Marketers often confuse the terms machine learning, automation, and artificial intelligence (AI), even using them interchangeably. They are interrelated but distinct, and those distinctions are important to consider.
Understanding the nuances of these intelligence concepts can inform smart marketing decisions — and lead to solutions that meet business goals.
What’s in a Name?
Machine learning, automation, and AI should be defined separately, but their capabilities build upon each other for marketing.
Machine learning is a collection of methods that allows a computer program to improve the accuracy of its predictions over time as more observations or data are input.
Use case: If the marketer’s objective is to better understand or predict an outcome, machine learning can be a very valuable approach. (However, machine learning can also be achieved by an analyst sitting at her computer powered by statistical software.)
Automation is the set of rules and feeds that allow for the automatic and ongoing input of data as well as the automatic and ongoing output of scores or predictions coming from machine learning methods.
Use case: If the marketer’s objective is to set up a system of learning that provides continuous predictions at scale as new data becomes available, then both machine learning and automation are needed.
AI includes both machine learning and automation, as well as a final component of automated action.
Use case: If the marketer’s objective is to set up an automated system of ever-improving learning and activation, then true AI is necessary.
Think of machine learning as the brain and AI as the brain plus the arms, legs, and voice. Full AI includes the automation to enable the thinking and acting to occur without explicit human action or intervention.
Leveraging AI for Effective Marketing
Most marketing situations are unique, making it difficult for a full-blown AI application to come perfectly formed out of the box. Although many marketing technology companies are touting their “built-in” AI platforms, in reality, what’s built in are some common machine learning methods and some configurable automation.
By using those technologies, a marketer can achieve true AI application, but it typically requires a lot of outside partner expertise to configure and maintain the automation, activation, and validation of performance.
In a few specific areas where the marketing challenge is discrete, out-of-the-box AI applications can be successful. For example, AI has found good success with chatbot and automated customer service interactions. AI technologies can add tremendous efficiency and scale to natural language parsing, learning, and interaction in those situations that previously required human intervention to promote a satisfactory customer experience.
Staying Informed About AI
The field of AI and its application to marketing is fast evolving — but there is also a lot of hype and confusion.
Marketers can cut through the noise by getting information from objective, third-party sources such as Forrester and Gartner. University thought leadership is also a fair and reliable source. Sources that aren’t financially incentivized to review or provide product information are the best bet.
Organizations and agencies can also benefit from consulting a trusted data scientist. This professional should provide real-world examples and an honest assessment of which technologies are legit, and which ones are hopping on the AI bandwagon by repackaging or renaming their tool.
Intelligent Approach
AI has become such a hot topic that technology sales teams often blur the true capability of their products in order to capitalize on the trend. There are also cases where simple logic rules are passed off as AI to uninformed buyers.
The best advice for marketers? Gather information so you can make educated decisions about intelligence.
