How Do Modern Product Recommendation Engines Match Products and Services to Customer Needs
|Content-based Filtering Model||Collaborative Filtering Model||Hybrid Model|
|By using the content-based filtering model, banks can leverage the deep insights gained on a particular user’s current preferences to recommend similar products/services in the future. For example, if Customer A has been investing in low-risk, long-term savings bonds, the content-based filtering system recommends similar investment products, such as other bonds or fixed-term deposits with similar low-risk profiles and long-term maturities.||Using a collaborative filtering model, banks can identify patterns in user-item interactions within comparable customer groups to make precise and personalized product recommendations, enhancing the overall effectiveness of targeted offerings. For example, if the credit card transaction data of Customer B and Customer C reveals travel-related expenses and these customers have opted for a travel rewards credit card, then the system automatically offers a travel rewards credit card to Customer D who has a spending pattern that is similar to Customer B and C.||For banking and financial services (BFS) organizations that typically have millions of customers using only a few of their products or services, the hybrid model works quite well. Using the flexible hybrid model which is a combination of content-based and collaborative filtering models, banks can recommend more relevant products and services to their customers.|
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Anand Subramaniam is the Chief Solutions Officer, leading Data Analytics & AI service line at KANINI. He is passionate about data science and has championed data analytics practice across start-ups to enterprises in various verticals. As a thought leader, start-up mentor, and data architect, Anand brings over two decades of techno-functional leadership in envisaging, planning, and building high-performance, state-of-the-art technology teams.