How to Balance AI and Authenticity in ABM Marketing
There are plenty of ways that ABM marketers use AI today to gain efficiency: generating email subject line ideas, reformatting creatives to fit different templates, or recording transcripts of a call. These efficiency gains just scratch the surface of what AI can really do to boost ABM performance. When marketers start working with AI as a more integrated part of their strategic initiatives, they get a lot further a lot faster. But that’s easier said than done. ABM marketers need to make sure AI doesn’t derail the company’s carefully orchestrated relationship with their customers and prospects.
Our recent B2B marketer survey reveals how ABM marketers are thinking about authenticity in the age of AI. ABM marketers chose authenticity as the most important attribute of their brand than any other at 56 percent and 59 percent consider authenticity “to a great extent” in their brand marketing. Compared to other types of marketers we surveyed, ABM marketers are much more likely to say that balancing AI and authenticity is a “tightrope walk” where many other kinds of marketers were more confident that they could absolutely balance the two.
One reason ABM marketers think the balance is tricky could be that they are considering how their company comes across to an entire buying group across the entire buying journey, where there is much more complexity to the communication. A purchase process could be months or even years long, and could include stakeholders with expertise in technology, finance, healthcare, law, and more.
In this high-stakes environment, getting the balance right really is a “tightrope walk,” but with the right strategy and the right tools, ABM marketers can reap the deeper rewards of AI.
Content Co-Pilot
One of the most popular ways for marketers to use AI is for content generation. At 66 percent, content generation is the most popular AI-driven marketing launch in 2024, according to Boston Consulting Group. However, marketers, including me, have gotten back some pretty cringey content from ChatGPT. There can be telltale phrases and overly formal language, repetition and plain falsehoods.
One thing that ABM marketers shouldn’t do is take these early missteps as a sign that generative AI doesn’t help with content generation. Yes, it’s unlikely in the next few years that ChatGPT can spit out perfectly personalized emails and blog posts for everyone in an audience segment. But it can act as a great co-pilot.
Depending on how a marketer works, AI can be used in different phases of the content process. At the beginning, it can be used to find data, citations, or examples from the web, to summarize something complex or to generate ideas for timely topics to use in outreach. Or, if that’s the part that a marketer excels at, AI can be used to clean up a draft, or provide a few different variations on the first version for different audiences.
There are some tricks to the process, many of which have been shared by marketers and writers online. For example, asking AI to rewrite something like it’s explaining it to a “drunk friend in a bar” ensures that the language is very simple and familiar.
Let AI Get to Know Each Account
Generative AI is a learning technology. The more information it gets, the more it knows. However, that can be good or bad. Imagine giving AI recipes for brownies, caesar salad and hamburgers all at the same time and asking it to come up with a new recipe. Nothing good is going to come out of that mess. But give AI a wide variety of different dessert recipes, complete with notes and feedback, and it might come up with something pretty great (raspberry cream cheese brownies anyone?)
The way this translates into actual output is to keep different sets of content separate so that AI can learn within a specific framework, and continually provide AI with feedback like "this email got a CTR of 3 percent." Marketers can feed AI content organized separately for each account so that the tool can get more and more familiar with the account details, its individual stakeholders, product use, and more. Then a marketer could take a detailed technical white paper and ask for a summary that would “work for the CEO” and get something back that was usable for that specific account, making it more authentic.
Seeing Around the Corner
Another great use case for AI in ABM is to spot patterns in the data. Bringing performance data together across different activation channels and layering AI analytics on top can help ABM marketers start to see new opportunities and evaluate performance more effectively. For example, AI-driven predictive analytics can help ABM marketers understand the importance of velocity or the strength of intent in the customer journey and better prioritize different accounts.
Analytics like these actually help ABM marketers come across as more authentic because they will be able to time and tune messages to be as relevant as possible.
The beauty of these different AI use cases is that they can improve over time. AI learns, but so do ABM marketers! Getting started now means building expertise that grows, creating a better and better partnership without sacrificing the authenticity that matters so much.
Nikki Candito is vice president of integrated marketing at Anteriad, where she is responsible for demand, operations, web, and content strategy. Prior to Anteriad, Candito led marketing teams at a number of leading technology companies with a focus on lead and demand generation.