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Content Personalization

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Content Personalization

Problem/Challenge – Consumers these days increasingly expect relevant information provided to them. Marketer’s biggest challenges these days is relevance. For many marketers, broad segmentation based on historical data and segment-based content creation has been the best approach available but it is not practical or scale able for human teams to understand and adapt to the individual preferences of millions of customers.

Resolution – Machine learning algorithms can accurately discover the preferences and purchasing behaviors of individual consumers. With machine learning models marketers can identify patterns about customers intent including real-time behaviors, prior purchases, preferences, and interests of similar customers. For example, on web pages, AI can be used to dynamically select the content including images and messages that will be most likely to convert a given customer. An AI based recommendation tool can help increase customer satisfaction, higher brand value, and therein result in more sales.

Why CafeBot – CafeBot’s aim is to leverage AI in accurately predicting customer’s interest aligned content, it will automatically engineer and identify the individual reasons for why each customer has interest in that product or offer. It empowers data science teams to scale by dramatically increasing the speed to develop highly accurate predictive models with lesser number of resources. CafeBot includes innovative features including data ingestion from different data sources, data blending, data visualization, automatic machine learning and deep learning, model deployment and predictions, and interpreting the machine learning model built.