Optimizing editors’ time by automating content creation and publishing processes across different channels is one of the milestones of the digital age.
Repetitive activities such as social posting, the selection of trending topics, the choice of the best content to publish and tags to use, they all can be automated with Artificial Intelligence.
Social Automation systems such as SocialBeat are powered by automatic learning algorithms that extract real-time data and analysis from social networks, doing the work of task managers on these channels, with remarkable results.
It is a continuous cycle of improvement: technology learns from data, and in addition to optimizing the processes of production and delivery of content, it ensures more guarantees of hitting the target because of the higher statistical probability it identifies. An example of success story on the automated use of algorithms is Netflix, whose user base has risen to 125 million worldwide.
Netflix's artwork personalization
Netflix's custom recommendation system relies on artwork. The perfect image, the most relevant to the tastes of every single spectator, can grab attention way more effectively than titles alone would.
Of a single film, a wide range of themes can be presented visually: a frame portraying Keanu Reeves will have greater influence on a person who has watched all his films, while a love scene will be more click-baiting for a fan of romantic comedies.
Before, through multi-armed bandits algorithms, Netflix was able to measure reward – the viewer’s positive commitment - from the action they performed on the homepage. However, unlike typical recommendations with multiple choices, Netflix could select a single artwork to represent each title. It also had to consider how much each of them influenced the others, that is, the final choice of one rather than the other.
Before correctly assimilating customization strategies, the system needed to collect a lot of data to identify the best artwork for each user. Instead of waiting to collect a full set of data, train a model or perform A/B tests, Netflix decided to use contextual bandits, which can be summarized as context - action - reward model.
Starting from the analysis of context, the algorithms choose the option they consider best for the user and present it to them, evaluating the reward. This controlled randomization of the learned model’s predictions has the advantage of being impartial with the context of each user, and of being updated continuously through feedback cycles.
The importance of semantic analysis
A recurring problem for a media company that has to manage huge amounts of content on a daily basis is availability. Petabytes of assets, whose production required a cost in terms of money, time and energy, risk of lying unused if they can’t be retrieved when needed.
For this reason, understanding them semantically is an essential step: however, the assignment of tags - or labels that identify their main topics - is a long and laborious process when done manually. A thorough knowledge of the topics is required, and the same topic may be named differently by the various workers involved in the operations, with an uneven result.
An engine powered by AI algorithms is certainly more effective: thanks to Speech-to-Text, Image Recognition and Text Summarization functions, the assignment of tags to videos, images and documents becomes automatic as the technology distills the useful information.
How does it work? Machine Learning starts from a database: it looks for a pattern and applies it, finding relationships. Deep Learning is a step further: once its neural networks have been trained with a huge database, the margin of error will be almost zero.
Speaking of social media, we can affirm that the volume of published content is a critical factor in increasing the importance and value of a page. It is true that few posts get more interactions, but more posts, even if they generate less engagement, eventually achieve better results in terms of reach and followers.
Social Automation systems work in this direction. While in the past 3 people were needed to manage social networks, now the same tasks can be entrusted to a machine, reducing effort by 70%.
SocialBeat's customization systems take into account several contextual factors. To make an example, Mirko is interested in automotive, he is traveling through the Alps and snow is expected. He will be showed an image that depicts a car in the mountains.
Algorithms are also very useful to find microinfluencers: in this way, trade marketing activities can be done with them to expand the brand’s contact list, thanks to their resonance.