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 &
services
Enables
Analytics
engine
Subscriber data
Real-time
network data
Internet
Data 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!
Volume
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
BusinessValue
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
mode
Supervised/
unsupervised
learning
Predictive
modeling
Behavior
prediction
Service/channel
affinity
Sociographic
Physiographic
Millionsofsubscribers
Thousandsofproducts
Hundredsofcontexts
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
Gold
Ultra
New

Advocacy Phase
Delighted 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-1
How good is the Service
Experience?
Usage Phase-2
retain the right
customers
Migration Phase
Prepay 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-go
Make 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 decisions
Context
Design
Execution
Monitor
Data from
source systems
Work flow
management
Feedback
analysis and
planning
Data science
Decisionscience
Operations Analyst
DataAnalysis

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 Vasudevan
Contact: vinod.vasudevan@flytxt.com
www.flytxt.com

Deriving economic value for CSPs with Big Data [read-only]

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