Predictive analytics is a form of computerized AI analysis that uses current and historical data, machine learning algorithms, and real-time status or performance information to make predictions on the likelihood of future events.
Predictive analytics are used in a variety of fields, including businesses for customer risk scoring for diseases in healthcare, predicting appointment no-shows in high-value industries, and fraud detection.
Some of the biggest benefits of predictive analytics, however, are seen in businesses that use it for customer intelligence and asset management. In these cases, predictive analytics can help determine how customers will react to marketing messages and make purchasing decisions, as well as optimizing asset performance to improve business efficiency. Here are seven ways predictive analytics can benefit businesses.
Business Benefits of Predictive Analytics
1. Gain a Competitive Advantage
Everyone has access to data, but few businesses use that data to effectively draw insights about future behavior. Those that do will have a significant advantage over their competitors. Businesses can use that information to create better products and services tailored to their customers, creating more wins in their market. The same principle can be applied to customer loyalty and rewards programs.
2. Reduce Risk
Predictive analytics can be used to great effect to reduce a number of business risks. Retail and service-based businesses can use predictive analytics to gain insights into the success of new products or to get a better picture of who they are doing business with. Service-based businesses, such as utilities, can use analytics in a similar way as well as using predictions to reduce the risk of asset failure and downtime.
3. Reduce Cost
Lower risk translates to lower costs. The better you can predict the future, the more steps you can take to make sure that potential negative financial impact is as low as possible.
For example, many businesses use predictive analytics to determine asset maintenance needs before a problem happens. They then service assets while they’re still functioning, enabling predictive asset maintenance. Predictive maintenance proactively addresses and prevents problems, increasing performance and reducing the costs associated with asset failures and downtime.
4. Increase Production and Operational Efficiency
Predictive analytics are particularly valuable when identifying new revenue-generating opportunities, production optimization, and improving other operational efficiencies. Businesses can use predictive analytics to assess the market for new products, forecasting inventory needs to make production as efficient as possible.
For asset-based businesses, predictive analytics allows companies to take steps to optimize asset performance and timelines. Logistics and railway companies, in particular, often use predictive analytics to improve timelines and schedules, as well as keeping fleet vehicles running at peak performance levels.
5. Improve Safety and Compliance
Predictive analytics can be used by businesses to identify fraud and used by logistics and utility companies to address safety concerns. The result is a higher level of regulatory compliance and a reduced risk of fraud for businesses.
6. Understand Customers and What They Want
Do you really know your customers? Most businesses have a general idea who their typical and ideal buyers are, but predictive analytics can take customer insights to a new level. Smart analytics help businesses form a precise and in-depth picture of who their customers are, which products they are most interested in and what they want in the future.
You may know that your top customers are women in their thirties, but you may not know that your biggest opportunity to win and keep customers starts with women in their early thirties, who are focused on becoming entrepreneurs and growing their business. Producing more products to help them succeed could greatly improve your profits and customer retention.
By leveraging predictive analytics, businesses can start to tailor future offerings precisely to customers’ needs and desires.
7. Process Data Instantly
As AI and IoT devices become more widespread, businesses’ already considerable data collection will increase significantly. That data will become harder and harder to process effectively.
Predictive analytics uses AI algorithms to process data faster and more effectively than statistical algorithms or humans alone, providing better insights in less time. The more data you collect, the more accurate your analytics will become.
Predictive Analytics Aren’t One-Size-Fits-All
Predictive analytics is a broad field. It’s a tool to analyze data that can be used in multiple industries, and businesses typically need different tools to do different types of analysis. Business intelligence tools could help with customer insights, for example, while asset analytics help with asset management. Utilities may need entirely different analytics tools to address their challenges, such as providing better service and reducing outages.
There isn’t a one-size-fits-all solution to predictive analytics. Look at your business’s biggest challenges and the opportunities you have to grow, then determine where predictive analytics could help you improve. Once you understand the need for predictive analytics, you can start evaluating options to find the right solution for your business.