AI Driven Personalization

Today’s User Profile

  • Explicit User Profiles are created when the user answers different questionnaires and provides ratings. This kind of gathered information is usually of high quality. But it requires user effort to update the profile information.
  • Implicit User Profiles are system generated. They are created from the digital footprints of users. This requires minimal user effort. However, many interactions happen between users and content before an accurate user profile is created.
  • Hybrid User Profiles are a combination of both explicit and implicit user profiles. They are created using AI tools and does not require much effort from the end user.

How are the Millennial User Profiles created?

  • Content-Based Method is used to predict user current behaviour using his past behavior. In this method, user profiles are represented with user search queries and then the system selects the activities that have a high content correlation with the user profile. Hence, this method tends to perform badly if the users’ content is limited.
  • Collaborative Method assumes that the users who belong to the same group (e.g. same age, gender or social class) behave similarly, and therefore have similar profiles and is based on the rating patterns. These groups are referred to as Like-Minded People. Unlike the content-based method, collaborative method recommends the activities or products based only on the similar users’ ratings.
  • Hybrid Method is used to overcome the issues with content-based and collaborative methods. This method guarantees the immediate availability of a profile for each user. The system that employs the hybrid method provides a more accurate description of the user interests and preferences, as it continuously monitors and retrieves the user related information through the user-system interaction.

AI-Boosted Personalization — Some Awesome Use Cases

  • Volvo is trying to leverage technology big time. Its machine can now analyse one Million events to discern their relevance to breakdown and failure rates
  • American Express is using AI to crunch through its mass data and make quicker, smarter decisions, detect fraudulent cases in pretty much real time.
  • Thread is using AI to provide personalized clothing recommendations to its customers. Customers take style quizzes which provides data about their personal style.
  • The first AI-powered jeweler, Rare Carat uses AI to compare prices of diamonds across numerous retailers to find the best deal for its customers.
  • Macy’s uses AI technology to enhance the shopping experience. Macy’s On Call, its smartphone-based assistant chats with the customers when they enter the store. The chat bot asks questions to make personalized shopping, provides recommendations and directions around the store. It can also sense when the customer is not able to find what he needs, sends out an alert to human associates to intervene appropriately.

Shaping Data into Knowledge

Data Protection and AI-driven Personalization

Final Thoughts



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