda – Uganda Telecom Zimbabwe – TelOne

Ivory Cost – MTN Namibia – MTC, Telecom Namibia Angola – Angola Telecom

Russia – RosTelecom, Uralsvyazinform Ukraine – Kyivstar Belarus - BelTelecom Georgia – Magticom Kazakhstan – KazakhTelecom Turkey - Vodafone Greece – Cosmote, HOL (Hellas Online), Forthnet Albania – AMC (Cosmote), Plus Romania – Cosmote Serbia – Telekom Serbia, Vip mobile Slovakia – Orange Slovakia Bosnia Herzegovina – Telecom Srpske Croatia - Croatian Telecom T-HT Bulgaria – Mobiltel Poland – T-Mobile Macedonia – Vip operator Slovenia - Simobil

UK – BT, O2, Orange, T-Mobile, Colt, Three, Sky, VirginMedia, Cable & Wireless Germany – DT, Vodafone, E-Plus, 1und1 France – France Telecom, Orange Spain – Telefonica, Telefonica Moviles, JazzTel Italy - Telecom Italia , TeleTu Switzerland – Swisscom Austria – A1 (Telecom Austria Group) Sweden – Tele2, Telia, Telenor Denmark- Telia Netherlands – KPN, Telfort, Orange, Vodafone Ireland – O2 Lithuania – TEO, Omnitel Finland – DNA Portugal – PT, ZON Liechtenstein – Mobilkom Belgium - BICS

Brazil - Vivo, TeleSP, TIM, Embratel, CTBC Mexico - Movistar/Telefonica, Iusacell Argentina - Movistar/Telefonica, Telecom

Argentina Peru - Movistar/Telefonica Chile - Movistar/Telefonica

Ecuador – Movistar/Telefonica, CNT Colombia - Movistar/Telefonica Venezuela - CANTV, Digitel, Movistar/Telefonica

Guatemala - Movistar/Telefonica, Telgua El Salvador - Movistar/Telefonica, Claro Panama - Movistar/Telefonica, Cable & Wireless

Nicaragua - Movistar/Telefonica, Claro Costa Rica – ICE Caribbean’s – Cable & Wireless, Digicel

Puerto Rico – PRTC Guyana - GT&T Uruguay – Antel

India – Vodafone, Airtel Vietnam – MobiFone Philippines – Bayan, Globe

Australia – Three (H3G), Optus Thailand – CAT Singapore – MobileOne

Macau - CTM Israel – Bezeq, Bezeq International, Cellcom, Orange, Pelephone,

YES, HOT Palestine – PalTel Fiji – Digicel

Papua New Guinea – Digicel Sri Lanka - Mobitel

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What data is available to Telco’s

Usage Data - Events (CDRs, xDRs)

Location Data

DPI data

Customer information – (e.g. age, address, services, organization, id, email…)

Customer interaction information (payments history, complains history, churn)

Customer social information (who he calls, who calls him, when, duration.…)

Data from external sources – Facebook, tweeter, web...

Competitors plans

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What can be done with this data

Revenue Assurance (detect and prevent Revenue Leakages)

Design price plans & Best offers

Detect Fraud

Increase customer satisfaction and prevent churn

Cross and pre sale

Enable targeted advertising

And much more, plan traffic routes, prevent infections propagation,……

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First Example – Revenue Assurance

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TM Forum Revenue Assurance KPIs Study - 2011

98%

2%

Detected Leakage % of revenues

Revenues Leakage detected

31%

69%

Recovered %

Recovered Unrecovered43%

57%

Recoverable and Unrecoverable % of un-recovered revenues

Recoverable Unrecoverable

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IMS and LTE Network

Internet Evolved Packet Core (EPC)

e-NodeB

e-NodeB

e-NodeB

e-NodeB

Radio Access Network (RAN)

S-GW

MME

P-GW

HSS

PCEF

PCRF

SPR

ePDG

PDF CSCF

AS

MGW

PSTN/PLMN

IMS

MGCF

OFCG OCG

Wholesale I/C Billing

system CRM

Postpaid Billing system Provisioning events

Accountable usage events

ERP

IP Network

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The Challenge

Daily input of ~12 billion records in various formats

Find discrepancies between sets of ~40 billion records

No exact matching (clocks differences, partial information, rounding differences, similar but not exact values)

Need to detect also best match

Cost effective

Robust, and capable of manage significant delays in inputs

After discrepancies are detected, need to enable near-real time analytical investigation

Naïve sort and match will not work

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The Solution

Mediation

Events

Comparison Engine

KPI, KRI

OALP

Events and transactions

files Multiple formats

Fragmented info

Formatting Enrichment Correlation

Purging

Drill down/trough

Records

Discrepancies

Aggregations

Records Indexes to files

Aggregations

DB

Events

Files

Second Example – Design and offer Price Plans

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Next Best Action

Unstructured

Advisor

Next Best Offer

Impact

Propensity to Accept

ARPU Impact Influence Advanced Insights

Impact CMOs need flexible analytics solution to model multiple new plans and benchmark versus competitors’ offering

− Control customers’ migration and potential income dilution

− Consider behavioral changes due to price plan change

Advisor CMOs need “Next Best Offer” solution to provide personalized, accurate, value-driven, pricing plan recommendation per individual based on operator’s financial analysis − Based on individual historical

usage data enable the operator to better understand the “future invoice” per each price plan

Propensity to Accept

− Predicts per customer the propensity to buy each of the offers based on his usage and profile

ARPU Impact Influence

− Predict for each individual the ARPU change in case of accepting an offer to buy each of the offer

− Models are built by learning how did ARPU of similar customers have changed over time after accepting each of the offers

Advanced Insights

− CMO want to utilize clickstreams data and voice transactions to cross-sale and up-sell their own as well as 3rd parties offering (e.g. shows tickets, loyalty programs, etc.)

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The Challenge

The goal, increase customers’ satisfaction, reduce churn, and increase revenues

The method: Design new price plans, estimate their impact, and propose next best offer to customers

The volumes: 54 Billion events (30 Million subscribers, 10 events per subscriber per day, history of 6 months)

The requirements: cost effective (e.g. hardware costs), analyze multiple price plans in parallel, offer the right plan to the right customer

Using a regular billing system will not do the trick

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cVidya Solution Overview

Price Plans from billing

New Price Plans by

Marketing

Competitors Price Plans

Results database

Filter results based on post rating

business targets definitions

Priority definitions for eligible options:

best, 2nd best, …

Selection of price plans, options and

customers for rating

Filter eligible rate plans based on pre rating parameters

Rating and bill calculations

Pricing plan implementation Process Audit & control

Price plan implementation

Scenario & Rating definitions

Business rules definitions

Reports, logs and alerts

Extract eligible subscribers data

Exports to CRM & Campaigns

Rated CDRs Reports, detailed results for each

subscriber

Web interface

Customer Data

Invoices Data

Business rules simulations

Financial impact simulations

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Transform

Rating Engine

Price plans

System Storage Proposition engine

Invoice

Customer Details

CDRs

CDRs

Information Flow

Subscribers Profile

Data repository

Campaign Management

CRM

WEB Self care

Rating Engine

Rating Engine

Rating Engine

Rating Engine

Pricing Optimization

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The result Advanced Insights – Cross-sale and up-sale

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