Data-Driven Demand Generation: Harnessing AI for Better Insights

AI is nothing new — it’s been around for decades. But until very recently, AI tools have been cost-prohibitive, requiring a significant financial investment that’s beyond the reach of most businesses.

For B2B marketers, the arrival of everyday AI couldn’t have come at a better time. Faced with economic uncertainty and ever-tightening budgets, marketers have been under constant pressure to do more with less. Now, AI presents a real opportunity to elevate B2B demand generation while driving operational efficiency. 

Let’s cover a few of the key ways AI is posed to play a pivotal role in the future of demand generation.

Accurate B2B account intelligence data

Data is the lifeblood of successful demand generation strategies. Knowing precisely who you are targeting, where they spend time, what they care about, how they engage with content, and how to reach them are the kinds of actionable insights marketers need to execute engaging, personalized campaigns. 

AI tools make it possible to quickly and accurately collect, clean, and resolve discrepancies in this wide range of B2B marketing data. Since account intelligence data is aggregated from many disparate sources in different formats, it needs to be standardized and checked for errors before teams can access and use it. While that once required time-intensive manual processes, AI is now able to streamline and automate many of these steps. 

Predictive analytics and campaign optimization

Marketers can use this information to improve their targeting strategies for demand generation campaigns. For example, predictive analytics can be used to look at existing customers and calculate their customer lifetime value (CLV), and identify similar prospects that have the potential to become your most profitable customers. That allows marketers to focus their efforts on high-value prospects, targeting them with tailored content and messaging, rather than spreading campaign resources too thin.

Marketers can also use predictive analytics to monitor and improve campaign performance in real-time. For instance, campaign managers can use predictive analytics to look at data from the first day or two of a campaign, and begin optimizing based on predictions of future outcomes and return on ad spend (ROAS) — instead of waiting weeks for more performance data to come in. 

Enhanced lead scoring and prioritization

When AI is incorporated into lead scoring, everything becomes more automated and objective. Rather than relying on subjective scores and static criteria, predictive AI models can analyze the behaviors that lead up to conversions and begin to identify nuanced patterns. Those patterns can be used to score leads dynamically, based on each lead’s interwoven behaviors, rather than treating each download or webpage visit as an isolated incident.

AI-powered lead scoring also helps identify the leads most likely to convert, so they can be prioritized accordingly by your sales team for ongoing conversations. Especially on busy teams with finite resources, this data-driven prioritization helps improve ROI and drive conversions across campaigns. 

AI-generated content

While most marketing leaders agree these tools still need human oversight to generate quality, on-brand assets, they give teams the transformative ability to go from a blank page to a first draft or early design prototype in moments. This allows teams to explore more concepts, ideate and brainstorm quickly, and spend more time on strategic and creative work rather than manual processes.

As more marketing platforms embed generative AI into their existing toolsets, it will become easier and faster for teams to customize content across campaigns. For example, email marketers are now able to generate an email draft directly in their marketing automation platform. And content marketers can customize an ebook cover with industry-specific imagery within their publishing tool. 

Unleashing AI: The Future of B2B Demand Generation