According to CIO Magazine, May 2nd, 2018, major brands are intensely searching for explainable AI solutions for several reasons spanning guarding against ethical and regulatory breaches to understanding when to override machine based decisions or how to improve them in the future.
Today’s digitally-empowered customers have high expectations for relevant, highly personalized customer experiences. Companies must keep pace with these growing demands or they will be left behind by their more perceptive competition.
Market segmentation is one of the most basic arms of business strategy. Firms bundle customers to understand their preferences, manage relationships with them, improve product and service offerings, and assess risk.
It’s well known that the “Amazon effect” is wreaking havoc on retailers’ ability to lure and retain shoppers because of their inability to predict what motivates customers to buy and to be relevant in the moment of interaction.
Most marketers are just beginning to explore machine learning applications. Machine learning is already providing tremendous analytic efficiency gains and increased precision.
Demand for explainable AI over the last year has started to ramp up from a variety of perspectives. DARPA, the Defense Advanced Research Projects Agency, for example, has been calling for more explainable machine learning models that human users can understand and trust.
This is a synopsis of key findings from McKinsey Global Institute’s Study: The Age of Analytics, Competing in a Data Driven World. Machine Learning’s potential in improving retail forecasting, predictive analytics, and personalized advertising, demonstrate specific business improvements.
The base-line fact: retailers and ecommerce brands have more data available to them than ever before. The data is increasing by exponential factors as more endpoints are created to track consumers and their ever-changing behavior.