Today’s users are more likely to consume digital content with a few taps on their smartphones and tablets. But what they actually look for while discovering content online through a mobile application is personalized suggestions and relevant information that matches their specific needs and interests. However, the revolution of artificial intelligence (AI) in mobile apps paves the way toward a personalized approach to content discovery.
Businesses can harness the power of AI and machine learning to create state-of-the-art mobile apps that enable users to discover highly personalized and targeted content. In this blog post, we’ll discuss what AI-based content recommendation is, and how integrating it within mobile apps helps deliver tailored experiences that ultimately boost customer engagement and drive increased revenue.
Below is a quick summary of what content discovery is and what limitations are shared with traditional content recommendation systems.
What Is Content Discovery?
Content discovery is the process when a user browses online platforms to explore and interact with an array of digital content. This may range from accessing insightful articles, streaming videos, finding images, learning new material, or any kind of digital content. In the business context, the process of delivering personalized content or recommendations by identifying user’s needs and preferences and aligning the recommendations with their browsing habits and patterns is known as content discovery. With personalized content discovery, users can make quick decisions, as the AI system sorts out the most relevant content among the thousands that best suits their browsing behavior.
Moreover, businesses can utilize AI in mobile apps to learn about their content preferences to deliver highly targeted content that unlocks higher chances of cross-selling and up-selling. Here is a quick rundown of the key benefits of utilizing AI-driven content discovery in mobile apps:
- Enhanced user experience
- Increased user engagement
- Improved customer satisfaction
- Reduced churn rate or bounce rate
- Insights into customer preferences
- Higher conversation rates and ROI
Limitations and Challenges of Traditional Content Discovery
There are some challenges and limitations that come along with the usage of traditional content discovery systems within mobile apps. As a business owner, you must quickly identify and tackle these challenges to deliver targeted content that resonates with your target audience. Below, we’ve compiled a list of the top 5 challenges of traditional content discovery methods that can leave a bad impression on users and hinder their experience with your mobile app:
1. Lack of Personalization
Today’s modern user demands a more personalized content-searching experience through mobile apps. However, traditional content delivery methods in mobile apps leverage generic user profiles and customer data that don't meticulously align with individual user preferences. Users spend hours and hours browsing through your product catalog to get tailored recommendations and offers but can’t find content that matches their browsing preferences due to traditional content discovery. AI recommendation engine emerges here as a game changer, delivering targeted content to users.
2. Limited Understanding of the Context
Contextual information is crucial to delivering highly targeted content to particular audiences based on their geographical locations, references, hobbies, and interests. However, traditional content delivery lacks this benefit as they may struggle to understand the context in which users are interacting, leading to less relevant recommendations or product information. Consequently, utilizing the AI algorithm here is extremely useful as it deeply analyzes the context information for the targeted content discovery process.
3. Overloaded Content
Due to the overloaded and massive amounts of content available online, users feel lost and overwhelmed while browsing through traditional mobile apps. They may not be able to effectively filter out and prioritize relevant content that they are interested in. However, AI can overcome this challenge by implementing content and collaborative content filtering to discover only the most relevant content that matches user needs.
4. Inability to Handle Multimedia Content
Users now prefer consuming digital multimedia content, such as images, videos, or audio rather than text-based content. Unfortunately, traditional content discovery approaches lack multi-media content optimization capabilities that can hinder customer experience, especially for disabled individuals. AI, with advanced image & video recognition, chatbots & voice assistants helps overcome this challenge proficiently.
5. Limited Diversity and Accessibility
Traditional content discovery algorithms prioritize popular or mainstream content over niche-specific content. This means that users couldn’t explore a variety of content sources, as the system lacks diversity in content recommendations often leading to repeated content delivery. For instance, if a user has an interest in a fantasy movie, the algorithm showcases similar movies in the fantasy genre rather than exploring other genres. Furthermore, the algorithm's restricted compatibility with specific platforms, such as social media, web, or mobile can hinder users' ability to explore content across diverse channels.
How Leveraging AI in Mobile Apps Improves Content Discovery and Engagement
Integrating AI and machine learning algorithms into robust mobile applications is of significant importance to combating traditional content discovery and delivering personalized content recommendations and suggestions by catering to specific individuals' unique needs and preferences.
1. Personalized Recommendation Engine
AI-powered recommendations engine gathers customer data to analyze their content consumption patterns, searching habits, past interactions, and other online activities, including social interactions to offer personalized and targeted content. You can fetch useful insights into your customer preferences through real-time data analysis to deliver targeted products and content recommendations that users find similar to their interests. With mobile apps integrated with AI recommendation algorithms, you can better capture your customers' needs and preferences. Netflix, Amazon, and Spotify are the leading examples that leverage AI recommendation engines into their mobile apps to recommend movie shows and streaming content to their users based on their browsing habits.
2. Natural Language Processing (NLP)
AI can interpret human language to understand and analyze their sentiments and emotions to make the content delivery experience more personalized. Analyzing users' feedback and responses using NLP also plays an important role in identifying whether their interactions with your delivered content are positive or negative. Based on this information, the algorithm enables you to offer personalized offerings and recommendations that resonate with your target audience’s emotions and preferences. In addition, NLP helps categorize, tag, and summarize content, making it easier for users to find relevant information. Voice-based assistants are examples of NLP that enable users to interact with chatbots in real-time and receive tailored recommendations and relatable content based on individual interests, ongoing conversations, and feedback.
3. Content Classification and Tagging
The process of categorizing content into relevant topics or themes and tagging with descriptive labels is known as content classification. This technology in mobile apps effectively organizes and retrieves information powered by NLP and machine learning. Users can easily navigate through vast amounts of content available within your mobile app and discover items that match their interests. Netflix, again comes here as an example of categorizing and tagging content based on genres, themes, and user preferences, enabling personalized content recommendations for each viewer. Overall, content classification and tagging enhance search accuracy, facilitate personalized recommendations, and streamline content exploration through mobile apps.
4. Contextual Awareness
Targeted content recommendations based on your user's current context and preferences are the ideal approach to personalized content discovery. The app considers users' current location, time of day, device type, and browsing history to tailor content suggestions that align with your user's immediate needs and preferences. Contextual awareness seamlessly adapts to changing circumstances and provides users with highly valuable content recommendations that resonate with their current context. By implementing this dynamic approach to content discovery, you can enhance user engagement by delivering personalized and contextually appropriate content.
5. Customized Content Feed
Think about this as your Facebook and Instagram page that refreshes or updates your content feed every time you log into the app. Each user feed and post differs based on their preferences and interests, as these social sites leverage AI and machine learning to deliver targeted content. Relevant articles, videos, and other media are directly fetched to user's feeds for improved content discovery. These feeds are continuously updated based on the user's interactions, ensuring that the content remains fresh and engaging. Further, you can also customize content feeds within your mobile app to empower users to discover new content that seamlessly aligns with their tastes while filtering out irrelevant or undesirable content.
6. Content Filtering
AI sorts out the most relevant content that matches your users’ browsing activities and preferences. It helps refine search engine results and focus on offering more most relevant and desirable content to users. Additionally, by integrating AI content filtering algorithms within your mobile apps, you can sift through various criteria such as keywords, categories, or user preferences to present users with tailored content selections that match their specific interests. Content filtering enhances the efficiency and effectiveness of the discovery process by eliminating irrelevant or low-quality content. Moreover, it enables users to quickly find and engage with content that meets their needs and preferences.
7. Visual Search Recommendations
Visual searches are among the most common content discovery processes. Mobile apps for visual search recommendations allow users to explore content based on images rather than text. As a result, you can leverage computer vision and deep learning algorithms within your mobile app development to help identify objects, scenes, or patterns and then recommend visually similar content to your users. This approach enhances user engagement by facilitating intuitive and efficient exploration of multimedia content, such as photos, videos, and product listings. For example, Pinterest employed AI-powered visual search recommendations to help users upload product images and immerse in exploring products similar to the uploaded image. This feature integration enables users to effortlessly explore diverse content and discover new ideas.
Conclusion
AI is revolutionizing the content discovery process by delivering targeted and personalized recommendations that help customers feel valued and appreciated. The robust AI and machine learning algorithms employed within the mobile apps enable businesses to capture their customers' preferences and needs through data analysis and constant feedback & responses. Moreover, AI predictive forecasting also lets you identify behavioral patterns of customers to forecast future trends and predict what types of content customers are likely to consume next. As you integrate content discovery trends within the development of your business mobile apps, your customers can anticipate a more personalized and tailored content discovery experience.
