mooga app personalizer
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Enhancing every App’s SalabilityTRANSCRIPT
Mooga App Personalizer
Enhancing every App’s Salability
July 26, 2010
iKen’s Purpose and Vision
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 Core Purpose
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.
Our Vision
• 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
iKen Overview
iKen References
iKen’s Global Presence – Clients & Partners
PARAGUAY
SRI LANKA
INDIA
U
URUGUAYY
BRAZIL
ARGENTINA
KENYA
• 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-‐[email protected]
iKen Recogni@ons
Operator’s Dilemma
Today’s Challenges
Apps Apps Everywhere..!!!
• Which app to promote to which user • How to mone@ze the en@re App inventory • How to enable App Discovery • How to Personalize the user’s experience
• How to quickly “get navigated” to an App of my choice/taste • I am willing to pay a premium for my experience, but I don’t get it.
Customer’s Dilemma
• 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
What is Mooga
Intelligent User Criteria Matching
Mooga Hybrid Artificial
Intelligence Framework
Lazy learning, adaptive and
real-time framework
Adapting to changing personal
tastes (including time and location )
Understanding wisdom of
crowd (what people do?)
Content filtering and clustering
Business rules, Flexible modeling, configuration and
customization
Mooga Hybrid AI Framework
Mooga App Personalizer (MAP)
Inputs to MAP
App Metadata
Dynamic Behavior & Interac@on
Wisdom of Crowd
Personal Preferences Business
Rules & Policies
Personal Profile
Market Informa@on
Personalized Apps to every user
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
Why Mooga App Personalizer
Content Universe
User and Business Logic and Policy Rules
Individualized and Common contents
Hybrid AI Techniques
Dynamic and Incremental CFs
True personalization based on Hybrid AI
Content Filtering User Preferences
Users’ Transactions, Ratings, Tagging,
etc.
Clustering (based on feature
matching)
Personal Attributes(global
and local)
Buy, browse, download, referred Ratings and location
Products or contents or promotional material or advertisements (at what time and when) the customer/user will likely respond to or would like to buy/view/ download or should be served. Automatically skips the contents already downloaded/bought etc.
Domain Knowledge
Meta Contents, Taxonomy, Keywords,
Tags,…
Content Discovery
Basic Ranked DB Search
How does it work?
User Transactions User Profile
User Profile Data What kind of products or contents user likes? What keywords, tags, etc. user searches? What campaigns user responds? When user prefers transactions (day, time, month)? Where user does transaction (location)? What kind of likely personal characteristics user is having?
INPUT OUTPUT (N=1) P&R Processing
Customers Long Tail (niches)
Unique and personalized experiences
Broader Groups (Clustering/ Classifica@on)
N=G N=LT N=1
Example-‐Clustering based on N=1
Cluster can be created based upon different Parameters
• Usage (Heavy, Moderate, etc)
• Location
• Access Interface (Web/WAP etc)
• Content Category
• Demographics
• Other configurable cluster
• Combinations of defined clusters
Heavy Users
Enthusiastic users
WEB(interface based cluster)
IVR(interface based cluster)
Create Unlimited Cluster Types
Common between two Clusters
All this Results in
• User specific Personalized App promo@on • Mone@za@on of Long Tail thru Discovery • Increased Customer S@ckiness • More revenue from each user
• Superior Experience • Less pain in naviga@on • “I get what I want”
Operator’s Delight
Customer’s Delight
Exploit the Unexploited
P&R Logical level diagram
CMS DB/Content DB/RSS Feeds
Client Application Server (Web/WAP/IVR, etc Server)
Mooga P&R Database
Meta data creation and data synchronization
Web Services
iKen Studio Mooga P&R extensions
Tag Mapping
Domain logic and models: Business Rules, logic etc.
Application Front-end (Mobile)
Integration APIs to wrap web services
Application Front-end
(Web)
Application Front-end
(Broadband)
Application speciEic Vocabulary
Domain Vocabulary
Scheduler
User info & Click Streams
P&R Information
Application Front-end
(Digital TV)
Mooga Component Level Architecture
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 *.
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).
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%
Case Study: Airtel
Contact Details
India
iKen Solutions India Pvt. Ltd.
3rd Floor, SINE, CSRE Department
Indian Institute of Technology Bombay
Powai, Mumbai - 400 076, India
Phone1: +91-22-2572 2675
Phone2: +91-22-6518 2059
Email: [email protected]
Latin America
iKen Solutions – Americas
Blanco Encalada 88, Piso 1, Oficina 6, Boulogne
(CP 1609) Buenos Aires, Argentina
Email: [email protected]
