James Abbey & Michael Ketzenberg
Information and Operations Management | Retail
We’ve all been there. A lack of time results in a hurried trip through a department store. That hurried trip leads to an item that is the wrong size, color, fit, fill-in-the-blank, which inevitably leads to a return. Or perhaps it is a late-night online shopping spree. At midnight, those shoes were flawless but when they ship to you and you try them on, your toes disagree. Problems with products meeting consumers’ exact needs are seemingly endless. That endless problem is a $350B American problem and a $700B world-wide problem. The research of James Abbey, Ph.D., and Michael Ketzenberg, Ph.D., is transforming the way that business is conducted.
From insurance underwriting and forensic accounting to target marketing and cyber-security, the accumulation of behavioral and operational data is enabling better decisions that attune consumer preferences with operational capabilities. The resulting impact on society should be less waste and greater productivity.
The data analytics that Abbey and Ketzenberg are diving into enables businesses to uniquely set return policies to specific customer segments, even individuals, to optimize performance and meet consumer needs. They use data from past purchase and return transactions of customers to predict the future return behaviors. By using extremely accurate predictions, companies can prevent return fraud and set policies that are customized to their consumers.
In this way, an improvement in business decision-making will reduce waste by minimizing the disposition of unnecessary returns. Of course, waste also manifests itself in terms of higher prices and more time-consuming consumer search and return processes. People should only pay for what they need.
As it stands, most retailers have a fairly standardized return policy for all of its customers. Abbey and Ketzenberg’s research aims to ask, and answers, the question, “why should this be the case?”
Instead of having a one-size-fits all approach to setting return policies, these policies can be customized to individuals based on their behaviors and preferences, improving customer satisfaction and profitability—and reducing waste. Why should customers that never make a return have to pay for the luxury of a lenient return policy? Why force a return within 10 days when some customers need more time to deliberate? The future of predictive analytics will enable both types, as well as a myriad of others to operate at the same time.
Learn more about Abbey and Ketzenberg: tx.ag/MaysAbbey and tx.ag/MaysKetz