Upsell and re-engage customers with “Products you may like”

Use Modern AI for recommendations

Show similar products based on customer’s browsing or purchasing history, even for new products where no data exists yet.

What is a recommendation system?

A recommendation system is a technique that provides personalized recommendations to users based on their preferences and behaviors.

What are use cases for using AI for recommendations?

AI-driven recommendation systems have revolutionized various industries by offering personalized content and product suggestions. Here are some popular use cases where AI is utilized for recommendations:

  1. E-Commerce Platforms: Providing personalized product recommendations based on user's browsing history, past purchases, and preferences, thus increasing sales and improving customer experience.
  2. Streaming Services: Recommending movies, TV shows, music, or podcasts tailored to users' tastes and viewing/listening habits, enhancing engagement and satisfaction.
  3. Online News and Media Outlets: Suggesting articles, blogs, or news stories that align with the reader's interests, encouraging longer site visits and more content consumption.
  4. Travel and Hospitality: Offering customized travel packages, hotel suggestions, and destination recommendations based on individual preferences, search history, and previous bookings.
  5. Health and Fitness Apps: Recommending personalized workout routines, dietary plans, or health products based on users' fitness goals, body metrics, and preferences.
  6. Social Media Platforms: Suggesting new friends, pages to follow, or content to engage with, based on the user's network, interactions, and interests, making the platform more engaging.
  7. Financial Services: Recommending investment opportunities, insurance plans, or banking products tailored to a customer's financial profile and goals.
  8. Education Platforms: Suggesting courses, study materials, or educational videos that align with a student's current study path, interests, and performance.
  9. Job Portals: Matching candidates with job openings or suggesting job opportunities that align with the individual's skills, experience, and career aspirations.
  10. Retail Grocery Stores: Recommending recipes, meal plans, or products based on past purchases, dietary restrictions, or preferences, encouraging repeat purchases.
  11. Dating Apps: Suggesting potential matches based on compatibility, shared interests, and preferences, increasing the chances of successful connections.
  12. Real Estate Platforms: Recommending properties, neighborhoods, or agents based on user's preferences, budget, and search behavior, enhancing the buying or renting experience.
  13. Gaming Platforms: Suggesting new games, in-game purchases, or challenges that align with a player’s gaming habits and preferences, encouraging continued engagement.
  14. Automotive Industry: Recommending vehicles, accessories, or service plans based on a customer's needs, preferences, and past interactions with the brand.

These use cases illustrate the breadth and power of AI-driven recommendations in enhancing user engagement, personalizing experiences, and often driving significant business results.

What are the benefits of using Modern AI for a recommendation engine?

The benefits of a modern AI-powered recommendation system include:

  1. Improved accuracy: AI can analyze large amounts of data to provide more accurate and relevant recommendations to users.
  2. Personalization: quickly analyze user behavior and preferences to provide personalized recommendations that better match each user's individual tastes and interests.
  3. Increased efficiency: process large amounts of data quickly, allowing recommendation systems to provide real-time recommendations to users.
  4. Scalability: easily scale to handle large volumes of data and users.
  5. Cost savings: automate the recommendation process, reducing the need for human intervention and potentially reducing costs.

Who can benefit?

Companies that can benefit from personalized recommendations based on user behavior and preferences.
  • eCommerce: suggest products based on a user's past purchases or browsing history.
  • Media and entertainment: suggest movies, TV shows, music, or other content based on a user's viewing or listening history.
  • Healthcare: suggest personalized treatment options based on patient data and medical history.
  • Finance: suggest investment options based on a user's risk tolerance and financial goals.
  • Travel: suggest personalized travel itineraries based on a user's preferences and travel history.
Why Graft

Go from idea to production in an afternoon with a Modern AI Platform.

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Faster Time-to-Value - Speed up your AI app deployment and provide faster time-to-market solutions.
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Lower Total Cost of Ownership - Use a single platform for your full AI production lifecycle, from data ingestion and labeling to optimization and monitoring.
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Higher Quality Outcomes - use the most advanced AI capabilities to achieve better outcomes, increased efficiency, and improved ROI.
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100% Data Coverage - your data is no longer a barrier, but a competitive advantage. Graft supports text, images, audio, and video.
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