Predictive Analytics Tools

Predictive analytics is an advanced form of analytics that uses historical data analysis, data mining, statistical modeling, and Machine Learning (ML) technology to predict future trends, events, or outcomes. Organizations typically use predictive analytics models to identify patterns, discover risks or issues, and highlight opportunities. 

Predictive analytics models can analyze data to predict events that will happen soon (within a day) or in the more distant future (within a year), so companies can use this data to guide short-term and long-term business decisions. 

Selecting and Implementing the Right Combination of Predictive Analytics Tools

Predictive analytics is a form of reporting that can be implemented both via dedicated Machine Learning platforms, as well as through Business Intelligence software. While AI tools offer ML and deep learning capabilities to identify patterns within data, leading BI vendors utilize similar, AI-powered technologies to deliver intelligent reporting capabilities to their users.

Alternatively, businesses can integrate both BI software and AI/ML tools to create a more customized predictive analytics solution. Determining whether a BI platform is sufficient, or a more comprehensive set of AI-powered tools is required to conduct predictive analytics is a quandary that needs to be carefully addressed before finalizing any software decisions.

For example, smaller companies just starting out in predictive analytics may benefit from a SaaS-based BI solution that is easily attainable via a monthly subscription. On the other hand, a large enterprise with established, comprehensive data analytics processes may require a dedicated AI or ML platform to drill deeper and leverage its complex data.

Key Use Cases for Predictive Analytics

• Sales: Predictive analytics uses historical sales data and customer behavior analysis to forecast future sales patterns, identify successful sales trends, highlight new market opportunities, and optimize sales processes and tactics.
• Marketing: Predictive analytics provides insight into customer buying patterns and behaviors, predicts the most effective marketing methods and messaging, identifies the best timing for marketing campaigns, and helps companies enhance marketing efforts.
• Human Resources (HR): Predictive analytics uses historical employee data to make data-driven predictions that help HR teams improve employee performance, reduce turnover rates, and recruit more effectively.
• Finance & Accounting: Predictive analytics identifies trends in historical financial data to forecast accurate budgets, predict cash flow, manage risk, and help companies make data-driven decisions on spending and investments.
• Manufacturing & Supply Chain: Predictive analytics uses past and current warehouse data to forecast demand, predict necessary inventory levels, and optimize shipping and warehousing processes.
• Security & Fraud Detection: Predictive analytics analyzes historical data and user behavior metrics to identify patterns, point out anomalies, and highlight suspicious activity to mitigate risk and reduce fraud.