Resources
The explosive emergence of OpenAI’s ChatGPT has generated a wave of intense interest among enterprises of all sizes and industries in leveraging Large Language Models (LLMs) to create chat-based interfaces for their end users.
Using AI to enable efficient cross-selling and upselling has become increasingly important in helping businesses increase revenue and profitability while simultaneously improving customer engagement and loyalty.
Graph neural networks (GNNs) have emerged as a leading solution for machine learning (ML) applications, as many real-world problems and data can be effectively modeled as graphs.
Kumo.ai enables users across the enterprise to rapidly develop, evaluate and deploy state-of-the-art predictions in production in hours instead of months.
When marketing resources are constrained, it is critical for businesses to identify and focus on the future high value customers that will have the biggest impact as these users represent the biggest opportunity for the business.
If you’ve ever unsubscribed from a notification, you’re aware of what a poor notification experience is. Similarly, if you’ve ever clicked on a notification, you’ve likely found it to be useful and relevant. In this article, we’ll share what a good notifications strategy looks like, and how to use AI to improve your approach for every user.
Personalization is all around us. If you’ve ever received a relevant recommendation on a website, a notification from an app, or a promotion in your inbox, you’ve been delivered a personalized experience.
Customers are watching their pennies now, just like the rest of us. This means at a time that companies are doing everything they can to grow revenue, they need to be on high alert to anything that could push their customers away.
Predictive analytics traditionally refers to the process of identifying meaningful patterns in historical data in order to predict future trends and events. Having the ability to forecast potential scenarios can help drive strategic decisions.
As every data scientist knows, it takes a significant number of manual steps to go from a business problem with raw data to a fully operational production model.
By using this website, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Cookie Policy for more information.