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Attryb: Artificial Intelligence–Driven Website Personalization for Online Sellers
內容大綱
Amid rising online content, modern consumers want a personalized online shopping experience and to be presented with products, services, and deals that are relevant and specific to them. Anil Bains launched Attryb to create a hyper-personalization stack to deliver a meaningful experience to each website user. Using artificial intelligence, machine learning, and statistical models, Attryb’s predictive intelligence model could assess thousands of customer signals to create meaningful segments that increased acquisition, engagement, and sales for online sellers. However, small and medium companies establishing direct-to-consumer websites often lacked strong technological capabilities and were preoccupied with starting their businesses. How could Attryb convince these prospective clients about the economic benefits of its solution and communicate the value without being overly technical?
學習目標
The case illustrates how artificial intelligence and machine learning algorithms can be used to create value for organizations. It can be used in introductory marketing courses to showcase how marketers can use data and analytics to create business and economic value, as well as in courses on marketing analytics, digital marketing, and B2B marketing. After working through the case and assignment questions, students will be able to do the following:<ul><li>Explore the types of data that can be collected from different types of e-commerce website users.</li><li>Identify the technical and privacy challenges associated with collecting these types of data.</li><li>Gain insights into how data-driven segmentation creates economic value for an organization.</li><li>Understand how to build a business case and value calculator to justify the client’s acquisition of a new analytical solution.</li></ul>