Top 6 Predictive Analytics Models and Their Use Cases

People who use predictive analytics models aim to look at current and past data and use it to pinpoint the likelihood...

People who use predictive analytics models aim to look at current and past data and use it to pinpoint the likelihood of future outcomes. Here are six common types of predictive analytics models and potential reasons to use them. 

The decision tree model has a structure similar to a flow chart. It makes predictions based on the answers to previous questions in the tree. The base of the model is the root node, and all the possible questions stem from it. The decision nodes represent the questions, and the leaf nodes are the answers. This model does not always reach firm conclusions, but it often allows data scientists to study options and make more-informed decisions. 

Marketers use the decision tree model to predict the success of product launches. It can also help them determine if at least acertain percentage of the target marketwill show interest in an option. Emergency room managers might use decision-tree models during triage to get guidance on which patients to see first for the best outcomes. 

This model helps people determine future metrics. It’s an appropriate choice as long as a user has historical and numerical data to use. This model can also account for various parameters when making its predictions.

For example, consider if a restaurant manager wants to know how many restaurant bookings they’ll have next Friday night. The forecast model could account for things such as holidays or other special events that could make people more or less likely to want to dine out, whether illnesses are circulating in the area and if there are large events, such as sporting events or concerts that could bring more tourism to the vicinity than usual. 

The outlier model allows making predictions about whether something is an unusual event that deserves flagging. Banks often use this type of predictive analytics for fraud detection. These applicationsusually have pattern-recognition capabilitiesthat pick up on what’s normal. Think about if a person suddenly makes a 100 on average. That transaction would trigger the outlier model because it’s unusual. 

Many organizations teach people to recognize when something might be amiss — particularly in legitimate industries that are rife with fraudulent activity,such as debt reliefand credit card companies,state lottery systems, andcharity and nonprofitorganizations. Typos, requests to provide information urgently and emails with strange attachments are all common signs of potential online scams. However, even the most sharp-eyed people can’t catch everything, but outlier models can fill in the gaps. 

The classification model analyzes historical data and uses it to make predictions. It’s one of the simplest predictive models because it’s primarily best for dealing with questions that have “yes” or “no” answers.

Statistics indicate it can costmore than 1.25 times a person’ssalary to train them for a role. That’s why many human resources professionals use platforms that rely on the classification model to determine which employees are likely to leave soon. If managers get clues about that in time, they could intervene by having in-depth conversations with unhappy employees and try to turn the situation around. 

The regression model predicts the relationship between two variables. The results provide users with a numerical value they can use to make time-based decisions.

One possible use case is to learn how much revenue a particular piece of critical machinery will generate for a company before it experiences an operation-halting breakdown. Company leaders can rely on the outcomes to figure out the best times to schedule parts replacements and other vital maintenance. 

People often choose the time-series model when they need to makeshort or long-term predictionsbased on historical data patterns. One common application enables retailers or other supply chain members to assess which items will most likely be in the highest demand during particular months or seasons.

However, it can also aid people who need to forecast traffic levels. How busy will an airport likely be during the summer months versus the winter? When will an e-commerce site probably have its highest number of visitors? These are valuable questions, and knowing the answers to them can help people steer clear of unwanted consequences.

These use cases highlight why predictive analytics is so significant across numerous industries. Most decision-makers don’t like uncertainty, and using the right predictive analytics models can substantially reduce it. However, there’s no universally best model to select in every case. Reviewing how people often apply the types here and others will help you draw an informed conclusion for any current or future project.