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by Dev Ganesan
The landscape of marketing and technology has experienced a seismic shift over the past three decades. Martech platforms, the founding and rapid expansion of social media networks, Big Data, and other advancements have drastically changed how brands go to market and how B2B buyers buy.
Of course, there is one major advancement missing from that list: artificial intelligence.
For decades AI has been considered an emerging technology, but the public release of ChatGPT in November 2022 changed that. Suddenly, the average person could activate decades of prior work on advances in large language models (LLMs), machine-learning, and AI models from within their browser in a simple chat window.
The sky, it seemed, was the limit.
Fast-forward to now: one of the most common ways that marketers are using AI is to create more content. A valuable use case to be sure, but it barely scratches the surface of AI’s potential in marketing.
Let’s take a look at five additional use cases for AI that go beyond just generating content and instead help Marketing drive more high-quality leads, qualified conversations, and, eventually, revenue.
Thanks to modern systems and software, creating and publishing content is more frictionless and easier than ever—and that’s a very good thing. What’s not ideal is the rapid pace at which new content can be created.
Before long, you’re creating too much content to keep track of, and it forms a pile of unstructured data of your own making.
Content intelligence helps you understand your content ecosystem—and that starts with accurate content tagging and categorization. But it requires an immense amount of effort.
The more content you have, the more effort required, making the problem so overwhelming that companies often do a poor job, or even skip this step. The result? Marketers waste a significant amount of time trying to find relevant content for their buyers, plus personalization capabilities are severely affected.
Fortunately, AI is exceptionally good at reading huge amounts of information and processing your content. AI doesn’t just tag content faster; it can do so with a level of consistency and depth that’s simply unattainable through manual processes. `
By analyzing the context, tone, and subject matter of your content, AI can categorize it across multiple dimensions—industry vertical, buyer persona, funnel stage, and more.
The highest-quality content tagging happens when marketers guide the process. That’s why the key is marketer-augmented tagging, which combines the organization-specific knowledge of the marketer with the scale and accuracy of AI-assisted tagging. It is essential for ensuring quality content recommendations.
AI-aided tagging opens up a host of possibilities. What if your teams could instantly surface the most relevant content for a specific account, industry, or buying stage? What if your content became a dynamic, searchable resource that can be leveraged across your entire marketing and sales ecosystem?
Accurate tagging is the foundation of being able to accomplish those two possibilities.
PathFactory’s 2024 Benchmark Report found that prioritizing content tagging, especially around content formats and topics, helps marketers understand how buyer content preferences are shifting over the years, according to. Moreover, it plays an important role in content findability for both marketers and buyers and improves reporting capabilities.
Fully 71% of consumers expect companies to deliver personalized interactions, and 76% of them are frustrated when this doesn’t happen, according to McKinsey. The days of one-size-fits-all marketing are long behind us, and brands have to keep up.
Much as with content tagging, creating digital experiences that are both tailored and delivered at scale is next to impossible when attempted manually.
By using AI, dynamic webpages can adapt in real-time to each visitor. That extends beyond simple demographic data. AI can synthesize demographic and firmographic data with behavior patterns, content preferences, and engagement history to deliver a truly tailored experience.
Consider this for an ABM campaign: A potential customer lands on your website, and based on their industry, role, and previous interactions their experience changes. The messaging, case studies, and product features displayed are aligned with what they’re most likely to be interested in. As they navigate through your site, the content continues to evolve, presenting the most relevant information at each step.
That level of personalization not only improves user experience but also significantly increases conversion rates and accelerates deal cycles.
A closed-won deal can succeed or fail because of one measure: timing.
The issue of timing can’t be circumvented: Even if your marketing team has created compelling content, your sales team has nurtured the relationship, and your solution perfectly addresses the prospect’s pain points… if prospects aren’t ready to buy, they won’t.
And depending on who you ask and other factors, such as industry and audience, the issue of timing will arise often: Only about 5-10% of your TAM will be in market at any given time.
Which is why intent data can shine. Incorporating intent data into your go-to-market strategy helps narrow down that 5-10% instead of shooting blindly.
That said, as with anything in marketing and business, intent data is far from a silver bullet. It’s only in combination with all of the data at your disposal that you can get a clear picture of not just who might be interested but also who’s ready to buy right now.
By using AI to analyze a combination of first-party data (such as website interactions and content engagement) and third-party data (such as technographic information and online behavior), AI can detect subtle signals that indicate a prospect is in an active buying cycle.
The value of the data doesn’t stop there. AI can also recommend the next best action for each high-intent prospect: Should they receive a personalized content track? Be invited to an exclusive webinar? Or is it time for direct outreach from sales?
Maximizing intent signals can guide your outbound efforts. But what about when customers search you out on their own?
The B2B buying process isn’t getting any simpler. We’ve gone from the age of the single buyer to buying committees that may reach as many as 10 team members, each of whom consults at least 4-5 pieces of information as they research.
Making matters more complicated, buyers today prefer to be in control of their investigation for as long as possible: Gartner reports that 75% of B2B buyers prefer a sales experience that doesn’t involve any sales reps. Nevertheless, there will always be room for a human touch as deals get more complex.
The solution, here, isn’t to throw more cold emails and cold calls and force the conversation. Instead, brands should make it as easy as possible for buyers to get the exact information they’re looking for. AI-powered buying agents are the perfect fit for this. In short, these bots act as 24/7 virtual buyer assistants, capable of understanding complex queries, providing product information, and even guiding prospects through the initial stages of the sales process.
They are trained on and can reference your entire content library (carefully tagged and organized by AI, of course), they can train themselves on those assets to support natural language conversations, and they even recommend the most relevant assets based on the prospect’s questions and needs.
The use of such agents not only improves the prospect’s experience and self-service expectations but also frees up your sales team to focus on those high-value, complex interactions that require a human touch.
All of the above use cases are definitely impressive, but revenue intelligence is one of the more exciting AI use cases. The ability to easily and accurately report on the ROI of content on the buyer’s journey has long been a challenge for marketers.
Too many systems, too many silos, and, lately, the sheer volume of data has historically made tying content to revenue results like trying to track a string of needles in a giant pile of needles.
AI-driven solutions can make the process a lot less pointy and provide insights into how your content influences buying decisions we could have only dreamed of before.
Similar to its ability to ingest huge amounts of content and produce an output, AI can analyze patterns in content consumption, correlating them with pipeline progression and closed deals. That’s more than just tracking views or downloads or other vanity metrics; it’s the ability to really understand which pieces of content truly move the needle in your sales process.
The utility of these sorts of insights goes beyond looking at historical data. You can tune your marketing and content strategy in real-time and use AI to automate much of that for you. You (and your AI) can know which topics resonate with your highest-value prospects and accounts, which formats drive the most engagement, and which content pieces are most effective at each stage of the buyer’s journey.
The further along we get from ChatGPT’s release, the more its varied utility to organizations becomes clear. This list of use cases isn’t exhaustive by any means, but using one or all of them can give your organization a clear edge in the market.
No manual, time-consuming processes. No spending days or weeks analyzing results or updating metadata only to have the results outdated by the time you get them. No more guessing what’s working and what’s not.
Teams equipped with AI-powered tools can deliver the right content to the right buyer at precisely the right moment, all at an unprecedented scale.
So, if all you’re doing is using AI to generate blog posts in a fraction of the time, you’re missing out.
How CMOs Can Use AI to Make Career-Changing, Strategic Business Impact
Marketing at the Speed of Thought: AI Use Cases for Four Content Types
Six Ways to Use AI for B2B Growth
How to Implement Artificial Intelligence in Marketing: Rajkumar Venkatesan on Marketing Smarts [Podcast]
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Five Use Cases for AI in B2B Marketing (Beyond Content Generation)
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ABOUT THE AUTHOR
Dev Ganesan is president and CEO of content intelligence platform PathFactory, with a successful track record of building and scaling SaaS companies. He serves on the board of directors of Tradeinsight.AI and Ashley, and on the director network at Edison Partners and Northlane Capital.
LinkedIn: Dev Ganesan
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