Editorial
The Gist
- AI integration. AI in the customer journey aligns with traditional models like AIDA.
- Human insight. Despite AI advancements, human intelligence in SEO and content remains vital.
- Longevity emphasized. AIDA and Sales Funnel concepts still apply, reflecting human nature.
Some time ago, I was writing about the customer journey in the context of digital experience. We were interested in examining and measuring:
- How exactly do prospects find your brand?
- Do your best customers reply on social media for recommendations?
- Is your SEO strategy effective?
- Is paid search worthwhile for your product?
- What is the critical path to convert prospects to customers?
- How much do I need to understand about Google’s search algorithm?
The Concept of the Customer Journey Goes Back a Very Long Time
Curious about the history of the terminology, I did some research. Although there is some conjecture about when the term was first used, it seems the term “Journey Mapping” in some form, began to commonly appear sometime in the early 1990s.
Many authorities suggest Jan Carlzon’s book “Moments of Truth,” originally published in 1987, coined the term. Carlzon uses the words “customer journey” as part of an overall business philosophy, but marketers soon picked up on the concept, combining his ideas with the beloved Sales Funnel and AIDA-model.
A favorite topic in marketing textbooks, the AIDA model, Awareness, Interest, Desire and Action, goes back more than a century — conceived by Elias St. Elmo Lewis in 1898! Lewis was an advertising advocate, author, speaker and (as it turns out) a very insightful guy.
Usually, we see the words “Awareness, Interest, Desire and Action” superimposed over the familiar funnel graphic — always narrowing the journey from broad awareness to specific action — ignorance to purchase. Academics who write business textbooks have tried to update the model over the years: changing Awareness to Attention, inserting additional steps like Qualification and Presentation into the graphic, and tacking on post-Action steps like Retention, Advocacy and Loyalty. Regardless of these modifications, the essential model remains essentially intact, its longevity, proof of Elias’ remarkable insight into human intelligence and decision-making.
Related Article: Is Your Customer Journey Map Inside Out?
The Digital Age Did Little to Change the AIDA Model
Likewise, the digital age has done little to change this model. Even with the emergence of the Internet, mobile devices, and IoT collecting data constantly, the AIDA model remains relevant. Digital commerce melds with in-person commerce — channels change, human nature does not.
Generative AI Has Done Little to Change the AIDA Model — so Far
Soon after the launch of ChatGPT, McKinsey suggested how uses for the new tool would change business. They listed potential use cases for Marketing and Sales, IT/Engineering, Risk and Legal, HR and Utility/employee operations. It was a primer for those looking for suggestions, and it did a good job describing the reach of generative AI into every aspect of business.
In the months we have been reading about generative AI, however, those uses of the tool are already ubiquitous: creating content for web pages, blogs and social media; using chatbots for customer service; compiling product information, curating competitive intel, assembling product specifications; and recommending next steps in the journey. Each of these content types fit into the layers of the funnel, just as they would if purely human-generated.
Related Article: 5 CX Strategy Questions to Leverage the Future of AI
What Search Algorithms Do With AI-Generated Content
It makes sense that generative AI can enhance the selection of keywords and terms for SEO strategy, but automated generation of content has had that ability for a long time. Ranking systems, like Google’s, detect that strategy when the content itself is substandard. In other words, search algorithms know someone is trying to game the system.
Experts confirm Google can identify AI-generated content, but that does not mean there is a bias against such content. As long as the content is useful, not blatantly plagiarized, and otherwise follows their quality raters guidelines (QRG), AI-generated content is fine. In most tests, however, human editing and fact-checking make the difference in perceived quality. SearchEngineLand.com has a great article on this that goes into some detail.
Looking to Google’s Developer Documentation, we’re informed, “Google's automated ranking systems are designed to present helpful, reliable information that's primarily created to benefit people, not to gain search engine rankings, in the top Search results.”
They explain elsewhere, “Google's ranking systems aim to reward original, high-quality content that demonstrates qualities of what we call E-E-A-T: expertise, experience, authoritativeness, and trustworthiness.” In particular E-E-A-T considers the authority of the author or entity publishing the content, which should indicate that a reputable human author with byline should increase the visibility of your content.
This may be unwelcome news for CMOs hoping to replace their SEO experts with a robot, but it’s very good news for organizations who have established thought leadership through consistent creation of authoritative content.
AI-generated content can reach a level of quality deserving a high ranking, but it would seem wise to let human intelligence lead the AI when quality is important — at least in the short term.
Old Rules Still Apply With New Tools at Work
Generative AI is here to stay. As it evolves, it will continue to present benefits and challenges. There will be much to see and learn in the coming years. It is an interesting time to be alive.
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There will be changes, for certain. For now, however, it seems we stay the course, work better and faster with new tools, and stay with the model that has brought success for over a century. Elias St. Elmo Lewis would approve.
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