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.
Not being able to explain why a machine is predicting what it is predicting is a big issue for consumer-facing companies that need to be competitive by using machine learning, but need it to be explainable for complying with legislation and general needs to be accountable.
If your company isn’t using machine learning to detect anomalies, recommend products or predict churn it will be soon. However, understanding the “why” behind the “what” is another critical component of Artificial Intelligence. Trust and transparency are absolutely critical in a world of ML and AI.
The priorities, initiatives and benefits of Machine Learning are reviewed. Early adopters are realizing benefits and gaining competitive advantage in the markets they operate in. 50% of people planning to use machine learning identified a better understanding of customers and prospects as their number one reason.
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.
The number one trend identified in the 2017 Retail Banking Trends and Predictions was a renewed focus on the customer experience. Personalizing customer communications at scale, and leveraging AI to maintain a competitive edge along with significant efficiency gains are discussed.