Whitepaper

| whitepaper |

Transforming the Customer Journey with Generative AI

Strategies and techniques for marketers as they adapt to evolving consumer and B2B buyer expectations

Marketers are among the most impacted by the broad availability of generative AI tools. Indeed, these platforms and emerging dedicated tools have the potential to revolutionize the way both B2B and B2C marketers engage their customers. Potential benefits include:

augmenting human labor when writing social media copy, product descriptions, and blog posts

enhancing customers’ experiences with chatbots, and aiding agents during customer interactions

improving brainstorming of new pitches, topics, product ideas, and other efforts

supporting the analysis of qualitative data and providing recommendations for next steps

freeing up time so that marketers and others can focus on high-level tasks

THE STAKES HAVE NEVER BEEN HIGHER AS MARKETERS ADAPT

Despite these possibilities, generative AI also raises the stakes for marketers, many of whom are unsure how to use it to their advantage. In some ways, generative AI levels the playing field, giving smaller and newer companies more creative and productive capacities. Marketers in any industry who understand how to use generative AI are a step ahead of their competitors, no matter the size of their teams.

Marketers must empower themselves with a practical approach to generative AI, and a deeper understanding of its impact on their target markets. In this guide, we explore the impact of generative AI on customer expectations, its potential for the customer journey, and how marketers can use it formally to improve their marketing approach. We will discuss:

its impact on content and touchpoints within the customer journey

seven correct ways marketers can begin using generative AI today

use cases featuring success stories across three industries

five steps to incorporating generative AI into your marketing approach

WHAT MARKETERS SHOULD KNOW FIRST ABOUT GENERATIVE AI

The term generative artificial intelligence (AI) originally described technologies that use a generative adversarial network (GAN) to create original content. A GAN features two neural networks—adaptable machine learning processes that emulate the human brain—trained on large sums of data, which compete with one another in a zero-sum game that will ultimately generate its output.

The Generative Pretrained Transformer (GPT) is the latest evolutionary step of generative AI.

A Generative Pretrained Transformer (GPT) is a type of large language model (LLM) that uses deep learning to generate human-like text. They are called “generative” because they can generate new text based on the input they receive, “pre-trained” because they are trained on a large corpus of text data before being fine-tuned for specific tasks, and “transformers” because they use a transformer based neural network architecture to process input text and generate output text.

World Economic Forum, January 2023

The term generative artificial intelligence (AI) originally described technologies that use a generative adversarial network (GAN) to create original content. A GAN features two neural networks—adaptable machine learning processes that emulate the human brain—trained on large sums of data, which compete with one another in a zero-sum game that will ultimately generate its output.

The Generative Pretrained Transformer (GPT) is the latest evolutionary step of generative AI.

In these cases, humans prompt the AI with natural language to generate, evaluate, then output original content. This content can include original text, but also images, product designs, and computer code, among others.

The similarities between generative AI output and the creative output of humans are what have captivated the public. But unlike humans, “ChatGPT lacks the ability to truly understand the complexity of human language and conversation,” as The Atlantic describes. “Any responses it generates are likely to be shallow and lacking in depth and insight.”

When a person queries GPT, the answer it provides is not an answer to the query, but the simulacrum of an answer. Marketers who understand this as a foundational concept—that generative AI is actually quite shallow by human standards—will get the greatest benefits from generative AI. That’s because they will intuitively understand the “dos” and “don’ts” of its use.

With this in mind, here are some basic tips for marketers who choose to use ChatGPT or a similar tool:

Don’t copy and paste its output with only light edits; do pick what is useful for your own writing.

Don’t trust its claims and references; do use their context, then validate or evise them yourself.

Don’t ask it to “write an article”; do ask it to “draw conclusions” based on your own copy.

Understanding these principles can be a starting point for marketers as they begin using generative AI to engage their customers more effectively.

AI doesn’t understand or even compose text. It offers a way to probe text, to play with text, to mold and shape an infinity of prose… into structures in which further questions can be asked and, on occasion, answered.

Ian Bogost, Contributing Writer, The Atlantic

LEVERAGING GENERATIVE AI TO MEET EVOLVING CUSTOMER EXPECTATIONS

Critically, marketers must acknowledge that “If your content isn’t better than what AI can produce, it’s not worth making,” as Rand Fiskin at SparkToro describes. It took only five days for one million people to begin using ChatGPT upon its release, and that number is growing as new tools emerge. Now, any content producer—even those with no experience—can produce legible content. (The accuracy of that content remains in question.) In time, any person will be able to recognize raw texts and images created by generative AI as well.

Instead, marketers can identify use cases for generative AI that keep its output “in house,” improving internal processes instead. Marketing teams have already made significant headway with this approach: two-thirds of marketers use generative AI for “brainstorming sessions, first drafts and outlines,” MarTech reported in May 2023. “49% rely on AI to produce final content” as well.