6 Emerging Uses of GenAI for B2B CMOs

As a starting point, Crestodina advises that all marketers need to train their LLMs on their target personas. He explains, “If you teach the LLM what the target audience is and train it on their information needs, then you can give it anything (an ad, webpage, keyword list, email, etc.) and it can find the missed opportunities.” 

The goal is to use the LLM to find the answers to questions like “What are the information needs of my audience? What are their hopes and dreams? What are their fears and concerns? What are their priorities? What’s the decision criteria for them selecting a company like mine?” 

Note: We’ll go deeper on training your LLM in point #6 but when in doubt, just ask your LLM for instructions on how to best use it.

Websites are unquestionably the most critical marketing asset for most B2B brands yet they are rarely tested for information gaps. And no wonder, notes Crestidina, “It’s very hard for the human brain to look at a page and identify what’s missing.” On the other hand, testing for gaps is easy peasy for LLMs, assuming you’ve done the training work discussed above. It’s as simple as uploading your homepage and asking, “Which of my audience information needs are not met?”

Marketers typically develop content strategies with input from sales, product teams, and perhaps customers. “It’s easy to miss things,” explains Crestodina, “but AI is amazing at identifying the holes.”

In the case of content, you can use LLMs to identify all the potential information needs of your targets and discover which of these needs are least met by competitors. Of course, you can then use the LLM to help you outline and draft the missing content, keeping in mind that you’ll need to add your distinct perspective and voice to make it shine.

Given the importance of email to most marketer’s acquisition and retention efforts, finding insights to help optimize any aspect of your email efforts is a worthy pursuit. Crestodina used GenAI to discover the days and times his readers were most engaged with his emails over the last several years. He notes, “It’s almost impossible to do that analysis on your own because if you add dates and times as a secondary dimension to other analytics, it’s just a bunch of numbers, but AI does that beautifully.” 

“You just import the email analytics data into AI and ask it to identify the days/times (or other factors) that are most likely to have higher than average engagement and conversion rates,” explains Crestodina. Importantly, the data in this case comes from two separate sources (the email service provider and Google Analytics) which is why it’s so hard to assess. “You can also ask it to draw a chart to visualize the data in a way you’ve never seen it before,” he concludes.

Crestodina sees unlimited use cases for Gen AI as an analytics enhancer. Another example he shared involved assessing the performance of his LinkedIn and YouTube posts. He suggests, “Take all of your data out of LinkedIn (or YouTube) and ask your LLM to show you the topics that grow your subscriber rate the most.” 

He adds, “There are no metrics on LinkedIn or YouTube right now that show new subscribers by topic but AI organizes my content into topics and then finds the correlation between topics and new subscribers and puts it on a chart like a heat map matrix.”

In late 2023, ChatGPT added the ability for paid users to create custom GPTs. Creating a GPT is relatively simple (OpenAI offers step-by-step instructions) though it can be laborious.  Crestodina spent a weekend afternoon creating his voice GPT.

He explains, “I uploaded hundreds of past articles and transcripts from dozens of videos I’ve done and now it writes much more like me than it could in the past.” He adds, “You can also instruct your GPT to prioritize the knowledge you upload in the answers it provides to future queries.”