How ML Opens Gates to Personalization

Machine learning advancements are facilitating brands to provide customized content, driving viewer-satisfaction.

FREMONT, CA: Personalization is obtaining ground at a rapid pace. The streamers and other M&E brands are trying to understand individual customer tastes and choices to craft customized content contributions. Still, brands need further knowledge of their consumers' preferences to offer hyper-personalized content. Machine learning (ML), with the capability to connect hidden consumer behavioral patterns to real-time personalization approaches, proves to hold enormous potential in the M&E industry. With this, ML appears as the key technology for the brands that are eyeing to generate hyper-personalized content. The technology will provide more in-depth insights into customer behavior, which significantly affects the content that will be delivered to them.

Earlier, consumers didn't have many choices in terms of deciding the content they wanted to see. However, things turned rapidly as the brands understood the potential of personalization. The M&E sector has been among the top few to consolidate the feature of customization into its toolkit. Personalization strives to serve the audience with customized content that meets their uniqueness of choices. It's a reasonable approach to influence consumers and makes them feel treasured. ML enables the brands to approach their consumers with better-customized content. Yet, according to the specialists, the accomplishments in personalization are far from the potential that ML can give.

Smart content is another strategy towards personalization, which is assumed to gain popularity in the near future. Smart content changes according to the past behavior or curiosity of the viewer. Automated systems utilize state-of-the-art NLP technologies to generate customized, natural, and creative textual content at a mass scale. Personalization will grow significantly by integrating ML and content atomization. The above initiative necessitates breaking down a generic content into smaller pieces of information modules. With this, the content will become remarkably specific, depending upon the audience's interest and knowledge of the subject. For example, a person who has insufficient knowledge of finance will receive a bounded angle of finance news than the one who knows the domain better.

As stated earlier, the atomized content strategy will facilitate the brands to provide automated storytelling. The modulated versions of special content could be updated, replaced, modified, and discarded based on varying demands of the audience. Moreover, the content modules could be reused in the near future. ML algorithm will help in rightly distributing these modulated pieces to the individual consumer's choices. Modulated smart content will have various configurations and representations for multiple users, user interfaces, devices, languages, and environments. It will also be feasible to access the same piece of content through a voice user interface or using AR applications.

Currently, the ML algorithms are giving recommendations based on the user and content-related data. Future improvements in ML technologies and the development of smart content is expected to offer a fully hyper-personalized experience to the consumers. According to the modern trend, customers will expect content that resonates with their tastes as the least requirement while opting for a specific brand.

The progressions in ML will further guide the variations in the entertainment area. ML-driven solutions are being seen as the torchbearer among the improvements in the field of customization. Hyper personalized content will facilitate the entertainment brands to lure the public while empowering their lives. By making a stride in this way today, M&E players can walk towards success in the future. 

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