What is Predictive Analytics?
Predictive analytics is the area of data science focused on interpreting existing data in order to make informed predictions about future events. It includes a variety of statistics techniques.- Data mining: looking for patterns and relationships in large stores of data
- Text analytics: deriving analysis-friendly structured data from unstructured text
- Predictive modeling: creating and adjusting a statistical model to predict future outcomes
- Marketing: Predictive analytics predicts campaign opportunities and helps find new markets for products and services.
- Operations: Analytics power smart inventory management systems, forecasting supply and demand levels based on a variety of factors. They’re also used to optimize repair schedules to minimize equipment downtime.
- Sales: Identifying a company’s best clients and predicting customer churn are two strengths of predictive analytics.
Choosing The Right Model for the Job
Predictive analytics has a wide spectrum of potential applications. It follows logically that there’s an equally wide variety of models in use. These can be roughly grouped into some main types:Regression
Regression models determine the relationship between a dependent or target variable and an independent variable or predictor. That relationship used to predict unknown target variables of the same type based on known predictors. It’s the most widely used predictive analytics model, with several common methods:- Linear regression/ multivariate linear regression
- Polynomial regression
- Logistic regression
Classification
This form of predictive analytics works to establish the shared characteristics of a dataset and determines the category of a new piece of data based on its characteristics. It predicts future classes of data, so it does involve defining those classes. Some classification techniques include:- Decision trees
- Random Forests
- Naive Bayes
Clustering
Clustering involves grouping data by similarities into “clusters”, or groups of closely related data. During clustering, the most relevant factors within a dataset are isolated. The process maps the relationships between data that can then be applied to predict the status of future data. K-means clustering is arguably the best known form of clustering, though other techniques are in place. Clustering has the advantage of letting data determine the clusters- and therefore the defining characteristics of the class- rather than using preset classes. It’s extremely helpful when little is known about the data in advance. Analysts frequently use cluster models during customer segmentation. Here, it finds the traits that actually separate classes of customer from each other rather than relying on human-generated classes like demographics. Those classes can be taken a step further to inform targeted marketing strategies.Combining Models
Few problems are so simple that they can solved with a single predictive analytics method. In practice several techniques are usually applied together or in succession in order to produce the most accurate representation of the data.The Future of Predictive Analytics
Machine learning has made predictive analytics more efficient than ever by enabling the analysis of vast amounts of data. It’s likely, then, that predictive analytics will continue to be a popular and well-known application of data science.Are you having trouble finding useful predictions within your company’s data? Concepta has the data visualization tools to put your data into perspective. Contact us for a free consultation!