E-book recommendation

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E-contenta performs Facebook data analyses, identifies your preferences and recommends books to read. …

E-contenta performs Facebook data analyses, identifies your preferences and recommends books to read.

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  • Amazon – as a free source of content for pilot release. Integration through open Amazon API
  • E-contenta business idea is based on two remarkable trends that we can see now: first trend is a tremendously increasing share of e-book market, the second one – growing number of social media users. Nowadays more than 65% of all internet users WW explore social media. Mostly Facebook. At the same time e-book market is growing and US is the biggest one in the world – $2,79 billion. Imagine, that the share increased from 0,6 in 2008 to 10% in 2012. The major part of e-readers prefer to buy books on Amazon. What device do they use? About 40% prefer tablets and this percentage is growing! 60% of tablets users can be found in the airports. And guess why 8 from 10 people would rather select e-book? Because they are easier to find and they like to read e-books while travelling! These fascinating findings will help to define key features of E-contenta, its functionality for the pilot release and the market to position on. The concept is clear. For e-reading Americans use KindleFire – 40%, Nook – 7%, iPad – 10%. New tablet Kindle Fire 79-199 $ named iPad Killer
  • E-contenta recommendation approach will be different from the methodology that is being used now by the majority of book websites work. Classic recommendation system is based mostly on previous reading choices. Social media provide us with great amount of data. It can and should be turned into buying recommendations. 1) Approach based on books description and lexis from user's facebook page. This approach will be done first. We have crawled about 1000000 book's description s from www.goodreads.com. Then, we divided it to several(about 500) different clusters, using k-means algorithm(http://en.wikipedia.org/wiki/K-means). For every cluster we found best books (based on goodreads user's ratings). For recommendation we get keywords from users page(profession, interests, likes etc.), find the most closest cluster of books for this keywords and recommend most rated books from this cluster. 2) Approach based on classic machine learning algorithms. Some users post on their facebook page books, that they like. We can crawl a lot of such profiles from facebook and use it as traning set for classical machine learning algorithms. For this algorithms we must extract features from users page. As features we can use for example: - how often user chanche his avatar - how many friends user have - age - gender - marital mtatus - interests and many other features. We can decribe user as set of such features, and using classical machine learning algorithms(see for example http://en.wikipedia.org/wiki/Support_vector_machine) build recommendation engine on our traning set. 3) Collaborative filtering - classical recomendation approaches, used on large sites such as Amazon.com. This is based on compare preferences of different users. Base idea is follow: if a lot of users who like book A like book B, then if new user like book A we can recommend him book B. See http://en.wikipedia.org/wiki/Collaborative_filtering for details. 4) Using thrid-party search engines. We automatically can search in search engines such as google books relevant for user. For example, by this link: www.google.ru/search?client=ubuntu&channel=fs&q=k-means&ie=utf-8&oe=utf-8&gws_rd=cr&ei=hWQEUof0Je3M0AXo2ID4Dg#bav=on.2,or.r_cp.r_qf.&channel=fs&fp=6e282c34c22913bd&newwindow=1&q=book for venture investors&safe=off we can grab some books for venture investor. Profession we can get from facebook profile.
  • Reference audience to test concept – airport passengers. More than 60% of tablet users can be found in airports and 80% of travellers prefer e-reading to paper because they find the process of acquiring, transporting and reading digital books easier. After the initial trial of the program at one of the airports, we can expand the program and gradually implement E-Contenta in other airports worldwide. E-Contenta is a “win-win” offering for all the participants. Passengers will get targeted content recommendations and ability to get uniquely-matched digital media, while airports can promote other services to passengers through E-Contenta’s recommendation system. At the same time, it is a good opportunity for airports and airlines to increase the loyalty of passengers and enhance their social network image as passengers share quotes, recommendations, etc., with their friends. E-Contenta will facilitate this interaction and will serve as a connector that facilitates interaction among providers of digital contents, passengers who are eager to get uniquely-matched digital contents, and airports which want to promote their brands and services. The target of the trial is to draw approximately 200 thousand users, process their feedback and adjust the platform to their needs.
  • At the initial stages we will be more focused on B2C model: will get money from users who will enjoy individual recommendations and buy books through the E-contenta application. While the number of application users is growing companies such as publishing houses or other content distributors or independent writers may be interested in us to get very targeted access to book buyers. Distribution models. We can sell directly to users or through the channel. Easy integration with 3 rd party websites allow to grow network of channel partners. While releasing a new book a publisher always have a question how to market it best. E-contenta will allow to run targeted personalized marketing campaigns and gain maximum ROI. E-contenta will keep information about its users and their buying behaviour to provide publishers with trend analysis and predictive forecasts. Flexible service fee will be affordable for a business of any size. Here we need a quote from publisher how briliant the idea is… Business diversification Additional income source No investment needed Loyalty of existing customers

Transcript

  • 1. 20111
  • 2. 20112 Get unique and instant snapshot of your personality transformed into book library. E-contenta performs Facebook data analyses, identifies your preferences and recommends books to read.
  • 3. 20113 E-contenta is a predictable analytics platform focused on using social media data to provide more targeted purchase recommendations to users and more effective marketing strategies for e-commerce companies “Special sauce”, personalized recommendation system based on social media and online user behavior, allowing for higher success rate in selling e-books, movies, audiobooks and music. First release of E-Contenta will be based on integration of Facebook-sourced data with Amazon’s product offerings. Both systems have open API. Recommendation algorithms will based on social media behavior, while Amazon will serve as a book catalogue. Expecting revenue of $8.8 million and EBITDA of $1.8 million in the 1st year after the product launch Our mission is to be the best proactive recommendation system and achieve revenue of $20-$50 million in 3-5 years Executive summary
  • 4. 20114 Around 80% of readers prefer digital book to paper because it’s easier to find and read while travelling US e-book market is the biggest in the world: $2,79 billion 70% buy e-books on Amazon The share of e-book market in US is growing from 0,6% in 2008 to 10% in 2012 (in some genres up to 25%) 38% prefer tablets as a key device for e-reading (+25% growth compared to 2011) 60% of tablets users can be found in the airports FB is the leader: 1 billion active users (2012) All adult internet users who use social networking sites increased from 8% in 2005 to 65% in 2011 (WW) Trends that initiated the E-contenta business idea
  • 5. 20115 E-Contenta Books US market English Focus on travelling Recommendations: Facebook analysis Amazon/OverDrive/B&N, etc. Web and mobile, app
  • 6. 20116 How it looks like? E-Contenta 1st release
  • 7. 20117 Where are we moving? 2nd release concept Tag words cept!
  • 8. 20118 Value Proposition
  • 9. 20119 E-Contenta recommendation modules
  • 10. 201110 Architecture
  • 11. 201111 1. Check-in at the airport 2. Get a Facebook or other ad 3. Apply for E-contenta app 4. Get personalized recommendations 5. Refer friends and get points that can be exchanged for discounts on purchases How to get first users?
  • 12. 201112  Promo and outdoor ads in the airports  Joint campaigns with airport internet providers, airport restaurants and cafes and airline companies  Joint campaigns with publishing houses  Joint campaigns with tablet producers Offline OnlineMarketing
  • 13. 201113 Allows e-tailers to have more targeted approach to their potential consumers Facilitates targeted selling of e-books, audiobooks, movies, music or media items/subscriptions to customers B2 C B2B Business models
  • 14. 201114 Hypothesis • People will look for something interesting to read, watch or listen while travelling • They are interested in getting personalized recommendations • They will start using E- Contenta • The most satisfied users will recommend the app to friends Metrics • App views/app users = 70% • Paying users/users = 15% • % of those who share information in FB friends community – 35% • Repeated purchase – 40% from DB every 2nd month
  • 15. 201115 Competitors
  • 16. 201116  Competitors do not have social network strategy and prefer to work within their stand-alone portals  Amazon-60-70%, BN-20%, iBooks-10%, others (Books-A-Million, Kobo, Sony and others)  Classic recommendation system is based mostly on previous reading, viewing and ratings  The only feature based on info taken from social media is displaying what your Facebook friends are reading, [watching, listening]  There is specific niche that could be carved – be proactive, adapt to individual preferences and react to needs and wishes of every particular user Competitor s
  • 17. 201117  Geo-expansion  Integration with new social networks  New content types = transmedia  New knowledge map schemes = more and more personalized recommendation approach Strengths  Growing market of e-books, audiobooks, movie and music downloads  Micro payments  Agent role = minimum of risks  E-business = logistics transparency  Statistics = effective service management  Dependence on large and powerful players in the digital world  Competitors  Market immaturity  Ability to execute on the business plan  Adoption of the product  Potential competition from e-tailers if they decide to copy the strategy Weaknesses Opportunities Risks
  • 18. 201118 www.e-contenta.com +79218629604 Zoya Nikitina