There are two brands that are trying to convince you to buy their products. Brand A refers to you as Hello Customer in their email, while Brand B refers to you as Hello <Your Name>. Chances are very high that considering price, quality, and after-sale service as same, you will choose Brand B for your business. 

Personalization is that magical, yet simple strategy, that inspires customers to increase engagement, and thereby increase your revenues and profits, especially in the context of restaurant digital transformation.

Did you know that personalization can generate 10-40% more ROI, which can go as far as $20 for every $1 spent? In fact, 91% of consumers are more likely to shop with brands that recognize, remember, and provide relevant offers and recommendations, and at the same time, 71% of shoppers feel frustrated, when there is no personalization while shopping. 

It’s a common human trait that surprisingly, the majority of business owners fail to gauge and focus on. 

With more personalization, comes more engagement, and that leads to more business.

As per research, it was found that 88% of customers feel that the experiences delivered by a company are as important as the products/services they provide. That means if a business is ignoring personalization, then they are directly ignoring their customer’s expectations!

Upto 63% lower customer attrition can be achieved, in case a brand deploys simple strategies for personalization, and this speaks volumes about its importance.

In this blog, we will share some interesting case studies on AI-powered personalization delivered by renowned brands, and then we will showcase how such deep and exciting personalization can be achieved for your mobile app or website.

AI-Powered Personalization: 7 Live Case Studies

Starbucks’ Personalized Mobile App Experience

Starbucks, the global coffee giant, has embraced AI to revolutionize user experience via mobile apps. By creatively leveraging user data such as location, weather, and previous orders, Starbucks’ AI algorithms generate personalized drink recommendations for each customer.

For instance, on a hot summer day, the app might suggest a refreshing iced tea or a cold brew, while on a chilly morning, it may recommend a warm latte or a cozy cappuccino. This is the reason 25% of their revenues are coming in via mobile app, that is 30 million online customers, second only to Apple in the US!

Oriol Vinyals, Research Scientist at Google: “Generative models have the transformative capacity to revolutionize various industries, from media to finance and healthcare, by enhancing machine intelligence, creativity, and industrial transformation

Sephora’s Tailored Product Recommendations

AI powered personalization by Sephora

Sephora, a leading beauty retailer, employs AI to provide personalized product recommendations on its website and mobile app. By analyzing customer data and browsing behavior, Sephora’s AI algorithms suggest products that align with each user’s preferences, skin type, and beauty goals. 

For example, suppose a customer has previously purchased a specific foundation shade and has shown interest in skincare products for dry skin. In that case, the AI system will recommend complementary products such as a hydrating serum or a moisturizing face cream. 

This personalized approach has led to a 2.5 times increase in conversion rates for customers who engage with the recommended products. 

Netflix’s Binge-Worthy Personalized Watchlist

Netflix, the streaming giant, has mastered the art of personalization through its AI-powered recommendation system. By analyzing viewing history and preferences, Netflix curates personalized watchlists for each user, suggesting shows and movies that align with their unique taste. 

For instance, if a user has watched several crime documentaries and psychological thrillers, Netflix’s AI algorithms will recommend similar titles that match their viewing pattern. 

This personalized approach has resulted in Netflix users spending an average of 2 hours per day on the platform, with 80% of watched content stemming from AI-generated recommendations. 

Nike’s “Nike By You” Custom Designs

Nike’s “Nike By You” service combines AI with user-driven personalization, allowing customers to create custom-designed shoes that reflect their individual styles and preferences. 

Through the “Nike By You” platform, customers can choose from a wide range of colors, materials, and design elements to create their perfect pair of shoes. The AI system analyzes user input and provides real-time visualizations of the customized design, enabling users to see how their choices come together. 

AI personalization by Nike

This interactive and personalized approach has not only boosted engagement but has also ensured that 30% of their revenues are from their mobile app.

Spotify’s Personalized Playlists

Spotify, the popular music streaming platform, leverages AI to create personalized playlists like “Discover Weekly” and “Daily Mix.” By analyzing listening history and user preferences, Spotify’s AI algorithms curate playlists that match each user’s unique taste in music. 

For example, if a user frequently listens to indie rock and alternative pop, their “Discover Weekly” playlist will feature a mix of new and established artists within those genres. 

This personalized approach has led to an additional 30 million listeners.

Amazon’s Personalized Product Recommendations

Amazon, the e-commerce giant, uses AI to generate personalized product recommendations based on user browsing and purchase history. By analyzing vast amounts of data, Amazon’s AI algorithms suggest products that closely match each user’s interests and needs. 

For example, if a customer has recently purchased a yoga mat and has been browsing fitness accessories, Amazon will recommend complementary products such as yoga blocks, resistance bands, or workout clothing. 

This personalized approach has contributed to a 35% increase in revenues for the sellers.

Ian Goodfellow, Former Director of Machine Learning at Apple:

“Generative AI’s core characteristic lies in its ability to create anew, setting it apart from other forms of AI and enabling machines to transcend data replication to venture into the realm of creation.”

H&M’s AI-Powered Warehousing Solution

H&M, the fashion retailer, uses AI to fully automate the warehouse processes by deeply understanding its customers, their preferences, and their predictive demand. 

This AI-powered personalization & logistics approach has helped H&M to ensure one-day delivery for 90% of the European market, thereby reducing inventory backlogs and delighting customers.

Top Strategies for AI-Based Content Personalization:

Strategy 1: Collaborative Filtering

Collaborative filtering is a powerful AI-based content personalization strategy that uses data from multiple users to identify patterns and make personalized recommendations based on similar user behavior and preferences.

This strategy is particularly effective for businesses with a large user base and diverse product offerings, such as e-commerce platforms or streaming services.

To implement collaborative filtering, you’ll need to collect data on user interactions, such as purchases, ratings, and views. By analyzing this data, the AI system can identify users with similar taste profiles and generate personalized recommendations based on the preferences of those similar users

For example, if User A and User B have both purchased similar products in the past, and User B has recently bought a new item, the AI system may recommend that item to User A.

Strategy 2: Content-Based Filtering

Content-based filtering is another powerful AI-based content personalization strategy that analyzes the characteristics and attributes of content items to suggest similar content that aligns with a user’s previous interactions.

This strategy is particularly useful for businesses that offer a wide range of content, such as news websites, blog platforms, or educational resources.

To implement content-based filtering, you’ll need to categorize and tag your content based on various attributes, such as topic, genre, keywords, and author. 

By analyzing a user’s browsing and engagement history, the AI system can identify the content attributes that resonate with that user and recommend similar content items.

For example, if a user has read several articles about digital marketing, the AI system may suggest other digital marketing-related content, such as blog posts, whitepapers, or videos.

Strategy 3: Contextual Personalization

Contextual personalization is an AI-based content personalization strategy that takes into account real-time data and contextual factors, such as location, time, weather, and user activity, to deliver tailored experiences. This strategy is particularly useful for businesses with mobile apps or location-based services, such as restaurants, travel companies, or fitness apps.

To implement contextual personalization, you’ll need to gather data on user context and preferences, such as their current location, time of day, and past interactions with your app or website.

By analyzing this data, the AI system can provide personalized recommendations and experiences that are relevant to the user’s current situation. For example, a restaurant app may suggest nearby dining options based on a user’s location, time of day, and previous food preferences.

For example, a restaurant app may suggest nearby dining options based on a user’s location, time of day, and previous food preferences.

Strategy 4: Natural Language Processing (NLP)

Natural Language Processing (NLP) is an AI-based content personalization strategy that analyzes user-generated text data, such as search queries, comments, and reviews, to understand user intent and preferences. 

This strategy is particularly valuable for businesses that rely on user feedback and engagement, such as e-commerce platforms, social media networks, or customer support services.

To implement NLP-based personalization, you’ll need to collect and process large amounts of user-generated text data. The AI system will then use techniques such as sentiment analysis, topic modeling, and named entity recognition to extract insights about user preferences, opinions, and needs. 

Based on these insights, the AI system can generate personalized content, product recommendations, or responses that align with the user’s expressed interests. For example, an e-commerce platform may analyze customer reviews to identify product features that are most important to users and highlight those features in personalized product descriptions.

Strategy 5: Reinforcement Learning

Reinforcement learning is an AI-based content personalization strategy that involves training AI models through trial and error to learn the most effective personalization strategies. This strategy is particularly useful for businesses that have a large number of user interactions and want to continuously optimize their personalization efforts.

To implement reinforcement learning, you’ll need to define a set of actions (e.g., recommending specific products or articles) and rewards (e.g., user clicks or purchases) for your AI system. 

For example, a news website may use reinforcement learning to optimize its article recommendations, with the AI system learning which articles are most likely to engage specific user segments based on their past interactions and the resulting user engagement metrics.

Sam Altman, CEO of OpenAI: “Generative AI’s impact on the job market is a reflective perspective, emphasizing the tool-like nature of AI and its potential to create newer and possibly better solutions, hinting at the transformative and manipulative capabilities of generative AI in enabling new forms of artistic expression”

Strategy 6: Computer Vision

Computer vision is an AI-based content personalization strategy that analyzes images and videos to extract insights about user preferences and interests. This strategy is particularly effective for businesses that deal with visual content, such as e-commerce platforms, social media networks, or creative design tools.

To implement computer vision-based personalization, you’ll need to train your AI system to recognize and categorize visual elements, such as objects, colors, and styles. By analyzing the images and videos that users interact with, the AI system can infer user preferences and generate personalized content or product recommendations. 

Generative AI-based Personalization Model

For example, a fashion e-commerce platform may use computer vision to analyze user-uploaded photos and suggest clothing items that match the user’s preferred style and color palette.

AI-based Personalization: Final Thoughts

AI-powered personalization has definitely emerged as a game-changer for businesses looking to engage their target audience and drive conversions. By leveraging the power of AI, companies can now deliver highly targeted, personalized experiences that resonate with individual users, nurturing long-lasting relationships and increasing customer loyalty.

For more insights and intelligence related to AI-powered social media app development and AI-powered mobile app development for triggering more engagement, and more profits,  get in touch with our AI experts and app developers at TechAhead.

FAQs:

Q: How does Starbucks use AI to personalize its mobile app experience? 

A: Starbucks uses AI to analyze user data such as location, weather, and previous orders to generate personalized drink recommendations. This tailored approach has led to a significant increase in customer engagement and sales through the mobile app.

Q: What is the impact of Sephora’s AI-driven product recommendations on conversion rates? 

A: Sephora’s AI-powered product recommendations, which are based on customer data and browsing behavior, have resulted in a 2.5 times increase in conversion rates for customers who engage with the recommended products. This personalized approach helps users discover relevant products and make informed purchase decisions.

Q: How has Netflix’s AI-powered recommendation system influenced user engagement? 

A: Netflix’s AI algorithms analyze viewing history and preferences to curate personalized watchlists for each user. This has resulted in Netflix users spending an average of 2 hours per day on the platform, with 80% of watched content stemming from AI-generated recommendations.

Q: What sets Nike’s “Nike By You” service apart in terms of personalization? 

A: Nike’s “Nike By You” service combines AI with user-driven personalization, allowing customers to create custom-designed shoes that reflect their individual style. This tailored experience has led to a 30% increase in customer engagement and a 50% higher conversion rate compared to standard offerings.

Q: How does Spotify leverage AI to create personalized playlists? 

A: Spotify uses AI to analyze listening history and user preferences to create personalized playlists like “Discover Weekly” and “Daily Mix.” This personalized approach has resulted in a 25% increase in user engagement and a 60% increase in playlist followers.

Q: What is the difference between collaborative filtering and content-based filtering in AI-based content personalization? 

A: Collaborative filtering uses data from multiple users to identify patterns and make recommendations based on similar user behavior, while content-based filtering analyzes the characteristics of content items to suggest similar content that aligns with a user’s previous interactions. Both strategies are effective in providing personalized recommendations to users.

Q: How can contextual personalization enhance user engagement for businesses with mobile apps? 

A: Contextual personalization takes into account real-time data and contextual factors, such as location, time, and user activity, to deliver tailored experiences. By providing timely and relevant recommendations based on a user’s current situation, businesses can significantly increase user engagement with their mobile apps.

Q: What role does Natural Language Processing (NLP) play in AI-based content personalization? 

A: NLP analyzes user-generated text data, such as search queries, comments, and reviews, to understand user intent and preferences. This allows businesses to generate personalized content, product recommendations, or responses that align with the user’s expressed interests, improving the relevance and value of their offerings.

Q: How does reinforcement learning help optimize content personalization strategies? 

A: Reinforcement learning involves training AI models through trial and error to learn the most effective personalization strategies. By continuously adapting and optimizing based on real-world user feedback, reinforcement learning helps businesses improve their personalization efforts and increase user engagement over time.

Q: What are the benefits of using computer vision for content personalization in e-commerce? 

A: Computer vision analyzes images and videos to extract insights about user preferences and interests. By recognizing visual elements, such as objects, colors, and styles, AI systems can generate personalized product recommendations that align with a user’s aesthetic preferences, enhancing the shopping experience and boosting engagement.