AI Drives Relevant Dialog with Customers
One of the biggest challenges for marketers today is relevance. More and more, consumers expect content to be personalized based on their needs and preferences. An email with generic messages falls flat and has a low click through rate, or a website landing page with generic messages and content fails to convert prospects to the next level. For many marketers, broad segmentation based on historical data and segment-based content creation has been the best approach available. Unfortunately, even this approach leaves many campaigns with low click through and conversion rates.
AI techniques can be used to create 1:1 personalization of content that matches the right content with the right customer at the right time. AI models can use a broad set of signals about customer intent including real-time behaviors, prior purchases, preferences, and interests of similar customers. For website landing pages, for example, AI can be used to dynamically select the content including images and messages that will be most likely to convert a given customer. For emails, AI can be used to dynamically compose an email creative from available images and offers to provide the greatest appeal. For all these cases, the content that is selected is based on the individual customer’s real-time behavior, most recent purchases and interests, instead of old insights or broad segments.
The mission at H2O.ai is to democratize AI for all so that more people across industries can use the power of AI to solve business and social challenges. Across industries, marketing use cases drive significant use of H2O.ai products to solve key challenges including lead scoring, customer segmentation, offer optimization and content personalization. Marketing technology companies including G5 and Poder.io trust H2O.ai technology to help them deliver innovative marketing solutions for their clients. H2O Driverless AI is an award-winning platform for automatic machine learning that empowers data science and technical marketers to scale machine learning efforts by dramatically increasing the speed to develop highly accurate predictive models.
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