The goal of Agile is to bring user stories from ideation to deployment, considering who the user is, what they want to accomplish, and how they accomplish it with that feature. Plot that numeric representation in the form of vectors across a 3D sphere like we did above — and now Netflix start forming relationships between data points. The former has been watched by 80 million households (58% of all subscribers), the latter — by 40 million (29%). Do we have data for that? One is Tobey Maguire and the other is Andrew Garfield. Well, besides learning the millions of individual thumbnails that converted users to loyal subscribers over time, here are a few additional things Netflix has learned for what works in terms of thumbnails: Netflix has done a phenomenal job of applying AI, data science, and machine learning the “right way” — using a product-based approach that focuses on business need first, then AI solution next, rather than the other way around. Netflix recognizes that it competes, above all, for the customer’s undivided attention. What data does Netflix use target these custom-created thumbnails to the appropriate individual? A 1 hour episode of Stranger Things has >86,000 static video frames, These video frames can each individually be assigned certain attributes that are later used to filter down to the best thumbnail candidates through a set of tools and algorithms called Aesthetic Visual Analysis (AVA). From a product perspective, the short answer is yes, and we’ll get to why that is later in this article as we dig deeper. Whether you stream shows or surf TV channels, there’s a lot we can learn from the media mogul. One of the major advantages of Agile is that it allows teams to release software more often instead of having longer release cycles and only delivering every few months or even years. Then we will dive a little deeper into what is perhaps the most interesting of these 5 use cases as we identify what business problem it seeks to solve. We want to provide a healthy mix of the familiar with the unexpected but also accurately portray content to the user so they aren’t improperly misled. The company draws upon consumer science, constantly uses data, does qualitative research and surveys, and A/B-tests new ideas. Lastly, the algorithm should take into consideration what thumbnail images the user previously saw in association with this movie and aim to provide consistent, non-confusing user experience. Netflix is a streaming service that offers a wide variety of award-winning TV shows, movies, anime, documentaries, and more on thousands of internet-connected devices. But at the end of the day, it’s pretty straightforward. Similar to Agile development, it seems like Netflix also strives to focus on fast feedback, iterative changes, and cross-collaboration. For the same Good Will Hunting movie below, one user identified as a comedy fan would be shown a Robin Williams (comedian) thumbnail, whereas another user identified as a romantic comedy fan would be shown a kissing thumbnail featuring Matt Damon and Minnie Driver. any seasonal or weekly trends related to a user’s level of engagement, etc. In other countries, we earn a lower percentage of screen time due to lower penetration of our service. And that individual or group worked together (probably with UX and related stakeholders) to put together user studies or data elsewhere, to prove that there was indeed a strong link between an image thumbnail and viewership. Watch AI & Bot Conference for Free Take a look, Netflix ended up presenting thumbnails to users that matched a user’s ethnicity, Becoming Human: Artificial Intelligence Magazine, Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data, Towards further practical model-based reinforcement learning, Designing AI: Solving Snake with Evolution. Introducing a subscription fee to the video on demand service was certainly a risky move. In fact, Netflix Originals have a 35% higher success rate than new TV shows released on-network. So when spatially represented, the distance between two user profiles represents how similar / different their tastes are. So it looks like Netflix is one of the leading tech company out there to use the science of behavior change for its success. 2. 5 Use Cases of AI/Data/Machine Learning at Netflix. In fact, algorithms designed to exploit metrics will do just that — so it is the role of the product manager to work with design or other team members to find ways to address these deficiencies in algorithms. Marketing Blog. This is just yet another example of how a business need supercedes a popular user need! Each movie should ideally have a personalized thumbnail that maximizes clicks. Here’s a possible way out. So those are some things a product manager would consider when designing edge case scenarios and what extreme cases of data usage can result in. It looks like Netflix learned how to mass produce successful and thrilling stories. In the process, the company creates moats and increases margins. Integrate testing into every step of the software development lifecycle so you can be sure you’re delivering something your customers will love. If predictions turn out bad or good, they adjust the mathematical positioning of these characteristics accordingly until the model becomes better and better over time. “We also saw that users spent an average of 1.8 seconds considering each title they were presented with while on Netflix,” Nelson wrote. For example, a tech enthusiast might say: Wouldn’t it be cool if you could analyze / debate an episode using voice with Netflix — and Netflix, with data input from thousands of other users’ reactions to that episode, could respond intelligently to your comments in a back and forth 2-way dialogue? Recommend this article to read more pieces alike. The Netflix Business Model. Only through proper positioning and connection with Netflix’s core business problem did these ideas become the reality that they are today. An Essential Guide to Numpy for Machine Learning in Python, Real-world Python workloads on Spark: Standalone clusters, Understand Classification Performance Metrics. This is how Netflix, or really any company leveraging ML models, creates relationships between seemingly unstructured data and turning that data into numbers. A product manager should always think ahead of possible edge case scenarios in which the algorithm may fail to produce the best results. See the original article here. As the world of AI, data science, and machine learning continues to grow, we product managers can all take a lesson or two out of the Netflix playbook when it comes to properly deploying AI solutions. If it’s related, what evidence (qualitative or quantitative do we have to support that relationship? By taking a closer look at the way the company operates behind the scenes (or should we say, behind the screens), Agile development teams can learn quite a bit from Netflix. Cross-browser testing is a no-brainer, so be sure to not to skip it if you really want to release a high-quality application for every user. So be aware that an overly optimized / personalized experience could create a monotonous user experience that in some cases can be misleading to the user. What data does Netflix use to create these personalized thumbnails / artwork? That will depend on company strategy. Netflix has a huge customer base, which means they probably have a lot of diversified device usage. Netflix has found that releasing new movies and shows on a weekly basis keeps customers excited and intrigued, so they don’t get bored by the same selections over and over. An intuitive introduction to Machine Learning. Not only would this confuse the user, but it would also make it difficult for a Product Manager to assign attribution to a click — which image resulted in a higher click-thru-rate (CTR) when it keeps changing? Yet, Netflix’s algorithm (arguably) made false thumbnail recommendations of supporting black actors/actresses who don’t really represent what the movie was about, but did experience a higher click rate among certain ethnic audiences. Does that make sense? That’s not to say that the development team does or does not follow an Agile methodology, but it more so has to do with the way that Netflix releases content. Opinions expressed by DZone contributors are their own. PM’s need to be able to properly attribute each new result to a specific change — so maintaining consistent data attribution is important. Because this “feature” actually reduces viewership, as negative reviews discourage users from trying out a video. Wouldn’t it be weird for the user to see both portraits of Maguire and Garfield as Spiderman with their masks off — side by side? Likewise, if a user is labeled a “4” by Netflix, then he/she will be placed in the general vicinity of where all the other magenta 4’s are in the above spatial representation (near the top). This could be analogous to how users who like romantic comedies could also like parody or satire movies because they both involve laughing. Before Netflix, this practice was still unheard of. That was their hypothesis: that adjusting the artistic content of an image thumbnail could have a strong link to viewership. What’s the business result we are trying to achieve with ML? And how confident are we that tweaking an image thumbnail will affect viewership or subscriber loyalty in a positive way? Well, turns out, back in 2014, Netflix conducted studies showing just how important that thumbnail is: Nick Nelson, Netflix’s global manager of creative services, explained that the company conducted research in early 2014 that found artwork was “not only the biggest influencer” for a user’s decision about what to watch, it also constituted over 82 percent of their focus while browsing Netflix. When applied properly, AI can do wonders. What business impact would such a solution have in comparison to the level of effort? If the goal is to maximize that probability of watching by tweaking the thumbnail — what are some product decisions to consider?
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