Business View Magazine | Volume 9, Issue 3

13 BUSINESS VIEW MAGAZINE VOLUME 9, ISSUE 3 Written by Srikanth Aravamuthan, Vice President, Exafluence, Inc. With the ability to capture and analyze massive data in every sector of the economy, predictive modeling has already become the new essential for businesses of all types and sizes. That’s because it touches all aspects of business, from how customers will react to a specific product or service to forecasting how sales or revenue will grow or shrink at any given time. In addition, predictive modeling facilitates a glimpse into the psyche of customers and their level of loyalty and satisfaction with a vendor or supplier. Just imagine how advantageous it would be to identify a customer who may be ready to jump ship and abandon a business for a new start with a competitor’s company – and then prevent that event from taking place. Predicting the future Predicting the future enables businesses – large and small – the opportunity to tailor services and products to retain customers and gain a competitive edge in the marketplace. For example, if data shows that three months down the line a segment of your customer base is likely to leave, a business owner could – and should – take steps to retain those customers by taking a deep data dive to prevent the loss. But it’s tricky. Identifying potential problems and concerns to stop a human behavior takes finesse, extensive information, and the ability to analyze and interpret the data collected. While most predictive models include a ‘probability’ of an event occurrence, they do not provide a description of the why this event is likely to occur. Here’s where “explain-ability” comes into play. The explain-ability factor in predictive modeling allows businesses to slow or stop the churn (turnover) that is typical in all businesses. For instance, if a customer from a telecom company decides to move to a new telecom network, the original vendor now has the ability to predict which customers are more likely to switch and what the potential reasons are. Explain-ability in this instance provides a very powerful tool and insight to the organization to take or not take action at an individual customer level to prevent customer churn. What’s the rationale? Fine-grain, massive amounts of data give companies an edge over their competitors and enable them to grow revenue. Algorithms that provide “black box” models enable businesses to run every system better, whether it’s a computer or cell phone infrastructure, or even how businesses may strategize to win business from competitors. But massive data collection generally doesn’t provide the rationale behind predictions. Are prices too high, is service lacking, are delivery schedules too long? Every bit of information requires explanation for context, which then allows Predictive Modeling Using Data Analytics to Change the Future