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HeadNtail - the headlines till the tail:

HeadNtail - the headlines till the tail

Problem of Plenty:

Problem of Plenty Excessive news content in the news sites today makes it difficult for a user to decide what news to read? The user invariably reads only the top headlines in the news site The top headlines do not constitute more than 5% of the total news in the site Hence the news stories which do not become popular are not getting the user attention and are not consumed by users

Reading News without Context:

Reading News without Context When user reads a news about a developing story, he might not be aware of the fact that this news is part of a developing story Unless the user understands the context in which the news is present, he will not be able to make full sense out of a single news article in a series Majority of the news fits into a developing story and hence in most of the cases, the users need to know the context of the news being read.

Introducing HeadNtail – Timeline(tail) of news delivered around entities(Head):

Introducing HeadNtail – Timeline(tail) of news delivered around entities(Head)

News on Entities:

News on Entities HeadNtail brings in a paradigm shift in the way the news is read today It all starts with “ Entities ” as Head and not a list of headlines The app when launched will show a list of top entities which are making the news now The list of top entities will be personalized for each user based on their interests observed from their news reading pattern in the app

TimeLine of News:

TimeLine of News HeadNtail introduces the concept of “TimeLine of News” as Tail which helps the user understand better by giving the context of the developing story. When a user clicks on an entity in the app, he sees a timeline of news about the entity in diverse contexts. He can choose to read any of the headline in the corresponding news site or he can choose to select a context/headline to further drill down to see the timeline of news about the entity in the selected context The user can follow a given timeline to get notifications about the latest news featuring in the timeline

How does it work?:

How does it work? HeadNtail crawls news links from various news sites in return for traffic driven to the news sites from the NewZLine app The news content from the news links are extracted and the named entities ( person, location, organization, misc ) in the news content are extracted by the algorithm. The extracted named entities are processed by the algorithm to deliver personalized and contextual news around entities in a timeline.


Monetization During the initial phase of acquiring new users to the app, monetization of the app is done by running ads from Google AdMob in the app. After reaching a critical mass of users for the app (> 1 Million ), the in-house ad system for advertisers will be used for monetization. By leveraging the “User :: Entity” interest map, the advertiser can bid to display his ad to all users who are interested in a given entity AND who are also viewing headlines about the given entity. HeadNtail enables contextual and personalized ads by tracking the user interests and the user activity in the app

Market Opportunity:

Market Opportunity NewZLine is targeted for all users who read English news in Mobile India is the first market to be launched. There are over 50 million mobile internet users in India and they are growing at over 90% YOY for last 2 years. Out of the 50 million users, around 60% of them - roughly 30 million users read news online in mobile Total market size = 50 million Addressable market size ~ 30 million users which is expected to grow at close to 100% YOY in the coming years


Competition There are no mobile apps in the market today providing timeline of news around entities The closest alternative to read a timeline of news is to search for a topic in google and click “News” tab and sort it by “Date”. The number of people clicking “News” tab in a search result is absolutely minimal


Risks The product is built around machine learning and distributed computing Recruitment of talented software engineers with the skillset of machine learning and distributed computing in India is a challenge as the talent pool with this skill set is very small

The Team:

The Team Subramanian Narayanan - Founder, Product Owner Product enthusiast. Have spent close to 7 years at Yahoo working on problems of scale in consumer internet space. Shunmuga Krishnan – Co-Founder, Developer A tech geek who can code anything and everything. Holds a masters in Computer Science from Indian Institute of Science, Bangalore. Arun Rajkumar – Co-Founder, Researcher Research student from Indian Institute of Science. His research interests including machine learning – ranking algorithms in particular.

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