Enhancing every App’s Salability

1. Mooga App
Personalizer
Enhancing
every
App’s
Salability
July 26, 2010

3. iKen’s
Purpose
and
Vision
Our
Core
Purpose
To
make
available
to
customers
what
they
want.
Trea@ng
each
individual
dis@nctly
and
Personalizing
his/her
experience
in
content/service
consump@on
lies
at
the
core
of
iKen’s
products.
Our
Vision
We
are
poised
to
bring
about
a
paradigm
shiH
in
the
way
market
treats
customers
today.
iKen
is
confident
of
taking
Mooga
from
present
day
“great
to
have”
percep@on
to
a
“must
have”
demand
in
the
following
years.

4. iKen
Overview
• An
IIT
Bombay
research
spin-­‐off
• Opera@ons
began
in
June
2008
• Headcount:
25,
with
offices
in
Mumbai,
India
and
Buenos
Aires,
Argen@na
• Exper@se
in
Intelligent
Business
Systems
backed
by
Business
Intelligence
2.0
and
Hybrid
Ar;ficial
Intelligence
Techniques
• iKen
has
a
comprehensive
soHware
framework
named
as
Mooga.
It
is
a
BI
2.0
pla[orm
for
N=1
analy@cs
services.
• Mooga
can
be
applied
into
Telecom,
Mobile
VAS,
Internet
(Entertainment,
Retail,
e
Commerce),
Customer
Lifecycle
Management,
Customer
Care,
BFSI,
Billing,
ERP/
CRM,
Educa@on
and
with
Independent
SoHware
Vendors
having
respec@ve
Domain
Exper@se

5. iKen
References

6. iKen’s
Global
Presence
–
Clients
&
Partners
U
INDIA
BRAZIL
SRI
LANKA
KENYA
PARAGUAY
ARGENTINA
URUGUAYY

7. iKen
Recogni@ons
• NASSCOM
Innova@on
Awards
2008
Finalist
• Selected
by
MicrosoH
to
par@cipate
in
Le
Web
´08
as
one
of
the
Top
10
innova@ve
startups
in
the
world.
• First
at
the
Tie-­‐Canaan
Entrepreneurial
Challenge
2008.
• Mooga
won
Silver
Award
for
“Best
Technology
Innova@on”
at
the
Mobile
Content
Awards
2008.
• Among
Top
25
start-­‐ups,
Silicon
India,
May
2010
hlp://www.thesmarlechie.com/magazine/
• Among
DARE’s
“75
start-­‐ups
you
can
bet
on”
hlp://www.dare.co.in/people/75-­‐startups-­‐you-­‐
can-­‐bet-­‐on/iken-­‐solu@ons.htm

9. Today’s
Challenges
Apps
Apps
Everywhere..!!!
Operator’s
Dilemma
Customer’s
Dilemma
• Which
app
to
promote
to
• How
to
quickly
“get
which
user
navigated”
to
an
App
of
my
• How
to
mone@ze
the
en@re
choice/taste
App
inventory
• I
am
willing
to
pay
a
premium
• How
to
enable
App
Discovery
for
my
experience,
but
I
don’t
• How
to
Personalize
the
user’s
get
it.
experience

10. What
is
Mooga
• Next
genera;on
personaliza;on,
matching,
discovery
and
recommenda;on
framework
based
on
the
N=1
concept
• Supports
various
types
of
structured
contents
and
generic
transac;ons
seamlessly
and
uniformly
• Based
on
social
(collabora;ve)
filtering,
content
(logical
and
contextual)
filtering,
intelligent
matching
and
on
individual
tastes
along
with
adapta;on
to
;me
and
loca;on
dimensions
• Works
in
real-­‐;me,
self-­‐learning
and
is
completely
programmable,
configurable
and
customizable
based
on
products,
contents
and
required
func;onality

11. Mooga
Hybrid
AI
Framework
Understanding
wisdom of
crowd (what
people do?)
Content filtering
Adapting to and clustering
changing personal
tastes
(including time
and location ) Mooga
Hybrid Artificial
Intelligence
Framework
Business rules,
Flexible modeling,
Intelligent User configuration and
Criteria Matching customization
Lazy learning,
adaptive and
real-time
framework

12. Mooga
App
Personalizer
(MAP)
Personal
Preferences
Business
Wisdom
of
Rules
&
Crowd
Policies
Dynamic
Personal
Behavior
&
Profile
Interac@on
App
Inputs
Market
Metadata
to
MAP
Informa@on
Mooga
Analy@cs
Engine
learns
each
user’s
taste
&
preference
thru
her
consump@on
palern
and
picks
up
the
most
relevant
app
that
suits
her
liking
Personalized
Apps
to
every
user

13. Why
Mooga
App
Personalizer

14. How
does
it
work?
INPUT
P&R
Processing
OUTPUT
(N=1)
User Transactions User Profile
Users’ Transactions,
Ratings, Tagging,
etc.
Buy, browse, Personal
download, referred Attributes(global
Ratings and location and local) Clustering Individualized and
Meta Contents,
(based on feature Common contents
matching)
Taxonomy,
Keywords,
Content Filtering
Tags,…
User
Preferences
Dynamic and Domain
Incremental CFs Knowledge
Products or contents or promotional
Content Discovery material or advertisements (at what
User Profile Data User and time
What kind of products or contents user likes? True personalization Business Logic and when) the customer/user will
What keywords, tags, etc. user searches? based on Hybrid AI
and Policy Rules likely
What campaigns user responds? respond to or would like to buy/view/
Basic Ranked download or should be served.
When user prefers transactions (day, time, DB Search
Content
month)? Universe
Automatically
Where user does transaction (location)? Hybrid AI skips the contents already
What kind of likely personal characteristics user is Techniques
downloaded/bought etc.
having?

15. Example-­‐Clustering
based
on
N=1
N=G
N=LT
N=1
Customers
Broader
Long
Tail
Unique
and
Groups
(niches)
personalized
(Clustering/
experiences
Classifica@on)

16. Create
Unlimited
Cluster
Types
Heavy Users Cluster can be created based
upon different Parameters
• Usage (Heavy, Moderate, etc)
• Location
• Access Interface (Web/WAP etc)
• Content Category
WEB(interface
based cluster)
• Demographics
IVR(interface
based cluster) • Other configurable cluster
Enthusiastic users • Combinations of defined clusters
Common between two Clusters

17. All
this
Results
in
Operator’s
Delight
• User
specific
Personalized
App
promo@on
• Mone@za@on
of
Long
Tail
thru
Discovery
• Increased
Customer
S@ckiness
• More
revenue
from
each
user
Customer’s
Delight
• Superior
Experience
• Less
pain
in
naviga@on
• “I
get
what
I
want”

18. Exploit
the
Unexploited

20. P&R
Logical
level
diagram

21. Mooga
Component
Level
Architecture
Application Application Application Application
Front-end Front-end Front-end Front-end
(Mobile) (Web) (Broadband (Digital TV)
)
Client Application Server (Web/WAP/IVR, etc Server)
Integration APIs to wrap web services
User
info
&
P&R
Click
Streams Information Domain
Vocabulary
iKen
Studio
Mooga
P&R
Scheduler
Application
speciEic
extensions
Web
Services
Vocabulary
Domain
logic
and
Meta
data
models:
Business
creation
and
data
Rules,
logic
etc.
synchronization
Mooga
P&R
Tag
Mapping
Database
CMS DB/Content DB/RSS Feeds

23. Case
Study:
Airtel
About
Airtel
• Bhar@
Airtel
Limited,
formerly
known
as
Bhar@
Tele-­‐Ventures
LTD
(BTVL)
is
an
Indian
company
offering
tele-­‐communica@on
services
in
18
countries.
• It
the
largest
cellular
service
provider
in
India,
with
more
than
135
million
subscrip@ons
as
of
May
2010.
• Bhar@
Airtel
is
the
world's
third
largest,
single-­‐country
mobile
operator
and
fiHh
largest
telecom
operator
in
the
world
in
terms
of
subscriber
base.
It
also
offers
fixed
line
services
and
broadband
services.
• It
offers
its
telecom
services
under
the
Airtel
brand
POC
for
Personalized
Ring
Back
Tones(RBT):
Scope
• Aitel
proposed
a
market
with
high-­‐traffic,
diverse
demographics,
high
consump@on
of
music
and
which
could
be
representa@ve
for
other
markets.
Mumbai
was
the
chosen
circle.
• RBTs
get
downloaded
through
various
channels
such
as
WAP,
USSD,
IVR,
*Copy,
OBD,
etc.
Implemen@ng
Mooga
services
on
a
Virtual
Number
(VN)
was
step
1.
Based
on
results,
integra@on
on
other
channels
was
to
be
encompassed.
A
virtual
number
is
a
short/long
code
which
subscribers
dial
in
to
listen
to
a
sequence
of
songs.
They
can
select
a
song
of
their
choice
any@me
by
pressing
a
*.

24. Case
Study:
Airtel
POC
for
Personalized
RBTs:
Scope
• Before
Mooga
deployment,
Airtel
would
play
a
set
of
5
songs
randomly
every
day
for
all
its
subscribers
(irrespec@ve
of
their
likings).
If
a
user
didn’t
find
a
song
of
her
interest
aHer
calling
the
VN,
she
would
hang
up
and
call
back
aHer
some
@me
to
get
to
listen
to
a
new
set
of
songs.
This
would
go
on
@ll
she
would
finally
come
across
a
song
of
her
choice.
• We
started
off
with
providing
Personalized
Recommenda@ons
on
the
VN
from
the
1st
week
of
June
2010.
Mooga
gave
Personalized
Recommenda@ons
to
each
and
every
individual
based
on
her
taste
and
liking.
The
sequence
of
songs
would
dynamically
change
in
real-­‐@me
from
session
to
session.
• Since
Mooga
is
a
self-­‐learning
system,
Recommenda@ons
get
more
and
more
precise
and
relevant
with
@me
(as
the
system
learns
more
about
the
user).

25. Case
Study:
Airtel
Results
 The
average
number
of
downloads
increased
by
a
staggering
150%
over
the
VN
in
just
a
span
of
1
month.
 From
a
Sales
Distribu@on
perspec@ve,
Mooga
is
helping
Airtel
sell
in
one
day
what
they
used
to
sell
in
one
month.
 The
total
numbers
of
calls
made
to
the
VN
have
increased
thrice
as
much
as
people
are
making
more
and
more
calls
as
they
are
hearing
up
to
100
songs
of
their
interest
from
earlier
5
earlier.
Because
it
is
a
toll
free
number,
people
have
made
this
like
radio.
Here
conversion
rate
is
higher
than
10%

26. Contact
Details
India Latin America
iKen Solutions India Pvt. Ltd. iKen Solutions – Americas
3rd Floor, SINE, CSRE Department Blanco Encalada 88, Piso 1, Oficina 6,
Boulogne
Indian Institute of Technology Bombay
(CP 1609) Buenos Aires, Argentina
Powai, Mumbai - 400 076, India
Email: iKen@iKensolutions.com
Phone1: +91-22-2572 2675
Phone2: +91-22-6518 2059
Email: iKen@iKensolutions.com

27. www.ikensolu@ons.com
Thank
You