Marketers are missing opportunities when they use static segmentation, which is not predictive of customer behavior and fails to take customer context into account.
Most marketers are just beginning to explore machine learning applications. Machine learning is already providing tremendous analytic efficiency gains and increased precision.
Welcome to the rebirth of AI. Computational experts have taken inspiration from cognitive neuroscience for decades, but technical advances leading to the proliferation of data, efficient storage methods for it, faster processing techniques, and accessible scripting languages have brought classic algorithms to the forefront of cutting edge technology.
Machine learning helps level the playing field for all marketers if applied effectively, because it can quickly be deployed at speed and scale within critical areas of the business – such as preventing high value customers from leaving or more effectively attracting and delighting high value customers through more relevant dialogs.
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.
The General Data Protection Regulation (or GDPR) going into effect in the EU in 2018 requires that algorithms used to make credit or insurance decisions must be explainable to consumers.
For financial services firms, the fact that artificial intelligence algorithms are “black boxes” that don’t enable them to explain decisions is a major problem to due legislative requirements.
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.