Effective customer outreach is challenging, and building an ML system to solve this isn’t exactly easy either.
ML-powered recommendations can be useful to your business in many ways. It can come in the form of a personalized homepage showing the user highly relevant content, or showing them a curated list of products they are most likely to be interested in.
One of the most important questions growth and GTM teams ask their data teams is - who are my most profitable customers and how can I get them to increase their spend?
Churn and retention models are useful to identify who is likely to stop using your product or service within a specified timeframe, say within the next week or month.
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.