Predictive analytics is a way for businesses to reduce risk and prepare for the future. It is, essentially, a decision-making tool. This specific type of analytics uses probability from data science to generate predictive scores which then determine the best course of action for an organization to use when interacting with individuals like customers, partners, or employees. Data scientists analyze the results to streamline decision making by predicting the likely outcomes.
Predictive analytics software uses historical and real-time data to create custom business models in order to best predict the outcomes of events. Similar to business intelligence (BI) tools, these systems allow companies to prepare for the unknown by analyzing past information. Companies used to manually forecast business decisions, but predictive analytics software now automates the process.
There are three main types of predictive analytics:
Predictive analytics software can adapt to any of these three types depending on your business needs. The solution you use may even combine all three. Whichever type of advanced analytics you use, the results can be read by data scientists who interpret the data into real-world solutions.
Using predictive analytics software can provide many benefits to your business operations:
Whenever you begin a new project, you can use predictive analytics to better understand the scope of what you’re starting by looking at what you’ve done. Enter historical data to search for past trends in several fields. Then, analyze the information and build models to predict the outcome of your project. By using predictive analytics software, you can begin your new project with confidence.
Cutting-edge predictive analytics products now include artificial intelligence and machine learning algorithms for even more advanced project analytics. AI can see the patterns you can’t, helping you to avoid repeating past mistakes and make better decisions.
In order to make it as easy as possible to interpret big data, the analytics platform includes modeling applications for data visualization. Color-code different data sources, generate continuously updating graphs, and breakdown analytics into reports anyone at your company can understand. By using a visual medium, it’ll be easier for business users and data scientists alike to see commonalities in data analysis.
For example, some predictive analytics tools include decision tree modeling. This software breaks down all the potential outcomes so you can better understand the consequences of your business decisions.
It’s impossible to predict the future, but analytics can help you anticipate potential issues and prevent worst-case scenarios. The software begins the prediction process by collecting historical data about your business, such as past sales performance or industry trends. You decide which variables to study. Next, the software analyzes the data using the parameters you set.
For example, imagine you want to prepare your business for potential disasters like COVID-19. Predictive analytic tools include modeling applications to test out emergency response measures. Use your past data to simulate what would happen if there was a global disruption to your supply chain due to disasters like a pandemic.
Models can also be used to study the probability of small-scale events. You can deploy a model to test the likelihood of a new product doing well with a target demographic. Or you can estimate how your revenue may change in the coming fiscal year. How you utilize the models can have a huge impact on your company performance.
With predictive analytics, you can better understand your target market by reviewing historical data. When do they buy? How often do they require your services? How much are they willing to pay? Predictive software helps you determine potential customers for your products and services by analyzing past buying trends.
Before you plan a sale, analyze who to target in order to increase sales. Predictive modeling applications let you try out different marketing scenarios and see possible results. Want to see how likely 18-24 year olds are to buy your new products if you send an email blast? You can deploy a model for that. Want to target new parents? Test that too. You can even mix variables to get more specific data. Then make a more informed decision before you start your marketing efforts.
Predictive analytics software includes forecasting tools which help you minimize risk from fraud. Machine learning AI can identify common patterns, then flag outliers in the system. This gives you time to review and react to any unusual activity which may indicate fraud.
For example, predictive analytics can review customer behavior. Is someone suddenly buying products in bulk? Has there been a change in buyer frequency? These may all be signs of possible fraud. By using predictive analytics, you can prevent being caught by surprise.
Certain industries benefit more from using predictive analytics:
Predictive analytics has some similarities to business intelligence (BI) tools. While both collect and analyze past data, predictive analytics are far more comprehensive when predicting future events. BI tools are intended to help management make ideal business decisions baked on the study of financial data. Predictive analytics has more in-depth forecasting by using artificial intelligence and machine learning algorithms. These advancements allow for more variables when generating advanced models to review many potential outcomes.