Better together: Predictive analytics and AI boost each other
Through machine learning models, companies in retail, insurance, energy, meteorology, marketing, healthcare and other industries are seeing the benefits of predictive analytics tools. With these tools, companies can predict customer behavior, foresee equipment failure, improve forecasting, identify and select the best product fit for customers, and improve data matching, among other things.
Enterprises of all sizes are now finding that the combination of predictive analytics and AI can help them stay ahead of their competitors.
Retail brands are constantly looking to stay relevant by associating themselves with the latest trends. Before each season, designers are hard at work coming up with new styles and designs they think will be hits. However, these predictions can be faulty based on a number of factors, such as changes in customer buying patterns, changing tastes in particular colors or styles, and other factors that are difficult to predict. AI-based approaches to demand projection can reduce forecasting errors by up to 50%, according to Business of Fashion. This improvement can mean big savings for a retail brand’s bottom line and positive ROI for organizations that are inventory-sensitive. Another industry that has seen tremendous improvements is meteorology and weather forecasting. Traditionally, weather forecasting has been prone to error. However, that is changing, as the accuracy of 5-day forecasts and hurricane tracking forecasts has improved dramatically in recent years. According to the Weather Channel, hurricane track forecasts are now more accurate five days in advance than two-day forecasts were in 1992. These extra few days can give people in a hurricane’s path extra time to prepare and evacuate, potentially saving lives. Utility companies are also using predictive analytics to help spot trends in energy usage. Smart meters monitor activity and notify customers of consumption spikes at certain times of the day, helping them cut back on power usage. Utility companies are also helping customers predict when they might get a high bill based of a variety of data points and can send out alerts to warn customers if they are running up a large bill that month.
For industries that heavily rely on equipment, such as manufacturing, agriculture, energy or mining, unexpected downtime can be costly. Companies are increasingly using predictive analytics and AI systems to help detect and prevent failures. AI-enabled predictive maintenance systems can self-monitor and report equipment issues in real time. IoT sensors attached to critical equipment can gather real-time data, spotting issues or potential problems as they arise and notifying teams so they can respond to them right away.
