As a CMO, who has been supporting CMO’s for 25 years with hundreds of analytic and data driven marketing campaigns across multiple industries, I am all too familiar with the latency inherent in the CRM campaign process of large enterprises.
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.
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.