Authentic Intelligence

Marketing has long been referred to as a practice that is equal parts art and science. Storytelling, creating an experience, developing campaign concepts, and branding are all art. Experimentation, technology leadership, measurement, data analysis, predictive and programmatic marketing are all science. However, one could argue that there is an art to developing a great A/B test, and there is a science—even if it is a soft science like psychology—to determine the correct brand attributes for your desired audience.

I see it as a true blend, but given today's martech stack which is central to most marketing capability, a focus on data-driven marketing approaches, and the ever-present discussion of GenAI, have we tipped the scales? Will it soon be all science and technology and no art and humans? Let's start by considering that:

Data-driven is not new to marketing

Data has been intrinsic to success for over 100 years. The Nielsen Company is a great example of data collection and analysis to help brands assess their advertising spend. Their methodology is still the gold standard for evaluating TV, radio, and newspaper audiences around the world.

Artificial Intelligence is not new

John McCarthy, aka the father of Artificial Intelligence, named it back in 1956. The American Association of Artificial Intelligence was founded in 1979. I'm sure many will remember the highly publicized Deep Blue from IBM in the mid-90s, IBM Watson, and a couple of conversational assistants named Siri and Alexa—all examples of AI. It truly feels like we should be saying, "What took so long for AI to come to marketing?"

"The Amazon Effect"

Digital marketing has already embraced recommendation software, first used by companies like Amazon and Netflix. This brought about the "you might like" era and spread "The Amazon Effect"—not just to retailers but also to B2B marketing.

Artificial intelligence (AI) has already begun transforming the digital marketing landscape by allowing businesses to capture large amounts of data, leading to data-driven marketing strategies. It also has the capability to mimic human cognitive functions such as learning and problem-solving. Through AI, a computer system uses math and logic to simulate people's reasoning to learn from new information and make decisions. AI is a powerful tool, but the key is that it "mimics" functions such as learning and problem-solving. Because of this, marketers need to use it thoughtfully. Otherwise, we risk audience trust in our brand and less engagement in meaningful dialogue.

It is clear a less thoughtful adoption of AI will only highlight an essential truth: the irreplaceable value of human intuition, judgment, and creativity in marketing.

AI technology can help optimize and speed up many different marketing tasks. When deployed strategically, it can improve customer experiences and drive conversions. Here are several specific marketing use cases that I see as greatly benefiting from AI.

1. Processing large data sets to identify market trends and patterns. AI excels at processing large data sets and spotting trends and patterns in data. It can be effectively used to gain valuable insights from data and deliver this information in a way that's easy for employees across all levels of marketing to understand and use.

2. Augment customer service agents. AI can reduce customer service agents' workload by providing quick responses to common questions and issues or properly routing calls. This leaves the agents free to deal with conversations that need a more personal or nuanced response. It also contributes to success in common top drivers of CX: answering questions faster and better, resolving problems on first contact, communicating clearly, and leaving the customer feeling respected. On the last point, machines, unlike humans, never have a bad day, so they can always be polite and consistent in their responses. Perhaps this is one of the reasons that Forrester is predicting that the global average customer experience (CX) will improve for the first time in three years — the last time that gains outpaced losses was in 2021. (Predictions 2024, 2023 Forrester Research, Inc.).

3. Track customer behavior and preferences. AI uses predictive analytics to track customer behavior and preferences, and based on the intelligence learned from previous interactions, it can predict future behavior.

4. Personalize customer experiences and recommendations. The answer to improving personalization is in AI. Machine learning algorithms enable marketers to offer a hyper-personalized customer experience by analyzing customer data. Communications on an individual level rather than the broader, more generic target groups relied on in the past are more likely to convert.