Intent management application jan 2014

1confidentialFlytxt. All rights reserved. April 10, 2023April 10, 2023©

Personalized Intent ManagementCapture, Associate, Recommend


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 Dubai, Delhi, Mumbai, Dhaka, Lagos and Nairobi

Our vision is to create >10% measurable economic value for CSPs through Big Data Analytics

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 500M+ 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

Company Awards & Achievements

Sample text

IEEE Cloud Computing Challenge

B.I.D International Quality

Sample text

Customers (50+ customers, 32

countries)Operators & SIs Brands


Big Data Opportunity for Telco’s

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

Revenue Enhancement

Customer Retention

Intent Management

Mobile Advertising

Granular Margin Management*

Current solutions from Flytxt

The opportunity is to create an impact of well over $250 Billion p.a. by 2017

Flytxt current solutions’ scope is estimated at a third of this.

* Roadmap Application


KPIsINSIGHTs

RECOMMENDATIONsACTIONs

PREDICTIVE MODELS

FILTERING

STATISTICAL CLASSIFIERS

SOFT CLUSTERING

CORRESPONDENCE ANALYSIS

TIME SERIES ANALYSIS

AGGREGATION

COVARIANCE

TWO-PASS ALGORITHM

K MEANS CLUSTERING

SCORINGITERATED FILTERING

NESTED SAMPLING

EXPECTATION MAXIMIZATION

SOCIAL NET MODELS

PREDICTIVE MODELLING

TIME SERIES ANALYSIS

ANALOGICAL REASONING

PREDICTIVEINFERENCING

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

Ad Network

Insight Monetization

Revenue Enhancer

Intent Management

Ad Market-place

Customer Retention

Faster, More efficient, Easier

Full Service: Technology, Consulting , Execution


500+ Million

Subscribers

Flytxt in Action

440 Million prepaid and 60 million postpaid customers

750 thousand customer segments

25 Million data products sold in 2012-13

0.3 Million mobile money transactions for a leading CSP

Products sold through 8 different touch points

2 multi-national group level framework agreements

3 out of global top 15 CSPs based on revenue (Q1 2013)

70% market share in Africa

50+ CSPs across 32 countries

300+ Million

products sold in 2013

$250 Million Incremental Revenue till

now

27 Billion customer

offers made

33% usage enhancement for Kenyan CSP

Churn reduced by 25% for South Asian CSP

>300% ROI on mobile Ad campaign for handset upsell

3% average improvement in total MoU for African Tier 1 CSP

3 Billion data offers sent

3 Billion VAS offers sent

1 Billion annual personalized recommendations in one CSP


Real-time Visibility and Measurements

KPI’s, Insights, Actions,Recommendations, Correlations,

Multi-Dimensional Trending, Multi-granular segmentation, …

10s of 1000s, Micro-Segmented ,Multi wave, Recurring, Contextual,

Control group,Best fit, Next-Best, …

Managed Multi-ChannelCommunications

Flexible Workflows, Comm. Policy, Traffic management, PrioritizationMulti-channel Communication, …

On-Screen Business Dashboard, Performance,

Milestones Real-time Impact, …

ROI Reporting, Impact Measurement, Experiment Results,

Rating, Billing. …

ANALYSIS

PLANNING

EXECUTION

TRACKING

MEASURING

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

activates send Best fit offer2. 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

Platforms: Real-Time, Integrated, Closed Loop

Measurementand Reporting

Real-time Analytics

Real-time ActionsReal-time Visibility

<90d 90-180d > 180d

>=300 KES Diamond

Top 1% Platinum

Next 9% Gold

Next 40% Silver

Next 50% IvoryNew Silver

ARPU/% of base

AON

GoldUltra New

T M

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Enhances Customer Experience through personalizing services and content, with behavioural association and contextual recommendations

NEON-Big Data Analytics Powered Platform


Functions of Intent Management

Next Best Action

Service Personalization

Lost Intent Revival

Touch point personalizationContent personalizationProactive customer care

Auto recommendationExperience optimizationOffer discovery

Lost intent capture and revivalRFM based prioritizationContextual cross-sell offers


Why Service Personalization is Important?

Food

Air

Shelter

Esteem

Self actualization

Spec

trum

Network

Affordab

le

com

munica

tion

Brand

loyalty

Serv

ice

perso

naliza

tion

Innovative Operators are striving to meet Service Personalization objectives for Self Actualization


What Goes Wrong?

Untimely offer

Inconsistent customer experience

Bad experience

Blanket offers

Improper segmentation

Operator Customer

Irrelevant content

“Frustration leads to opportunity loss and customer churn”


Approach Needs to ChangeBlanket recommendations Persona driven recommendations

"If we have 4.5 million customers, we shouldn't have one store, we should have 4.5 million stores." - Jeff Bezos, CEO, Amazon.com

“Event Triggered, real-time recommendations based on customer behavior have 10-15 times the response rate than mass marketing “ – Gartner

Persona Driven Recommendations focusing on unique needs of individual customers

Personas of same person


Flytxt Approach to Intent Management1. Big Data Analytics 2. Creating subscriber personas

3. Personalized recommendations

Service Personalization

Next Best Action

Lost Intent Revival

4. Business outcomes

Personas of same person


Deriving Insights through Big Data Analytics

Data Fusion Engine

Continuous Insight Engine

iTag Assembly Adapter

Online

Batch mode

Supervised/ unsupervised

learning

Predictive modeling

Statistical classifiers

Soft clustering

Correspondence Analysis

Nested Sampling

Iterated filtering

Social net modelsM

illio

ns o

f sub

scrib

ers

Thou

sand

s of

pro

duct

sH

undr

eds

of c

onte

xts

Insights Recommendations Actions

Heavy international caller

Provide value pack for international calling

Offer international pack at 20% discount


Capturing Subscriber Personas

Persona:Young subscriberHigh volume SMS usageParty enthusiast

Persona:ProfessionalBusiness content userHigh end handset user

Persona:Young SubscriberUses Social Networks & IMSports enthusiast

Personas of individual subscribers

Different personas of same subscriber

Persona:ProfessionalBusiness content userOnline money user

Persona:Sports enthusiastLoves cyclingAttends sports events

Persona:TravelerHeavy roaming pack user


Personas

Sports fan, celebrity fan, high

tech lover, etc.

Social models

Top 10 friends, top 10 influencers, etc.

Call/data models

Heavy caller, International

caller, night caller, etc.

Location models

Home location, frequent traveler

etc.

Custom models

Any custom models on demand

Usage models

Minutes of usage, Local, international,

GPRS,etc.

Intent Management

Personalized adsBest fit offers

Personalized experience

Personalized content and navigation Futuristic offers

Dedicated customer service

Creating Personalized Recommendations


Enhance Experience through Service Personalization

Optimize Customer Experience at each touch points through Sequence Discovery (Perpetual Pull Campaign)

Segment 2

Segment 3

Segment 1

Segments created from the full base using profile,

metrics and event Information

Offer A

Offer B

Offer C

Segment 3

Each segment is mapped to one or multiple offers

Rule based Offer

Cascading

SMS

MMS

OBD

WAP

WEB

Email

USSDOffers fetched through

multiple communication channels


Recommending Services and Best-fit OffersYour Friend just tried to call you.

Answer the calls while you are busy-subscribe to our automatic reply

services

Miss call alert Use Social profile (top friends) and subscribers

persona (frequent callers)

High value subscriber, treat accordingly!

Customer service Treat subscribers more personally Better recommendations “Frequent customer care caller” “Inbound calls only”

You are a frequent SMS user. A 100 SMS pack for X would save you money!

SMS package “Best fit offers” based on usage personas

Not only “push”, but also pull, through retailers, etc...

Sales and distribution “Value conscious subscriber” In store recommendations Helping subscriber and outlets SMS service “best offer” for retailer

Another voucher will provide more value than the one you are just buying.

Would you like it?


Content Discovery on Portal

Content type

Content structure

Customized content• Content itself can be highly personalized

Customized content structure• Content structure on subscriber preference


Next Best Actions through Contextual GradingObjective was to design an advisory system which aids the marketer in recommending most graded contextual offer to subscriber based on relevance & Objective

An analytics technique that identifies packs which would be interesting to each user based on their historical usage behavior, spending patterns and current context

Capability to tune the fairness of the algorithm (Subscriber Affinity vs. Correspondence Association vs. Contra Association)

Fuzzy matching, Soft Clustering, Propensity deduction technics applied to create graded matching of subscribers into soft clusters. Graded Association of subscriber to each cluster is a powerful approach for automated decisioning (e.g. Automatic matching of relevant offers to Subscriber)

The work got published in 19th National Conference on Communication at IIT Delhi - Thomas, Shyju et al. , available for download from IEEE Xplore

Next best Offer Fairness tuning:

Subscriber Affinity

Correspondence Association

Contra Association

Revenue Objective

Margin

other


Contextual Grading Recommendation Example

Offer-1 Offer-2 Offer-3 Offer-4 Offer-50

2

4

6

8

10

12

14

16

18

20

Conv

ersio

n Ra

te in

Jul-2

012

( in

%)

AP AS BHCHN DL GJ

KAR KLKOL

MHMUM OR

PUN RJ TN WB

0

5

10

15

20

25

Rule-based campaign Best-fit recommendation (fair)

Circles

Conv

ersio

n Ra

te in

Ju

l-201

2 (in

%)

Current Personas built:CLV (HVC, MVC, LVC), Volatile,Early Adopter, Frequent Handset Changer, Heavy Data user, Social Media Fan, Bollywood Fan, Music Fan, Sports Fan, potential ipad buyer, International Caller Etc…….

Objectives: Cross sell, Upsell, Stimulate recharge/usage/Service adoptionEtc…

Offers:Data Plan, 3G plan, VAS usage, International Calling packs, Bundle offers, Recharge stimulation, Seeding, ebill subscription etc……

Channels:IVR, SMS insert, In store, Retailer, WAP portal, Customer care portal


Failed subscriptions/purchases

CRBT packISD pack GPRS packSubscriber

Dear Customer, You have been successfully credited $50. Your purchase of ISD pack has failed

before, would you like to purchase now? If yes reply

ISD100 to 56363

Best match in value/recency/number of attempts

Lost Intent Revival: Best Match Selection and Revival

Recharge transaction

Recharge success notification + intent marketing message

Recharge value: $50


Definite Business Outcomes

Correlation between high Customer Experience Index and three elements of loyalty

Source: Customer Experience Boosts Revenue- Research note by Forrester

NBA conversion is 1.6 times higher than manual

40% of subscribers decides to buy another transaction due to low

balance

1% increase in Customer Satisfaction

leads to 2.9% increase in ARPU

Intent Management Enhances Customer Loyalty

Intent Management Enhances Revenues

Service/Content Personalization Next Best Actions Lost Intent Revival

Source: Arthur D. Little Source: Flytxt Source: Flytxt


Case Study: Recommendations at an Indian Operator

Subscriber Profile –67.53 Million base

Total Rows Processed/Day -175 billion rows at a data integration frequency of 5 Minutes to 24 Hrs.

Max. TPS Achieved – 1533 messages/sec

Total 7 Touch Points Integrated – IVR, webPayment, Retailer, USSD, SOMA (prepaid system), ASCC, MBR,

System Specifications

Impact Generated

1185 MN Recommendations annually

8 touch-point personalized

12 MN Unique conversions obtained annually

1.4% incremental revenue generated annually


Sample text

Advantages

Lower TCO

Enhanced marketing agility

Seamless Integration

Flexibility in defining

marketing workflows

Comprehensive Analytics

driven decisioning

Sample text

Service and content personalization• Better touch point experience • Highly targeted marketing communication

Contextual auto recommendations• Highly relevant next best actions• High possibility of influence and conversion

Lost intent revival• Better use of lost opportunities to engage

with customers• Increases ARPU and improves customer

experience

Intent Management Solution

TM


Flytxt Proven & DifferentiatedFlytxt Big Data Solutions are Faster, More efficient and EasierFully managed service with Consulting, Technology and Execution makes it Easy for CSPs to generate measurable economic valueProven to deliver 2%-7% economic value in some of the world’s toughest CSP marketsEndorsed by Industry experts and thought leaders as an emerging market innovation that is relevant for mature market CSP’s

Source: Cool Vendors in Emerging Markets, 2013. Published: 16 April 2013. Gartner.Source: Emerging Market analysis: CSP solutions for innovative solutions in Telecom services, 2013 and beyond. Published: 9 April 2013. Gartner.


Achievements and Recognitions

1. Recognized as ‘ Exemplar’ in Product Excellence Matrix for Marketing Analytics done with Frost & Sullivan

2. Featured in League of 10 Emerge Growth companies 2013

Won the 2011 IEEE Cloud Computing Challenge – C3 in

recognition of its innovation in Mobile Subscriber Fingerprinting

using Big Data Analytics.

1. Cool Vendors in Emerging Markets, APAC 2013

2. Featured in Analytics, Telecom, CSP Operations Hype Cycles, 2013

3. CSP Framework for Innovative Solutions in Telecom, 2013

Finalist among Top 5 in Asia Communication Awards under

innovation category, 2013

1. How Can European Operators Make The Most Of Mobile Marketing & Advertising?, 2010

2. Mobile Messaging Vendor Overview., 2012


Thank Youwww.flytxt.com

http://www.flytxt.com/

http://www.flytxt.com/