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There is no dispute in the fact that Machine Learning is one of the biggest breakthroughs that would transform the way people do business. When Artificial Intelligence came in, it definitely created a buzz, and everyone was happy. And then came in Machine Learning (ML); it is a subset of Artificial Intelligence, and can be lightly called as a technique for analyzing data. But there is nothing light about ML; it has the power to provide completely new and unique insights, and sometimes capture things for which it isn’t programmed.
Machine learning is a subset of Artificial Intelligence that allows computers and other devices to automatically learn and improve from experience without being actually programmed for the same. But the accuracy in predicting outcomes is impeccable. The systems then perform a specific task, in this case, collecting data from innumerable sources and then using it for predicting particular patterns of behavior.
As per a recent Google Research article, "the goal of automating machine learning is to develop techniques for computers to solve new machine-learning problems automatically, without the need for human-machine learning experts to intervene on every new problem. If we're ever going to have truly intelligent systems, this is a fundamental capability that we will need."
When organizations become overwhelmed with the heavy influx of data, they can make use of multiple ML frameworks to achieve operational efficiency and business agility.
AWS is of the opinion that more than a million minutes of video content will pass through IP networks every second of the day by 2021, and deciphering meaningful insights from this huge influx is practically impossible. And it also becomes critical to businesses that they decipher valuable, actionable and meaningful insights from all the video content. And that’s just the content that’s coming from the outside. A huge plethora of content is being created inside too, where companies create live and on-demand video to provide employee training, analyst meetings, to broadcast news to employees all over the world, product tutorials to customers and so on. All these comprise corporate streaming. They also push forward with the expectation that corporate video streaming will grow to 70% of all business Internet traffic by 2021. Henceforth, it is imperative for companies to make these videos searchable and localized for the users.
Machine learning plays a major role in financial trading, healthcare, security, fraud prevention, etc. The immense capabilities of machine learning are still being explored. Every day, new methods of searching for patterns in the huge influx of data are coming up. The machines can detect the patterns, and take necessary actions on their own using Artificial Intelligence, without being explicitly programmed.
The data scientists can make use of AI combined with ML to perform intelligent search in identifying quality content from time-wasting low quality content. Through intelligent search, it is possible to unify your company’s knowledge, and reveal what the customers prefer, and even help other customers with what they need. This helps you to align your technology, people and processes to more customer-centric decisions.
Video is an extremely powerful source of data because you get accurate information on your customer’s preferences. So it is by far, the most powerful resource in shaping your marketing campaigns; to capture not just what is happening in the present through real-time data, but what’s been happening in the past too.
Extracting massive amounts of data by watching the videos is practically an impossible task. However, we also need to remember that this was how the process first started. People used to sit for hours and days in front of the video, log in the content, notice the patterns manually and gauge insights from that. But you can’t do that anymore considering the amount of data that comes in every second.
Here are certain simple, but highly effective ways in which corporate marketers can drive video to bring in more success and more ROI.
The data scientists can make use of AI combined with ML to perform intelligent search in identifying quality content from time-wasting low quality content. Through intelligent search, it is possible to unify your company’s knowledge, and reveal what the customers prefer, and even help other customers with what they need. This helps you to align your technology, people and processes to more customer-centric decisions.
Intelligent search combined with automatic fine-tuning and machine generated predictions is a winning combination that drives results. No more fine-tuning or manual logging in of the content is necessary. But it is also important to remember that relevance is a relative matter. What was relevant to your customers a month ago may not be as relevant the next month, so develop intuitively. This is crucial for companies with large product lines.
For successful media memorability, it is important for the corporates to find the most relevant features and the right model to accompany it. Even though computational understanding of video memorability is still at its developing state, ML plays an important role in understanding the past videos to predict what could happen in the future.
This helps them to come up with a content strategy that relates to the maximum people in the target segment. There are exploratory technologies that help with analyzing billions of videos and images daily, with deep machine learning embedded in them. These technologies learn from new data, so you can add new labels, including facial recognition features.
Machine Learning and Artificial Intelligence help in the automation process of curating the video content accurately. The machines and platforms become more capable in analyzing the right type of content for your viewers as well. Video producers would find it infinitely useful when they have an automatic process of tagging, reading and correctly identifying thousands of hours of content and this helps them develop a series of flashcards, thumbnails, etc. to cater to the different kinds of requirements on platforms like Facebook, YouTube, etc.
There are technologies that would help marketers and video editors automate specific video optimization; meaning they no longer have to optimize the video content for different platforms manually. It will all be be done automatically.
Enterprises aim to provide exemplary service to their customers by having new technologies to seamlessly integrate audio and video. They can watch the buying habits of customers and match the patterns to understand their preferences and choices; right down to their expressions while they wait at the checkout counter to understand their frustrations at waiting, and even lip syncing when they speak with the sales personnel. This process can be quite a tedious and time consuming one and needs heavy resources to be done manually. There are technologies that can improve the process, while at the same providing quality video content.
As we mentioned earlier, the combination of ML with AI aids in video optimization and proper streaming so enterprises can cater to customer requirements in a better way. Large companies like Amazon, Facebook, Spotify, Netflix, etc. rely on both these technologies for their direct interaction with consumers on a daily basis.
When both these technologies are used optimally, they can provide significant solutions that can scale and grow. For example, a customer using Netflix would appreciate it greatly if movie recommendations are given to him based on his watching history.
That way they don’t have to search for the movies they love, you can engage them forever and they would subscribe on a monthly/yearly basis. This would also help with deciding the streaming quality of the videos. By checking the past viewing data, it is possible to analyze the bandwidth usage of the consumer, and based on this information, Netflix cashes regional servers for quicker load time during peak hours.
Another example is the Amazon Transcribe facility that provides automatic speech recognition services where you can easily add a speech to text feature. Through this facility, you can capture certain words in the audio of the transcribed version. This aids in speaker identification and speaker recognition capabilities.
In the field of capturing videos too, you have several valuable innovations in the market. The feature that Amazon has is called Amazon Rekognition, and all you need to do is provide a video or image to the corresponding Recognition API, and it would locate what you need in a highly accurate manner, with very minimal or no errors - people, text, activities, etc.
You can use the same feature to detect any inappropriate content too. Amazon Rekognition is a deep learning service that helps you index and search digital video libraries and is not widely used in a number of industries.
Google Cloud Video Intelligence is an interactive, deep ML service that help in analyzing huge repositories of video content, and churns out useful information that you can use for perfecting your product, understanding customer preferences, etc. Until some time ago, there was only image recognition APIs in the cloud, which means you can identify information from static images only. Now, you can discover and search within videos as well.
You can tag the videos, extract and store them in the Google Cloud storage service to pull out whenever required. This system would help you identify key entities within the videos in an accurate and intuitive manner. There is a transcribe facility for Google Cloud too to help transcribe the video to whichever language you need it, and categorize them for easy retrieval. They have video AI products like AutoMO Video Intelligence and Video Intelligence API to help with video search and indexing.
Companies like Walmart are using deep learning ML to their advantage. They have self-service convenience kiosks for customers who order online within their brick and mortar stores and they use ML to optimize delivery routes for their home delivery services too. They have stepped up their process of checkout process with the use of ML, sensors and computer processing to help with the checkout process. Customers no longer have to wait in line to pay the bill. They can get in the store, grab what they need, and within seconds the products they purchased will appear on a conveyor belt. Walmart calls this technique the Pick-up Tower.Cabot being one of the best US development companies can provide you with ML related queries.
Machine Learning has actually been around for sometime. But it was not an easy concept because deploying and managing the data and databases has been a complicated process. It had required developing and maintaining machine learning algorithms wherein data scientists, developers and database experts had to work around the clock. But combining ML with cloud was a boon, and there was a galloping change in the usage of the technique making it easier and cheaper to have intelligent search.
ML is helping video industry leaders, data mining experts and data scientists who make use of advanced technologies use it for a formidable competitive advantage. If you are already using ML to help reach out to your customers, then you don’t have to worry about customers jumping ship and embracing your competitors’ products/services.
Video streaming has greatly benefited from advanced machine learning. It would not only help you dynamically predict customer preferences, but would also reduce the cost of resources that was initially needed for performing manual optimization. Machine learning algorithms help in providing marketers with content-aware encoding, through exceptionally high quality video, but with reduced bandwidth consumption and raised cost savings. And finally, the collaboration of AI and ML presents a long-term and effective solution to the challenges that marketers and even consumers faced - lip syncing and closed caption text synchronization issues.
Want to know about automating your video marketing with ML? We can assist you!
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