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The artificial intelligence (AI)
industry has been leading the headlines consistently, and for good
reason. It has already transformed industries across the globe, and
companies are racing to understand how to integrate this emerging
technology, as Forbes Now reports.
Artificial intelligence is not a new concept. The technology has been with us for a long time, but what has changed in recent years is the power of computing, cloud-based service options and the applicability of AI to our jobs as marketers.
AI’s impact on marketing is growing, predicted to reach nearly $40 billion by 2025. Most CMOs are aware of AI, but many are still unsure and unaware of the magnitude of the benefits and how they can adopt AI to improve marketing.
Advances in AI now mean product developers can create innovative and leading-edge products and services that, until recently, would not have been within reach of the average marketing budget.
These new products and services entering the market make AI adoption lower risk with a focus on delivering practical and immediately impactful results. Many past attempts resulted in expensive and custom-developed marketing technology projects that left their scars.
But before marketers commit to and execute their AI strategy, they need to understand the opportunity and difference between data analytics, predictive analytics and AI machine learning.
Data Analytics
Marketing managers have readily engaged with data analytics, benefitting (and most likely suffering) from the mountains of data at their fingertips. This includes everything from user-tracking data on apps and websites, newsletter conversion rates and online advertising click-throughs, to CRM data analysis...
Predictive Analytics
Data analytics leads naturally to predictive analytics using collected data to predict what might happen. Predictions are based on historical data and rely on human interaction to query data, validate patterns, create and then test assumptions...
AI Machine Learning
Machine learning is a continuation of the concepts around predictive analytics, with one key difference: The AI system is able to make assumptions, test and learn autonomously.
Read more...
Source: Forbes Now
