Mistakes and inefficiencies cost money. They also impact time, resources, employee retention, customer satisfaction, and business growth.
The good news is that you can learn from these outcomes by tracking data like customer engagement, process efficiency, and employee productivity. Even better, you can reduce your likelihood of making those mistakes in the first place by analyzing the information you collect about what happened in the past and using it to predict future results.
This is the power of predictive analytics. You’re probably aware that this capability is available and that it’s an application of Big Data, often used in conjunction with artificial intelligence technology.
What you may not know is how predictive analytics can specifically benefit your organization. In many cases, we’re not talking about huge changes to your processes. We’re talking about taking the data you probably already collect (or could collect), applying a analytics software tool to deliver key insights, and predicting future behavior based on those insights.
It’s a simple concept—but what does it look like in an actual business context?
Whether you’re in marketing, risk management, HR, IT, or any of a multitude of other business roles, predictive analytics software can help you achieve better outcomes.
Let’s take a look at some specific industry examples.
Preventive maintenance is an artificial intelligence application that monitors equipment function and triggers an alert when maintenance needs arise, ideally before the machine breaks down. By quickly identifying which parts are most likely to fail and which ones are ready for replacement, manufacturers can minimize down time and improve equipment utilization across the factory.
Demands forecasting uses past customer behavior to plan for future product needs. The key difference with predictive analytics is that companies can pull data in from many different sources and identify trends, anomalies, and potential disruptive events (such as new product innovation) that will affect customer behavior in the future rather than simply relying on past data.
A counterpart to demand forecasting, predictive resource allocation uses real-time analytics to prevent resource bottlenecks, pinpoint fluctuations in customer need, and make resource decisions that will make the manufacturing floor more efficient and lead to the highest profit margin.
Predictive workplace safety software not only tracks past safety incidents and injury occurrence, but also analyzes data from multiple sources to predict where and when safety may be compromised. By identifying safety risks before injuries happen, companies can protect employees from harm, reduce insurance premiums, and improve safety compliance.
Lead scoring is typically a collaborative process between sales and marketing, but predictive analytics can take much of the subjectivity out of the process. By analyzing data like customer behavior, demographics, and psychological indicators, predictive analytics can identify which leads are most likely to buy soon and which ones should be funneled into a nurture campaign.
Where do your best customers come from? What do they look like? And most importantly, how can you reach them? Use predictive analysis to identify key audience segments, assess their behavior, and predict how, when, and where they will be most likely to engage with your brand.
You’ve probably wondered exactly how autonomous cars work. One of the key drivers (pun intended) of the technology is predictive analytics. These vehicles constantly collect and analyze data, giving them the ability to “learn” driving techniques and respond to potential hazards.
Automotive manufacturing can optimize equipment utilization, maintenance, resource allocation, and demand forecasting just like any other manufacturing industry. Success in any manufacturing endeavor depends on coordinating all the data collected on suppliers, logistics, and processes, and then using that data to predict which actions will produce the best results.
Customer feedback plays a key role in quality assurance, and predictive analytics helps companies collect insights and make useful predictions from that feedback. For example, predictive analytics can improve test efficiency by prioritizing use cases for the highest impact, optimizing processes to reduce cycle times, and predicting defects and risk to avoid recalls.
Most financial institutions track user behavior and demographics to identify anomalies, such as someone trying to log into an account from an unknown device or spending money in an unusual way. Predictive models can use that data to identify potential risks before they happen and suggest precautions that should be taken to prevent fraud.
Data breaches put customer accounts at risk, but predictive analytics can play a role in preventing cyber attacks by pinpointing vulnerabilities before a breach occurs.
Credit Risk Analysis
Lenders depend on predictive analysis every day. Using data such as payment history, total amount of debt, new credit, and personal indicators, credit scoring software can predict whether someone is likely to repay a loan or not, and therefore whether they will be an acceptable credit risk.
New sentiment analysis tools can analyze employee feedback to determine which ones are dissatisfied with their roles and suggest actions the company can take to address problems. But even without sentiment analysis, predictive tools can assess data to predict engagement problems and address them early.
Retention and engagement go hand-in-hand. By tracking employee engagement data, predictive models can identify which employees are most likely to leave the company. With that information in hand, companies can determine what steps they should take to keep top performers on the payroll.
Predictive analytics helps you decide where to invest your resources to improve employee productivity. For example, do your employees need more training, do you need to hire different kinds of candidates, or is your software causing process hang-ups? Data analytics can help you answer these questions, and predictive software can identify potential problems and next steps.
Predictive analytics also helps you hire more of the right people by predicting which candidates will integrate best into the culture of your company.
Of course, many of these applications can be implemented in multiple contexts, and we could make lists like these for every industry. Predictive analysis is useful for scheduling shifts in hospitals, assessing public health risks, identifying upsell and cross-sell opportunities for retail, preventing abuse and fraud in government, optimizing shipping routes in transportation and much more. Anytime you collect data, analyze it, and use it to predict an outcome, you have engaged in predictive analytics. With the right software tools, you’ll quickly gain a competitive edge over others in your industry.
Working in conjunction with the Internet of things (to connect equipment and devices for greater data integration), machine learning (to improve outcomes over time as more data is collected), and artificial intelligence (to minimize human error and perform processes more efficiently), the applications of predictive analytics will only keep growing.
In fact, I’ll engage in a little predictive analysis right now. Based on the game-changing evolution of predictive analytics and the competitive advantage those applications deliver, we can foresee that predictive software tools will be non-negotiable for success in the future.
Without them, you’re running blind.
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