Transform Data Into Insights With Predictive Analytics
This post is part of our Analytics of Things series.
You want your business to succeed, as does everyone who starts one, but you also need a plan in place to help you navigate uncertainties.
Predictive analytics may sound complex, but broken down into well-defined steps, it can support your decision-making process so you can maintain your competitive advantage.
Here's how you do that.
Get Started with Predictive Analytics
1. Define your business objectives
Your objectives are questions and tasks pertinent to your goals. Each goal will have an outcome that gives you data that your business can measure. These will form the base of your analytical exercise; a clear list of these questions is your starting point.
2. Prepare your data
The first step in data preparation is having a team assess data quality and availability. Your data scientists, engineers and business analysts would have the necessary expertise to perform extract, transform and load processes to prepare your data for analysis and predictive modeling.
3. Develop the model
Developing a predictive analytics model starts with setting a clear goal and gathering and preparing data. Choose a modeling technique like regression or classification (explained below). Train and evaluate the model using relevant metrics, then deploy it. Once done, you should continuously monitor and update it for accuracy and relevance.
4. Deploy the model
The newly developed predictive model must now be put into production to deliver results. After your model has learned from data and proven its abilities, you integrate it into your systems or applications. For instance, if your model predicts equipment failures, you'd use it in your maintenance process to spot potential issues before they happen. Deployment ensures your model's predictions assist in making real-time decisions and add value to your business.
5. Validate the results
Compare your model's predictions to what actually happens to see if its predictive capabilities match your requirements. Your team would have defined metrics for your business processes, such as measuring customer churn rate, and analyzing against them would assure you that the model is working as expected.
6. Monitor and adjust
Implement a process for monitoring and adjusting your strategy based on feedback from the data-collection process, analysis, and decision-making.
7. Scale and Optimize
As you gain experience, scale your efforts, explore new use cases, and continuously measure the ROI of your predictive analytics initiatives.
Predictive Analytics: Two-Phase Process
Using past data, you build a model based on statistics, mathematical or computational methods and then look at the results.
In this phase, you collect new data - apply the same model to the latest data - and make predictions.
The fundamental question you would ask yourself is:
How accurate were our predictions once we applied the model to the new data from phase two?
In essence, there are two basic analytics techniques used in predictive analytics. They are regression and classification.
Regression is a statistical method used in predictive analytics to model and understand the connection between independent (predictors) and dependent (target or outcome) variables, output as a number or a measurement. Examples include sales, weight, time, and anything numerical in value.
- Revenue forecasting - how much will we make?
- Marketing mix - how much should we spend on advertising?
- Price/cost modeling - costing products based on production costs
- Customer lifetime value - how much money will a customer spend with us?
Classification predicts a label instead of a number and categorizes or assigns data points to predefined categories. The primary goal of classification is to build a model that can learn from labeled training data and then predict the class or category to which new, unlabeled data points belong. Examples include male or female, legitimate or spam emails.
- Fraud detection - is this a legitimate transaction or a fraud
- Churn prevention - predict the future state of our customers, will they stay or leave
- Conversion modeling - is the customer going to spend money or abandon their shopping cart
- Warranty claim - is this a legitimate claim or a fraudulent claim
Implementing predictive analytics requires a data-driven culture and a disciplined information approach to make more accurate forecasts.
It's normal for teams to be assigned with margins for error and to start predictions from data you already have. By following these steps, businesses can successfully implement predictive analytics and gain a competitive advantage.
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