deriving economic value for csps with big data [read-only]

Measurable Economic Value for CSP’s

from Big Data

Dr. Vinod Vasudevan, CEO, Flytxt

2confidential© 2013 Flytxt. All rights reserved. 11 September 2013

Agenda

What can Big Data do for Telecom?

Key aspects that maximises the benefits.

Illustrated with real-life examples.


What is Big Data?

Big Data: Creating economic-Value from

high-Volume, high-Velocity, high-Variety

information assets with high-Veracity

using new techniques of information

processing.

� Creating transparency

� Micro-segmentation

� Enabling experimentation

� Replacing/Supporting human decision making

� Innovating new business models, products &

servicesEnables

Analytics

engine

Subscriber data

Real-time

network data

InternetData Storage

Decision

models

Operational

metrics

Business

intelligence

Customer

experience

Targeted

marketing

Real time network

behaviour

Value of Big Data

Source: Analysys Mason


Economic Value of Big Data for CSP’s

References

IDC: Worldwide BigData Technology and

Services 2012-2015 Foreast

E&Y: Global revenue assurance survey

2013

Gartner- Market Trends: New Revenue

Opportunities and Profitability for

Telecom Carriers (Developed and

Developing Markets), 2015

Gartner: Market Trends: Worldwide, CSP

Mobile Marketing and Advertising, 2010

Analysis Mason webinar: Key software

approaches to make the most of analytics

in telecoms, 2012

*All figures in Billion USD, predicted for 2017

Economic Potential for CSP’s ~ 250 Billion USD p.a


Big Data in Telecom

Always had the best digital data; but not

the systems or processes to derive

benefit from the value in that data!

Vo

lum

e

Variety Velocity

BI

CCM

HLR

IN

NEED NEW THINKING, NEW PROCESSES AND NEW TECHNOLOGIES


Any Data can be relevant

Mission is to increase data usage.

Facebook site has one of the highest data consumption

Whitelist Facebook

BE PREPARED TO USE ANY AND ALL DATA

� Data usage shot through the roof.

� Substantiated the appetite for data and

therefore 4G

� However the data revenues and the business

case for 4G all but evaporated


Case Study: Precise targeting and clustering Operator anchored advertising campaign in Bangladesh to upsell high end smartphone

>300% ROI on mobile Ad campaign for handset upsell.

>90% precision in micro-segmented location based campaign.

>2% CTR on mobile campaign to drive brand’s site traffic.

• Data from device Management System

• Data from SGSN/GGSN CDRs

• IN Decrement Data

• Billing Data

• MSC CDRs

• Data from GIS

• Customer Master, DND, VIP lists etc. as usual

Location

Handset Model

Data usage

Spend

Significantly increased mobile

Ad ROI:

Creates a new revenue stream

for the CSP


Case Study: A South Asian Operator

Data / Category Volume

Subscriber Profiles 65 Million, 3.9 TB of KPIs and Insight Store

N/W Data Sources/day

4500+ Data jobs from varied data sources like - Base File, Daily

Usage, Recharge Event, IN Decrement, VLR, GPRS Usage, Incoming

MOU, ARPU,IMEI, WAP logs, Content Purchase & Browse, Device

Management Data, Retailer Info,

Total No. of Rows Processed/day 175Bn at a data integration frequency of 5 Min. to 24 Hrs.

Campaigns & Conversions 543,616 segmented offers in a year , 53 Million


Bucket 1 Bucket 2 Bucket 3

Hard, Soft, Probabilistic, Heuristic?


Not just predictive …

1. Predict Churn Propensity

Depth of Analysis

Prescriptive

Predictive

Exploratory

Descriptive

Behavioral

Bu

sin

ess

Va

lue 2. Genuine Risk of Churn or just Deal Digger

3. Cause Identification & Prioritization

4. Next Best Action to Win Back Subscriber

5. Measure the entre process & feed into

them


Case Study: Real-Time Closed Loop Contextual Recommendations

Continuous

Insight Engine

iTag

Assembly

Adapter

Online

Batch

modeSupervised/

unsupervised

learning

Predictive

modeling

Behavior

prediction

Service/channel

affinitySociographic

Physiographic

Mil

lio

ns

of

sub

scri

be

rs

Th

ou

san

ds

of

pro

du

cts

Hu

nd

red

s o

f co

nte

xts

Impact Generated

1 Bn Recommendations per annum

2% conversions

10.2M USD annual incremental revenue

Contextual Recommendation


Real time makes a huge difference


Case Study: Real-Time On-Trigger Campaigns

Subscriber Profiles – 35 Million base

KPI’s – 10 Real-Time + 160 Others

Total Rows Processed/Day -1.6 billion rows at a data

integration frequency of 2 Minutes to 24 Hrs.

Total Campaigns per day – 250+ Campaigns/day

KPIs & Insight Updates – 1 billion updates per day

System Specifications

Impact Generated

Generating almost 1.2% incremental revenue month on month

Real-time on-trigger campaign yield 40% to 300%

higher conversion rate

Prominent real time events – Current Balance,

Recharges, Data usage, Long distance usage, On-

net Usage, Roaming OG/IC.

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

1 2 3 4 5

Performance Real-time Vs. Non Real-time

Campaigns (March-July 2013)

Series1 Series2

Real-time Segmentation

Real-time Analytics

Real-time Tracking

Real-time Fulfilment

Real-time Action

Contextual Grading

Scheduled Rule

Experimentation

Business Consulting

+


Real-time Visibility and Measurements

Managed Multi-

Channel

Communication

EXECUTE

Sample Actions:1. As soon as a zero-usage subscriber

activates send Best fit offer

2. Send two wheeler visual MMS to

subscribers who are Commuters but not

long distance travelers with free helmet

offer on test drive.

3. Offer Best Fit Data Upgrade to CSP

subscribers segmented on consumption ,

pocket size & Handset type

Real time , Integrated, Closed Loop

Measurement

and Reporting

Real-time

Analytics

Real-time

Actions

Real-time

Visibility,

Fulfilment

<90d 90-180d > 180d

>=300 KES Diamond

Top 1% Platinum

Next 9% Gold

Next 40% Silver

Next 50% Ivory

New Silver

ARPU/

% of base

AON

GoldUltra

New


Advocacy PhaseDelighted customer brings in

more customers

Across Customers’ Perpetual Lifecycle

1

Value

Time

2

3

4

5

6

7

Whom to acquire

Customer Joins

Loyalty

Retention

Acquisition

Phase

Handholding

Phase

Usage Phase-1How good is the Service

Experience?

Usage Phase-2retain the right

customers

Migration PhasePrepay �� Post-pay

Post-pay �� Prepay

Neglect Phase Predict churn & retain the

right customer

Customer Churns

Baby Care

Campaigns

Retention, Multi-wave, Interactive

Campaigns

Churn Prevention Campaigns

Loyalty Enhancement Campaigns

Churn Prediction


Case Study – Micro Segmentation Campaign

� Each segment further micro-segmented based on

ARPU drop/ Recharge /Usage with priority score

� Suitable offers are pre-designed against each

micro-segment

� Effectiveness of the campaign is measured against

the conversions from the control group

� Iterative Campaigning addressing non responsive

subscribers with updated offers


Listen Carefully to what Data speaks …

An Operator in India

10M pre-pay subscribers,

>5 years with Operator

> $12 ARPU

An Operator in Europe

All contract subscribers

AON for many is 100 days

Month-on-month change in plan

charges (reduction!).

Both are sure to lose all those subscribers!

Indian psychology does not accept post pay

The other views Pre-pay as non-serious mobile service!

Make these subs

pay-as-you-goMake all of them

post pay


… and Combine it with Decision Sciences

Auto: Promote 3G pack to all mid and

high 2G data users.

Manual: Exclude 3G dongle users

Auto: Identify Clusters demonstrating

youth characteristics

Manual: Ignore cluster with heavy

international traveller

Auto: Promote recharge packs through

multiple channels

Manual: Use OBD for Indian rural

segment

Auto: Better to stop HVC Retention

campaigns as there is only .1% conversion

Manual: This conversion is still good for HVC

segment, continue with the campaign.

Insights Decision

ActionFeedback

Manual intervention for

contextual decisionsContext

De

sign

Execution

Mo

nit

or

Data from

source systems

Work flow

management

Feedback

analysis and

planning

Data science

De

cision

scien

ce

Operations Analyst

Da

ta A

na

lysi

s


Full Service: Technology, Consulting, Execution

KPIs

INSIGHTs

RECOMMENDATIONs

ACTIONs

PREDICTIVE MODELS

FILTERING

STATISTICAL

CLASSIFIERS

SOFT CLUSTERING

CORRESPONDENCE

ANALYSIS

TIME SERIES ANALYSIS

AGGREGATION

COVARIANCE

TWO-PASS ALGORITHM

K MEANS CLUSTERING

SCORING

ITERATED FILTERING

NESTED

SAMPLING

EXPECTATION

MAXIMIZATION

SOCIAL NET MODELS

PREDICTIVE MODELLING

TIME SERIES ANALYSIS

ANALOGICAL REASONING

PREDICTIVE

INFERENCING

MATRIX REASONING

GENERALIZATION

STATISTICAL SYLLOGISM

REDUCTIVE REASONING

SET COVER ABDUCTION

PROBABILISTIC ABDUCTION

ABDUCTIVE VALIDATION

ABDUCTIVE REASONING

LOGIC BASED ABDUCTION

INDUCTIVE REASONING

BAYESIAN INFERENCE

SUBJECTIVE LOGIC ABDUCTION

Increase ARPU

Reduce Churn

Improve QoS

Increase CSAT

Reduce Cost

Increase Loyalty

Improve MarginNew Revenue Stream

Faster, efficient, Lower TCO


2.8% contribution to gross revenue

Incremental revenue from 48% of subscribers

48% improvement in usage drop over control group

24% conversion for retention campaign, with significant gains

Stable base increased by 20%

8% conversion rate for trigger based pack promotions

30K+ successful monthly online payment recommendations

32 Million Shillings incremental revenue in a month

>300% ROI on mobile Ad campaign for handset upsell.

>90% precision in micro-segmented location based campaign.

>2% CTR on mobile campaign to drive brand’s site traffic.

Some more case study results


Flytxt Overview – About Us

� 200+ employees consisting of Marketing Consultants, Data

Scientists & Analysts, R&D Experts, Software Engineers

� Management team with 200+ years in Telecom

� Dutch corporation with Global Development Centre at

Trivandrum, India and offices at Delhi, Mumbai, Dhaka,

Lagos, Nairobi and Dubai

Sample text

� Our vision is to create >10% economic value for telcos

from their data using Big Data Solutions

� Flytxt solutions increase revenues, margins and

customer experience for CSPs

� Products based on patent pending DLU framework

implementing complex analytics

� Serving many small & large operators across

continents totaling 400M+ subscribers, via a mature

CTE model

� Proven: 2% to 7% economic benefit to customers

� Emerging market innovation that has high potential

and relevance to the developed markets

Vision, Mission & Impact Customers (Operators, Brands)

Company

Awards & Achievements

Sample text

IEEE Cloud

Computing

Challenge

B.I.D

International

Quality 2013


Thank You

Dr. Vinod VasudevanContact: [email protected]

www.flytxt.com

deriving economic value for csps with big data [read-only]

Business

data data usage

data jobs

data revenues

device management data

data integration frequency

economic value of big

best digital data

varied data sources