Analytics & AI

AI & Analytics

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.

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.

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.

AI is a combination of technologies, and machine learning is one of the most prominent techniques utilized for hyper-personalized marketing. AI machine learning makes assumptions, reassesses the model and reevaluates the data, all without the intervention of a human. This changes everything. Just as AI means that a human engineer does not need to code for each and every possible action/reaction, AI machine learning is able to test and retest data to predict every possible customer-product match, at a speed and capability no human could attain.

Complex analysis, such as the example above, can be done instantaneously with many more variables involved, allowing the system to rapidly learn. This learning can deliver microtarget insights that could not be realistically done by human analysts across a large population. These results can dramatically improve conversion rates, marketing return on investment and customer loyalty.

It’s not a matter of one or the other — it is imperative that marketers understand the benefits and limitations of each. Simplified down:

  • Data analysis refers to reviewing data from past events for patterns.
  • Predictive analytics is making assumptions and testing based on past data to predict future what/ifs.
  • AI machine learning analyzes data, makes assumptions, learns and provides predictions at a scale and depth of detail impossible for individual human analysts.

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