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.
Most recommendations, promotions, and advertisements people encounter on a daily basis are the result of many complex data pipelines that transform consumer behaviors into targeted predictions.
Having a fully automated detection system at scale is critical for organizations to ensure trust with their customers, however this is incredibly difficult to do effectively in practice. In this blog post, we’ll dive into the mechanics of these systems and talk about some of the traditional approaches.
Today, when enterprises say they are “data-driven,” they primarily rely on a backward-facing approach for making decisions.