A: Predictive modeling uses historic data to predict future events. That can be time-based data, or it can be data about the behavior and characteristics of past consumers. By using historical data to build a mathematical model and capturing those important events or trends, a predictive model then uses current data to predict what will happen next or suggest actions to take for optimal outcomes.
Generally, people will group the process of predictive modeling into either five or seven steps:
Step 1: Understand the business objective.
You're not actually doing any predictive modeling yet, but if you don't have a clear understanding of what you are trying to achieve and what business question you need answered, then you're just modeling for modeling's sake.
Step 2: Define the modeling goals.
Do you have certain expectations around the accuracy of your models? Are there other factors that you need to account for, like the speed of the predictions or the scores being produced, because of the nature in which you plan to ultimately deploy that technology? Do you need to do it in milliseconds, or is it something that can be done over a week? That plays into which model you will select.
Step 3: Gather data.
Gather any data that will be relevant to making those predictions, including both the historic data surrounding the event you’re trying to predict and the data surrounding characteristics or behaviors. Data mining is a common way to gather the necessary information.
Step 4: Prepare the data.
Preparing the data is the longest and most tedious aspect of the entire process. Eighty percent of the total time will be spent cleaning the data, transforming the data, or turning the raw data into meaningful variables that fit better with your choice of predictive model.
Step 5: Analyze the data.
At this step, you may do some sampling to test and evaluate.
Step 6: Develop the model.
Next, you select and develop the actual model that you’ll use to get the business insights you need from your data. Once you've developed those models and you're pleased with the expected accuracy and the estimates that they are producing, then you validate the models.
Step 7: Validate the model.
This is where you're testing, optimizing, and deploying the models into the areas of the business in which you may need to take action. Not all predictive models need to be deployed. Some of them just produce an output, and then the business understands that output. But depending on the business objective and the intended goals, you may need that predictive model to be deployed. The deployment can require the model to be integrated into technologies that you're using, for example, instantly personalizing content for a customer as they're engaging with your website.