Delivering personalized content through machine learning
Content Personalization is a revered keyword in today’s digital universe. Today, content is the one true king and technology serves as the impeccable tool that supports good content. All businesses across different industry verticals have some form of virtual presence like websites, web apps, or social media handles, or others.
In all honesty, content has long had a presence almost everywhere. Ranging from manuals, books, magazines, and newsletters to websites, social media pages, newspapers, news sites, and the like; it exists everywhere.
The digital world feeds on content and the actions of people. These actions are performed both deliberately and subconsciously, driven by the immense power of content in their daily life. Content personalization plays an important role over here.
Customers love to see content that caters to their likes, dislikes, preferences, and vice versa. Personalization is indeed quite predictive once firms understand their target audience, and hence marketing mechanisms can be devised accordingly.
Mobile app developers reveal that machine learning has begun playing a much-needed role here. If businesses have adopted digital & technological capabilities using Machine Learning, they are above others and are on the digital highway.
Machine learning is helping marketers scale their content marketing strategies monumentally. It helps them know the pain points of their users, understand what ticks them, and then tweak their content accordingly to resonate with them.
Optimizing content delivery
Optimizing means locating essential points in a customer’s journey and adding a personal touch to them. Context plays the role of the trigger to the need of creating and optimizing a specific kind of content between customers.
Machine learning algorithms help deliver the right content at the right time for numerous individual website visitors based on their data about what their past activities regarding web and content search have been about.
Along with Natural Language Processing, Machine Learning scans content and then examines it deeply to understand its core meaning along with the context and then ends up indexing, creating a custom library for specific usage. This facilitates the automated delivery of personalized content across the web, email, desktop, or mobile.
Boosting the efficiency of content
What is the topmost priority in anyone’s wish list whilst sending email to the target audience? Content engagement and the way to increase content engagement metrics from the target audience is the email content. This content should thrive via all means and social media channels have now emerged as popular modes of the user interface.
Email marketers are now relying on the capabilities of Machine learning for the personalization and relevance of content. The former uses machine learning in segmenting markets, creating market-timings, and handling the copywriting aspect of emails.
The Machine Learning technology boosts the overall effectiveness of content efficiency in email marketing. This helps firms identify suitable email marketing tools (based on ML) for the business. As per market surveys, Machine learning capabilities boost the efficiency of tons of emails sent and received daily.
As per a recent report, almost 306.5 billion emails were sent and received last year. This figure is set to rise to 361.6 billion emails daily by 2024.
Creating an individualized content experience
Active content users are those who give more time to content and thus have a higher rate of engagement on a particular landing page. Traditional website metrics like the number of page views, the number of sessions aren’t sufficient for making an individualized experience.
Deep content analytics powered by algorithms of machine learning goes beyond traditional algorithms. They focus on the metrics that provide real-time insights on content engagement, based on the exact moment users engaged with that content on a real-time basis.
This way, capabilities of Artificial intelligence and machine learning power up the personalized tools which allow content to be individualized. These tools use insights for cross-channel content distribution towards users based on their behaviors, preferences, interests, and tastes.
Now that organizations and app development companies have the data, what next should they do?
Machine learning’s unique features and Advanced Algorithms ensure content personalization for users who visit different kinds of social media platforms and websites.
It has Predictive Content Personalization Engines collaborate with the Data Management Platforms and together they carry out activities such as data sync, data merger, and data segmentation. Consequently, we can deliver user-specific content promotions, messaging recommendations, and the like, thanks to machine learning.
Which apps and firms are top examples of delivering personalized content via machine learning?
Machine Learning-based predictive content personalization goes along really well in the eCommerce and entertainment industries. With other industries, it has recently established a synergy that is going quite well. Here are some prominent examples of apps and firms delivering personalized content via machine learning:
- Amazon leverages the monumental potential of email recommendations. According to Amazon, this is much more efficient as compared to on-site personalization.
- Netflix uses dynamic page metrics and follows recommendations that have been generated by ML. These metrics track and store the tastes and preferences of visitors.
- Spotify leverages the power of Machine learning-supported personalized playlists, including Discover Weekly, Release Radar, and Spotify Radio. All of these emphasize the content personalization aspect.
What do mobile app development firms have to do with this?
Mobile app development firms are not just developers, they are also marketers of their apps and their firm. They also track mobile app usage to see where the app broke down, which part of the app interested them the most, what should be the app’s layout, what content should it have and the like.
The app designers and developers work with quality assurance and marketing teams to ensure that the app is bug-free and offers the best functionality and optimal performance.
Moreover, predictive content personalization is backed and powered by Machine Learning algorithms, which enables firms to provide each user, visitor, or prospective customer a unique and worthwhile experience.
These high-quality recommendations are based on real-time personalization and are present at each touchpoint along a customer’s journey. Machine learning’s security algorithms ensure infallible Data privacy and security.