If you know what’s going to happen next, it would be incredibly easy to be successful. So far, predictive analytics is the closest we’ve been able to come to knowing the future. In short, if you have a deep wealth of data on a particular audience, analyze all that data to find patterns, and organize it in a way that can be acted on, you can then predict the possible outcomes.
This particular brand of AI is often favored by marketers to assess the validity of advertising, media, and traditional campaigns. The more data you have on your subject the more accurate your predications are too. Traditionally, statistical analysis, product modeling, market demographics, and data sellers have been the biggest proponents of predictive analytics.
However, in recent years, machine learning techniques have been used with great success. As with any machine learning application, there is not a universally accepted algorithm to use. Instead, the type of data analyzed and the type of predictions you want both heavily influence the right algorithm for your needs.
In one case, you may want to implement an AI to monitor your supply chain to detect shortages, delays, or other potential issues. A completely different function may be to identify marketplace trends and capitalize on them in the future. A few common algorithms used elsewhere in the field of predictive analytics include support vector machines (SVM), k-nearest neighbors, and neural networks.
Aside from the knowledge gained and time saved, an AI can crunch vast amounts of data much faster than a human. It can then find those common threads to hone in on so you can turn your data into actionable intelligence.