Predictive data analytic techniques are a powerful resource to use, helping businesses forecast future outcomes. Predictive analytical methods are applied in order to find the most probable answer to specific questions such as, “how long will this machine keep working before it breaks down?” or “how likely is it that this borrower will default on repayments?” The results of predictive analytics enable organizations to work more proactively and be better prepared for more probable outcomes.
Predictions become possible by building models based on historical data with a view to capturing trends. The model is then applied to current data in order to predict probable outcomes in the future.
So, what are some of these statistical and data analytics techniques? Here are a few:
- Data mining: Data mining uses software to search for patterns and is an important component of predictive analytics. Large volumes of data are analyzed to identify patterns and relationships between variables. This usually involves data preparation — i.e. cleaning and selection of datasets. This helps to identify the most relevant variables and the general nature of the models.
- Predictive modeling: Creates and adjusts a statistical model to predict future outcomes. This stage is all about evaluating different models and choosing the right one for your objectives. It involves applying different models to the same data set and comparing their performance to choose the most appropriate ones. Predictive modeling is generally applied on data collected from sources such as transaction data, CRMs, customer service data, survey or polling results, digital marketing, and advertising data, economic data, demographic data, machine-generated data (for example, from IoT sensors), geographical data, web traffic data, etc.
Let’s look at some of the models for predictive modeling that you may consider:
- Regression: Regression models help to determine the relationship between a dependent or target variable and an independent variable or predictor. That relationship is then used to predict unknown target variables of the same type based on known predictors. This is the most widely used predictive analytics model and includes linear regression or multivariate linear regression, polynomial regression, and logistic regression. Regression is used to predict future demand, in order to plan production and supply chain to best meet demand. Regression is also used to decide the right pricing strategy, suggesting the best price for a product based on sales data analysis. Regression models are applied to predict how changes in factors such as interest rates will impact stock prices.
- Classification: This modeling technique creates categories and observes patterns for the category. New data is assigned to a category and future outcomes are predicted. For example, customers can be classified into categories based on their demographics or socio-economic data, and the repeat purchase behavior for each category can be observed. New customers are then placed into a category and future buying behavior is predicted.This technique is also used to classify employees to predict their performance, attrition, etc. Some classification techniques are decision trees, random forests, and Naive Bayes.
- Clustering: Clustering also involves grouping but it differs from classification. Data is grouped into clusters based on similarities, not on predefined classes. Clustering allows the data to determine the clusters — and the defining characteristics of each — rather than using preset classes. This technique is extremely useful when we don’t know much about the data in advance. Classification is used for supervised learning whereas clustering can be used for unsupervised learning. Clustering helps identify the most relevant factors within a dataset. Some clustering techniques are k-means, Fuzzy c-means, Gaussian (EM) clustering algorithm, etc.
- Deployment: The selected model is now applied to predict future outcomes.
Predictive analytics has powerful applications in many different areas of business, such as demand forecasting, workforce planning, risk management, competition analysis, equipment maintenance, credit management, and customer service.
It is very important for the predictive analytical model to be based on sufficient amounts of data in order to succeed at predicting outcomes. The sample size should be large enough, have a wide variety of records and be representative of the target population. Most predictive analytics projects do not depend on a single modeling technique, but use a combination of techniques instead. Multiple modeling techniques are either applied together or sequentially in order to increase the accuracy of the prediction.