Abstract: AI infusion into a company's Customer Relationship Management (CRM) system revolutionizes the way businesses interact with their customers, creating a dynamic and personalized experience.
By harnessing the power of machine learning and AI, companies can unlock advanced capabilities and gain valuable insights that streamline sales processes and enhance sellers’ performance. This infusion eliminates the laborious task of manual information analysis across multiple disparate data sources, empowering sellers to dedicate their time and expertise to building meaningful customer relationships. In architecting AI-based solutions, it is of high importance to understand the users' daily workflow and challenges, and not just surfacing the insights but surfacing the right information at the right place to simplify users’ workflow.
One powerful AI-infused feature in CRM is personalized recommendations. By leveraging AI algorithms and customer data, sellers can receive tailored suggestions and insights to improve their customer strategy planning. This can be fully embedded in sellers’ workflow, and they can create new opportunities and modify existing ones based on these recommendations. These insights enable sellers to offer personalized product recommendations, deliver targeted offers, and provide a seamless strategy plan with their customers.
In this case study, we will delve into the architecture and design components that enable personalized recommendations to be integrated into our CRM platform with considerations around data flow, backend components, and usage tracking. We will also explore how AI algorithms analyze customer preferences, purchase history, and behavior to generate accurate recommendations and discuss how new AI capabilities can revolutionize real-time guidance to sellers.
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