Industry Forecast Project Pitch Deck
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Television production is really kind of archaic--it has more or less followed the same model since its inception, and that model doesn't really use data effectively to help decide what content is likely to be successful. We believe that by providing content creators access to our entertainment database that they can make more informed choices about their shows, and create better television for us all.
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This document describes a web application called TVForecastIFPro that aims to disrupt the television industry by utilizing big data analytics. It provides comprehensive entertainment data and insights to networks for show development, targeting audiences. The application offers customizable analytics on viewership, ratings, demographics and more. It claims competitive advantages over other solutions through its unique database, affordable fees, and intuitive interface. The goal is to provide networks an affordable alternative to traditional methods for better return on investment.
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The document analyzes Netflix's launch and popularity in Canada based on Twitter data from May to November 2010. It finds that tweets about Netflix spiked at its September launch date and July announcement, but then leveled off. While sentiment was mostly neutral, positive and negative comments were balanced. The analysis suggests Netflix monitor social media for feedback and leverage the strong connection between Netflix and Xbox in marketing.
Data to make hit tv show
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This document describes a web application called TVForecastIFPro that aims to disrupt the television industry by utilizing big data analytics. It provides comprehensive entertainment data and insights to networks for show development, targeting audiences. The application offers customizable analytics on viewership, ratings, demographics and more. It claims competitive advantages over other solutions through its unique database, affordable fees, and intuitive interface. The goal is to provide networks an affordable alternative to traditional methods for better return on investment.
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This document describes a web application called tvforecastifpro.co that aims to utilize big data in the television industry. It will create the world's largest entertainment data warehouse and provide actionable analytics and insights through an intuitive interface. This will allow users access to comprehensive television viewership data and metrics for affordable monthly fees, addressing the need for affordable big data software in the industry.
Netflix
The document analyzes Netflix's launch and popularity in Canada based on Twitter data from May to November 2010. It finds that tweets about Netflix spiked at its September launch date and July announcement, but then leveled off. While sentiment was mostly neutral, positive and negative comments were balanced. The analysis suggests Netflix monitor social media for feedback and leverage the strong connection between Netflix and Xbox in marketing.
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With so many TV shows out there, it is a very competitive area of responsibility. Roy has to find shows that are incredibly influential and inspiring for the audiences worldwide, keeping up with the brand value of Amazon Web Services. In simple words, he has to prepare shows that are very to nearer to 10 on the scale of 1-10.
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2) During data understanding, discovering key features is difficult given the vast amounts of data.
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- Their data set includes over 200 terabytes of information on 113 million daily events and 400,000 weekly ads, which they use advanced statistical techniques and machine learning to analyze.
- Their analysis shows that while audiences are fragmenting across many channels, simple algorithms applied at large scale using their extensive data can better predict audience movement and interests than other existing tools.
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1) There are challenges in data preprocessing due to issues like noise, incompleteness and sparsity in data. Additionally, determining whether more data is always better poses a veracity issue.
2) During data understanding, discovering key features is difficult given the vast amounts of data.
3) When learning and modeling data, there is a tradeoff between timeliness and precision that sampling methods must address, and more sophisticated techniques are needed.
4) Post-processing also poses challenges around effectively deploying complex models generated from big data analytics.
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- Social media tools like Twitter and location-based services allow widespread collection of geotagged data from users.
- Mobile devices are becoming the primary way people access and share data. This will impact application development and how information is input and output.
- Crowdsourcing of data from many individuals through social media could challenge traditional notions of authoritative data sources and funding models.
- New opportunities exist to involve communities and different stakeholders in data collection and decision making in ways not previously possible. Standards and policies may need to change in light of these new technologies and practices.
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The document discusses trends in digital media consumption and online advertising. It notes that the online ad market is growing rapidly and will surpass newspapers by 2009. It also discusses how consumers, especially youth, are spending more time with digital/online media like social networks, games, and videos compared to traditional TV and print. Marketers are thus shifting more ad spending online to reach these engaged audiences.
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Industry Forecast Project Pitch Deck
- 1. ifpr.c Hell ther! Jessic MGlor Founder an C.E.O. @tvforecast
- 2. Bi Dat bi solution ifpr.c @tvforecast
- 3. proble Traditional television does not effectively utilize big data ifpr.c @tvforecast
- 4. solutio A web application with actionable analytics and insights ifpr.c @tvforecast
- 5. Wha ar w doin? Creating the world’s largest and most comprehensive entertainment data warehouse so we can #disruptTV ifpr.c @tvforecast
- 6. Cas Stud: Ordered by NBC for 2011 pilot season + 2 million dollars/episode + Ran 3 episodes before cancellation ifpr.c Playbo Club Hug l for th networ @tvforecast
- 8. 50 dollar/mont 100 dollar/mont 500 dollar/mont Viewership General ratings Viewership General ratings Talent ratings Social media metrics Demographics Viewership General ratings Talent ratings Social media metrics Demographics Audience retention Predictive analytics ifpr.c @tvforecast
- 9. 300,000 television shows are currently in production in the United States 500,000 workers staff these shows 90% of them want affordable big data software that provides great return on investment ifpr.c @tvforecast
- 10. Meta Data Social media metrics Data Analytics Predictive Analytics Dynamic Charts ifpr.c Bluefin Labs Rovi IMDB Pro Variety Insight Industry Forecast Project Affordability Competitor @tvforecast
- 11. Competitiv Advantage Few competitor Hig customabilit Unique database Stagnant field Pick what works Pause when needed Accessibilit Desig Affordable fees Competitive insight Intuitive Minimalist ifpr.c @tvforecast
- 12. Together le’ #DisrupTV ifpr.c @tvforecast
- 13. B seein yo! @TVForecast IFPro /TVForecast ifpr.c Or reac ou t jessic@ifpr.c @tvforecast