Oracle NetSuite has unveiled new updates to its NetSuite Analytics Warehouse, incorporating advanced artificial intelligence (AI) capabilities. These enhancements are intended to enable businesses to swiftly analyse data and extract contextual insights that can enhance decision-making and foster growth.
"For growing businesses, making sense of data can be a time-consuming process that may require advanced data science and coding skills," said Evan Goldberg, founder and executive vice president of Oracle NetSuite. "With limited resources, many businesses are not able to invest in these skills and miss out on valuable data insights. We're dedicated to helping businesses of all sizes unlock the full potential of their data. The latest updates to NetSuite Analytics Warehouse will help customers automate data analysis and leverage AI to produce fast and meaningful insights that can help improve decision-making."
Constructed on Oracle Analytics Cloud and Oracle Autonomous Data Warehouse, NetSuite Analytics Warehouse utilises AI to process business data and pinpoint opportunities for efficiency. The recent updates introduce several new AI tools and models aimed at enhancing data analysis efficiency and providing predictive insights to refine forecasting.
Among the new capabilities are:
- Auto-Insights: This feature accelerates reporting and aids decision-making by generating data visualisations and natural language insights based on a dataset's attributes, measures, and other points of interest.
- Explain: Utilising AI, this feature helps customers glean deeper understanding of their business by identifying significant business drivers, contextual insights, and data anomalies.
- Oracle Analytics AI Assistant: This tool streamlines data discovery via conversational interactions. Customers can pose questions about data patterns, and the Assistant, through Generative AI, will formulate answers and relevant data visualisations.
- Out-of-the-box AI models: These models empower customers to improve decision-making by automating analysis with no-code models designed for specific use cases, such as predicting customer churn and inventory shortages.
- AutoML: This capability enhances insights and efficiency without necessitating technical AI skills by automating algorithm selection and customising modelling workflows.
- Oracle Machine Learning: Providing a collaborative interface, this tool allows users to visually explore data and tailor machine learning models to their unique business needs, aiding in enhancing algorithm performance and expanding insights.
BirdRock Home, a manufacturer of various home goods, has reported success using the new predictive model for customer churn available in NetSuite Analytics Warehouse. BirdRock Home completes over half of its sales through ecommerce marketplaces like Amazon and has employed the predictive model to optimise product strategies, improve customer experiences, and elevate revenue while maintaining customer engagement.
"Ecommerce marketplaces are extremely competitive environments, and it can be difficult for merchants to stand out and create brand loyalty," noted Mark Chuberka, senior NetSuite administrator at BirdRock Home. "With predictive insights on customer churn, NetSuite helps us understand customer demand for specific product lines and forecast which new products will likely drive continued growth."
