Predictive modeling is a technique that is used to predict future behavior. Predictive modeling solutions use data mining and probability to analyze past and current data to forecast future outcomes. It answers the questions: What will happen in future? How does it affect my business tomorrow?

For example, if a customer has purchased a laptop, it could possibly mean that the customer might be looking to purchase some accessories immediately. The customer will look for an antivirus software but his chances of buying another laptop immediately are quite dim. In a similar way, thousands of products and customers are analyzed based on their shopping and usage patterns and companies use these results to boost their business. In fact, studies say that in their daily lives, people follow a routine almost 90 percent of the times; hence behavior can be predicted with just a few mathematical equations.

In predictive modeling, information is collated from existing large data sets. This data is cleaned and organized in a structure so that patterns and trends can be identified. Once these data sets are ready, a predictive model is formulated and used to predict useful insights. The model may be simple or a complex neural network. NNs, SVMs, decision trees, regressions (linear and logistic), clustering, association rules, and scorecards are some of the predictive modeling techniques that data scientists use today to learn patterns hidden in the data. For example, some models could use a regression function to compute a score or risk, such as predicting the risk of customer churn or the risk of machinery breakdown. Predictive models can also be used to implement a classification function, in which, the result is a class, such as assigning a given email into ‘spam’ or ‘non-spam’ classes. These insights are used to create visual representations of the information and used upstream for deriving useful results.

To summarize, there are four aspects of predictive modeling that we must consider:

• Collect Sample Data: the collected data that describes the problem with known relationships between input and output.
• Prepare the Data: The data should be properly cleaned up, so that typical errors during the data acquisition phase can be normalized and a sound model is produced.
• Learn a Model: the algorithm that is used on the sample data to create a predictive model that can be later used over and over again.
• Make Predictions: use the learned model on new data for which prediction is required.

Irrespective of the type of model, one thing is sure and that is the fact that predictive models are shaping our experiences wherever we go and whatever we do. Predictive modeling helps recommend products and services based on our buying habits. It helps healthcare providers design and implement preventive life-saving measures based on risk factors associated with a particular disease.

Applications

When predictive modeling is deployed for commercial purposes it is referred to as predictive analytics. Predictive analytics is normally associated with weather forecasting and meteorology but it has innumerable applications to predict the behavior and assess risks over a wide variety of disciplines including actuarial science, insurance, banking and financial services, marketing, healthcare, fraud detection, crime prevention, retail market, customer prospecting and churning, cross-selling, production, and capacity planning.

Bayesian spam filters use predictive modeling to identify the probability that a given message is spam. Predictive modeling is used in fraud detection to scan millions of transactions to spot patterns and detect fraudulent transactions. Predictive modeling is also used in customer relationship management (CRM) for targeting messaging to those customers who are most likely to make a purchase.

Predictive modeling techniques are becoming popular with businesses that are looking to identify potential customers and sharpen their marketing initiatives.

For example, an online supermarket can sort out information based a customer’s age, gender, location, and past purchasing pattern to automatically recommend products to that customer based on the predicted propensity to buy these items.

A bank can use thousands of data points created by customers’ interactions to decide which type of credit card should be offered to which customer and when.

An online retailer can use a customer’s digital profile to decide which shoes to recommend to that customer as the next purchase, based on the jeans that the customer has recently purchased.

Predictive analytics allows retailers to improve their conversion rates as well as set pricing strategies based on consumer expectations and competitor benchmarks. It allows retailers to maintain stocks based on anticipated demand or even suggest new product lines based on changes in customer preferences.

Conclusion

Predictive modeling has proven its value as a foundation for companies who need insights into gaining a competitive edge, or are looking for new business opportunities, or enhancing their products and services.