It’s that time of year that people are starting to make their new year’s resolutions to get fit. Gyms across the country are using that data and information to do some targeted marketing, offering sign-up incentives for new memberships and discounts to renew. They know the historical trends that are replicable over time and take advantage of opportunities that are illuminated by the data. They are using predictive analytics.
This is one simple example; however, predictive analytics goes a good bit deeper. A type of advanced analytics often associated with big data and data science, predictive analytics takes historical data and integrates it with complex processes like statistical modeling, data mining, and machine learning to create predictive models. Corporations across industries can then utilize the information gleaned from these models to map out strategic planning, resource distribution, and budgeting.
There are tools available on the market that accomplish several types of predictive models used in analytics.
This model is a common type that uses supervised learning algorithms. A question is generated, prompting branches out training the model based on the possible responses to the question. This generates additional questions, with more branched responses, further training the model for predictive analyses.
This model is good for cause/effect relationships. The modeling technique examines the relationship between a dependent variable, or target, and an independent variable, or predictor. Often used in forecasting, it can help identify the significance of the relationship between the identified variables as well the strength or weakness of the impact of the predictor on the target.
This type of analysis uses mathematical models and data mining to simulate how the brain processes information to detect patterns and learns and refines those processes making them useful for business decisions. Neural networks look and a set of variables involved and analyze the relationships, whether subtle or complex and refine the analysis until a small prediction error is achieved. This renders the predictive value more reliable and therefore more valuable in the decision-making process.
To have predictive models that generate the best application of the data, you must take into account how human behavior and other dynamic factors can affect the final numbers. Algorithms are not failsafe and as such, do not guarantee outcomes, but rather point you towards trends and potentials. You must still rely on the likelihood of an event occurring or not occurring. Think about weather forecasts based on all the various models and predictive data available today. Despite the thresholds being well within the best error margin, predictions can still be wrong!
However, using predictive analysis offers a business several benefits.
When effectively applied, predictive analytics can help reduce risks, provide significant cost savings, allow for better utilization of both human and financial resources, and allow for targeted customer experiences and marketing strategies. It can also help identify new opportunities for generating revenue. Predictive analytics also offers opportunities for improving security and fraud detection, policies and procedures, and capacity and quality. This gives your business a competitive advantage!