Case study over current position of Netflix and where it is heading. AFI framework was used to provide insight into new viable strategies with recommendations on how Netflix can maintain a competitive advantage in the future.
Netflix’s unique DVD rental service has revolutionized the industry. They successfully took the best of traditional conventions (like physical media, the U.S. Postal Service) and mixed them with new world internet-conventions. They have also effectively managed to discourage competition from both more established businesses and new entrants. The future growth of Netflix as it expands into streaming media, poses challenges in legal, infrastructure/technology, and through additional costs. In order to remain competitive, it is imperative that Netflix partner with companies with global reach to overcome these challenges. This presentation was part of an MBA class assignment to audit and industry in the the technology sector. The presentation has multiple authors listed on the title page. If you would like copies of the executive summary, complete S.W.O.T. analysis, and/or the transcript of the presentation please PRIVATE MESSAGE ME and I will email it to you.
A comprehensive report evaluating Netflix, Inc. viability, stability, and profitability for future investment. The analysis provides an assessment of the firm's strategy, accounting, financial, prospective, and comes up with a buy/sell recommendation.
Over the course of the semester I worked on a group project on Netflix. Taking a look into Netflix's history and how they compete against their competitors.
This case study was done as a part of my class assignment for Introduction of Analytics. It explains how Netflix uses Big Data and why is so successful.
Why I chose Netflix
Netflix: Stepping into Streaming
CLV used in Netflix
How Netflix uses Big Data and Analytics
Latest Relevant News!!
Conclusion
The goal of a recommender system is to predict the degree to which a user will like or dislike a set of items, such as movies or TV shows.
Most recommender systems use a combination of different approaches, but broadly speaking there are three different methods that can be used: Content analysis, Social recommendations and Collaborative filtering.
Case study over current position of Netflix and where it is heading. AFI framework was used to provide insight into new viable strategies with recommendations on how Netflix can maintain a competitive advantage in the future.
Netflix’s unique DVD rental service has revolutionized the industry. They successfully took the best of traditional conventions (like physical media, the U.S. Postal Service) and mixed them with new world internet-conventions. They have also effectively managed to discourage competition from both more established businesses and new entrants. The future growth of Netflix as it expands into streaming media, poses challenges in legal, infrastructure/technology, and through additional costs. In order to remain competitive, it is imperative that Netflix partner with companies with global reach to overcome these challenges. This presentation was part of an MBA class assignment to audit and industry in the the technology sector. The presentation has multiple authors listed on the title page. If you would like copies of the executive summary, complete S.W.O.T. analysis, and/or the transcript of the presentation please PRIVATE MESSAGE ME and I will email it to you.
A comprehensive report evaluating Netflix, Inc. viability, stability, and profitability for future investment. The analysis provides an assessment of the firm's strategy, accounting, financial, prospective, and comes up with a buy/sell recommendation.
Over the course of the semester I worked on a group project on Netflix. Taking a look into Netflix's history and how they compete against their competitors.
This case study was done as a part of my class assignment for Introduction of Analytics. It explains how Netflix uses Big Data and why is so successful.
Why I chose Netflix
Netflix: Stepping into Streaming
CLV used in Netflix
How Netflix uses Big Data and Analytics
Latest Relevant News!!
Conclusion
The goal of a recommender system is to predict the degree to which a user will like or dislike a set of items, such as movies or TV shows.
Most recommender systems use a combination of different approaches, but broadly speaking there are three different methods that can be used: Content analysis, Social recommendations and Collaborative filtering.
This presentation on Mobile app marketing covers user acquisition strategies for launch and growth stage. Detailed info on user acquition - campaign planning, incent vs. non-incent, network selection, creative planning and testing, buying models, programmatic buying for mobile, campaign performance and analytics.
I presented this at NASSCOM Game Developers Conference ( NGDC 2015 ) in Pune, representing [x]cube LABS.
Mobile App User Acquisition - Launch & Growth Strategies[x]cube LABS
A detailed presentation on mobile app marketing, specific launch activities and user acquisition startegies, covering various aspects like campaign planning, Incent vs. non-incent, selecting networks, creative testing, buying models, programmatic buying for mobile, analytics & campaign measurement etc.
This was presented at NASSCOM Game Developers Conference - NGDC 2015 in Pune by Saptarshi Roy Chaudhury, VP Marketing, [x]cube LABS
Overview and some tactical advice for folks looking to put out a new mobile service or product. This presentation was given at the Women 2.0 Founder Lab Mobile event. Most of the folks were already working in mobile, but hadn't necessarily run their own projects. I'm not sure it'll make sense as a standalone set of data without me talking about it. But hopefully it will.
Recommendations are everywhere : music, movies, books, social medias, e-commerce web sites… The Web is leaving the era of search and entering one of discovery. This quick introduction will help you to understand this vast topic and why you should use it.
During this project we worked on creating and selling a mobile application and implementing it using Titanium - Appcelerator software.
The mobile application is:
MoviePedia, which is the best directory of movies you will ever use. It is very convenient for tracking new movies and their reviews. You can look for the best movies to watch in your favorite categories, watch HD trailers and photos, enjoy listening to the soundtracks of the movie, easily create your watch list, and much more.
At Your Service: What Netflix and Assessments Have In Common | SoGoSurveySogolytics
The most important data deserves the most attention, right? Whether you're scoring a quiz with weighted items and categories or prioritizing some behavioral data points over others, the algorithms behind assessment drive deeper understanding of valuable takeaways and implications.
[DSC Europe 23] Rein Zhang - Improving YouTube Recommender systems for big sc...DataScienceConferenc1
Recommender systems at YouTube scale are massive. They contain a multitude of ML and heuristic models all focused on providing timely recommendations to billions of end users. The challenge in improving the recommendations comes from 3 major sources. First, we need to identify specific user needs and behaviors that are different when using a different platform (for example, users using smartphones have different expectations of the content vs. users watching YouTube on the TV in a shared household). Secondly, we need to identify the systems where investment would lead to improved performance (and similarly, make a decision if we improve the system for all or build anew). Finally, there is always a question of tradeoff in terms of measurability of the success and cost at which it comes with (for example, how would LLM investment look like). In this talk we will elaborate more on these aspects with special focus on how we address these challenges and opportunities that come with it.
Care about learning 'Mobile App Marketing 101' You will find this deck presented by Binay Tiwari, Mobile Internet Evangelist during Digital Marketing Webinar for Digital Vidya. Interested in attending similar Webinar Live? Register Now at http://www.digitalvidya.com/webinars/
Similar to Netflix-Using analytics to predict hits (20)
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
2. Types of data collected
Tracked every search made by the subscriber
Good or bad rating attributed by each subscriber
along with Nielsen rating.
ZIP code of subscribers
Type of device on which they streamed.
Number of times he paused the show; whether
exited the show before it ends
Subscribers switching off before/after credits began
to roll
3. Post Play Feature
Netflix Knew when the customer would most probably switch off.
Popup would then be carefully adjusted by the algorithm.
4. Personalized Recommendations
Recommendations given out based on:
• Prior viewing patterns
• Viewing behaviors and recommendations from
other users
• Based on the movie just watched
• Personalized Trailers
Results:
Customer ordered more movies
75% of viewer activity is based on these suggestions
5. Rating algorithm
• Netflix recommends movies based on ratings provided by the subscribers.
• Subscribers can rate even if they have watched the movie or not watched the movie.
0
1
Not Watched the movie
0.5 Partly Watched
Watched the movie
•Netflix also looks at data within movies. They take various “screen shots” to look at “in
the moment” characteristics.
A/B testing is carried out for testing
alternate webpage designs to find out
which design generates a positive result.
7. Integrating social media for the recommendations
After logging into Netflix using the twitter and
Facebook, subscriber updates can be analyzed by Netflix
to make context based recommendations to the users.
The research suggests there is different viewing
behavior depending on :
• the day of the week
• the time of day
• the device
• and sometimes even the location.
Designing the poster after analyzing the color recognition patterns of the users and finding the impact on
customer viewing habits, recommendations, ratings and the likes.
8. Challenges
• Analyzing relevant information
• Requirement of huge Storage capacities
• Providing multiple customization to different users with same account
• The authenticity of ratings provided.
• Difficulty in decoding the tweets/Facebook updates.
• Privacy of the customers
9. Benefits..
• Accurate Licensing fees based on collected data
• number of subscribers
• number of times they watched it
• Saving $40 million a show on marketing campaigns
Only 22% of
movies are
profitable
Only 33% of
new shows
survive for
more than
one season
