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 companies to create comprehensive solutions that will boost the bottom line. Currently there are developers of algorithms that don’t quite grasp exactly why the product they developed behaves the way it does. A new wave of explainability fills that gap, providing companies with practical data derived from a complex model.
Artificial intelligence and predictive modelling have evolved over the last decade from a purely statistical framework to systems that drill down into every detail of product development and sales, to customer service that not only measure client satisfaction, but gauge holistic experiences as well. These calculations give companies the edge required to increase their sales base and improve the delivery of services.
Is there a downside?
Predicting the future in business will always be controversial. Privacy issues, manipulation of data, possible exploitation of people who do not know how they’re being manipulated may not be looked upon kindly in the social fabric of society. Explain-ability will be required to offset criticism.
The future of business has taken on a surreal quality with such enormous access to mountains of data. Looking at data from the past to predict the future is thorny, but explain-ability softens those prickly barbs, allowing businesses to see the future more clearly. Understanding modeling facilitates the development of various scenarios that management can use to take appropriate courses of actions to gain an edge over the competition. Predicting business outcomes in customer retention or acquisition, reducing marketing costs and increasing customer loyalty are just some of the areas where data analytics and predictive modeling can drive business growth.
Science fiction or new business reality?
Does explainable, predictive modeling border on science fiction? Kind of. For instance, if a future has yet to happen and the future is influenced by a million factors, how can it be that modeling, data analytics and reliance on algorithms really predict what’s to come? Everything counts. If predictive modeling gives a little or a lot of insight into the future of a business, what business owner wouldn’t accept that gift?
Businesses that don’t keep up with the changing technology are simply at a disadvantage. Every CEO in every company big or small must consider the advantages of predictive modeling, algorithms, and data analytics. The future is here, and it is a new business reality.
Srikanth Aravamuthan, Vice President, Exafluence, Inc.
About the Author:
Srikanth Aravamuthan is one of the recognized consulting professionals who has a unique blend of management consulting and analytic advisory knowledge across varied domains. He helps business leaders in building a strategic vision, He also advises and assists the organizations to be more efficient, customer friendly, cost optimized, analytically driven and digital-enabled businesses. srikanth.aravamuthan@gmail.com .
