Many articles I’ve read recently about Artificial Intelligence (AI) suggest that marketers should be using AI and those who aren’t, are desperately behind. But what exactly is Artificial Intelligence and how can it help marketers deliver value to their organization?
These are two questions I’ve been pondering lately and I’m guessing I’m not alone in wondering just how marketers can prepare for this new technology. In this article I want to explore what AI is in the context of the marketing field. Over the next few weeks, I’ll pen additional articles that will explore the sub-categories of AI and how they are relevant, and useful, to today’s marketer.
Dictionary.com defines intelligence as “1. capacity for learning, reasoning, understanding, and similar forms of mental activity; aptitude in grasping truths, relationships, facts, meanings, etc.” Merriam Webster defines artificial intelligence as “1. a branch of computer science dealing with the simulation of intelligent behavior in computers, 2. the capability of a machine to imitate intelligent human behavior.” Putting these two definitions together, artificial intelligence is a body of knowledge that applies computer algorithms, programs and machines to problems to define relationships, solve problems and/or imitate human behavior.
The primary building blocks for Artificial Intelligence include big data, statistical models/algorithms and computing power. Recent advances in computing power and the availability of affordable data storage and access have helped accelerate AI’s proliferation.
But AI isn’t a monolith. In fact, it has distinct sub-areas – Machine Learning, Natural Language Processing, and Robotics. While I’ll dig into these in future articles, here’s a brief overview of what these terms mean.
Machine Learning: Machine Learning (ML) is a category of AI in which data scientists apply algorithms and models to a set of big data to identify relationships among the data. The system typically “learns” in an iterative approach whereby data scientists flag relevant and irrelevant outputs so that the model can better filter outputs and identify meaningful relationships and trends among the data.
Natural Language Processing: Natural language processing (NLP) is an area of artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to fruitfully process large amounts of natural language data. Challenges in NLP processing frequently involve speech recognition, natural-language understanding and natural-language generation.
Robotics: Robotics is an interdisciplinary branch of engineering, (artificial intelligence) and science … Robotics deals with the design, construction, operation and use of robots, as well as computer systems for their control, sensory feedback and information processing …These technologies are used to develop machines that can substitute for humans and replicate human actions.
There are several misconceptions about current AI, largely created by movie portrayals of intelligent machines, like Hal in2001: A Space Odyssey. Far from replacing humans in the workforce or turning against humans to rule the world, AI solutions require human intervention and are beneficial in streamlining more mundane tasks, amongst other things.
For marketers, AI has the potential to help better identify target markets, improve the impact of our campaigns and enhance the relevance of customer interactions. While the field is just getting started with AI, it is being used today in a variety of different ways. Chief among these are:
- AI-enhanced PPC advertising models that combine known data about an individual with historical behavioral data and big data to offer up the ad most likely to result in a conversion for that individual.
- AI-powered chatbots that answer online customer service questions by understanding key words in a question and providing relevant content.
- AI-powered targeting market populations that combine big data, customer demographics and firmographics with algorithms to uncover unknown prospect segments that share the business problems a specific solution offers.
Using algorithms AI-powered solutions mine data for relationships and presents the relationships discovered without any reasoning or analysis. Some of the solutions may not make sense or be relevant for the problem at hand. And so it is a human marketer that takes the raw output, and eliminates irrelevant results and selects the information that is relevant and then applies it to the problem at hand. By feeding this information back into the machine, the machine ‘learns’ thereby improving the model and outputs in the future. While this presented as the machine becoming more ‘intelligent,’ the marketer is still central to the process through interpretation, analysis, and application.