Mooga app personalizer

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

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.

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

iKen References

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

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

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

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

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

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

Why Mooga App Personalizer

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?

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)

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

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”

Exploit the Unexploited

P&R Logical level diagram

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

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 *.

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%

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

www.ikensolu@ons.com Thank You

Mooga app personalizer

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