Case Study

Recommendation System


Key details

Recommendation System

  • Challenge

    The finance company aimed to enhance its customer experience by offering personalized product recommendations based on user behavior. They wanted to leverage their users' buying patterns to identify complementary financial products that, when combined with their current purchases, formed a more comprehensive and tailored package. Additionally, they sought to recommend products to new users based on their similarity to existing users, providing a seamless onboarding experience.

  • Approach

    Intellekt AI developed a sophisticated recommendation system that utilized advanced machine learning algorithms. The system analyzed the buying behavior and transaction history of users to identify patterns and correlations among different financial products. It then leveraged this information to generate personalized recommendations that offered enhanced value to users.

    For existing users, the system identified the products they had already purchased and recommended additional products that complemented their existing holdings. By understanding the user's financial goals, risk appetite, and historical preferences, the system tailored recommendations to create comprehensive and personalized packages.

    For new users, the system employed collaborative filtering techniques to identify similar user profiles and recommend products based on the preferences of similar existing users. This approach ensured that new users received relevant and targeted recommendations from the start.

  • Goal

    The goal was to develop a recommendation system that leveraged user behavior patterns to provide personalized financial product recommendations. The aim was to enhance customer experience, drive cross-selling and upselling opportunities, and ultimately increase the finance company's yearly revenue.

  • Outcome

    Intellekt AI's recommendation system yielded remarkable results for the finance company. By providing personalized recommendations based on user behavior patterns, the system significantly improved customer satisfaction and engagement. Users were presented with tailored product packages that aligned with their financial needs and preferences, resulting in a higher likelihood of purchase.

    The system's ability to identify complementary products and suggest them to existing users led to increased cross-selling and upselling opportunities. Users were more likely to explore and purchase additional products, resulting in a significant boost in the company's yearly revenue. The finance company experienced a remarkable 50% increase in yearly revenue, highlighting the effectiveness of the recommendation system in driving business growth.

    Furthermore, the system's ability to recommend products to new users based on similarities to existing users improved the onboarding experience and helped new users discover relevant products from the outset. This personalized approach fostered trust, engagement, and long-term customer loyalty.

Recommendation systems


Explore how Intellekt AI developed a powerful recommendation system for a finance company, leveraging user behavior patterns to recommend complementary financial products and drive revenue growth.

The system analyzed user buying information, identified product combinations, and provided personalized recommendations to both existing and new users, resulting in a significant increase in yearly revenue.


Technologies used


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