For today’s marketers, data is more than just numbers on a dashboard — it’s the key to unlocking new opportunities and staying ahead of the competition. But here’s the catch: many marketing teams are flooded with disconnected data points and siloed insights, struggling to understand what truly drives customer behavior.

The key to breaking this cycle is to recognize and leverage high-quality buying signals — critical, timely indicators that can transform generic outreach into targeted, personalized interactions that drive results.

When combined with AI, these signals become powerful tools that reach potential customers much earlier in their journey — giving go-to-market teams a massive advantage over competitors who rely solely on ideal customer profile matches, traditional intent data, or account-fit scores.

Here’s how leading companies are leveraging AI to harness the right signals, develop compelling go-to-market strategies, and seize the opportunities that spur sustained growth.

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Understanding the Power of Signals

Signals are data points or indicators that provide insights into potential customer behavior or purchase intent. These could be anything from website visits, content engagement, and social media interactions, to more advanced sales signals like changes in a company’s leadership, financial performance, or public statements from executives.

“Think about signals as triggers. They allow you to kick off your marketing or your sales motions across the relevant channels you’re going to use to capture demand for your products or services,” says Millie Beetham, ZoomInfo’s senior director of GTM strategy & ZoomInfo Labs.

By recognizing and categorizing these signals, companies can identify high-priority opportunities and tailor their marketing efforts to address specific customer needs. Signals provide the context necessary for crafting personalized, engaging messaging that resonates with potential customers.

Combining first-party customer and prospect data with trusted, high-quality B2B data partners — especially providers who can incorporate an additional layer of advanced signals — takes the effectiveness of signal-based marketing to another level.

“Data at scale unlocks multi-signal plays, moving from single signals to combined insights,” Beetham says.

Harnessing AI and Signals

To maximize the potential of signals, companies also need to adopt a structured approach that includes AI tools and technologies. “AI helps predict the next best actions and develop strategies that align with customer behavior patterns,” says Deeksha Taneja, vice president of growth & optimization at ZoomInfo.

AI helps marketers not only analyze vast amounts of data, but also derive actionable insights that inform decisions. For instance: understanding the interaction between signals like news, website visits, and ad engagement can reveal which prospects are more likely to convert.

Deploying AI at scale, however, requires AI-ready data. AI’s ability to create convincing answers and suggestions with lightning speed means that bad data can essentially be weaponized, creating errors much faster than human teams can correct them.

Companies that want to leverage quality AI quickly can choose to build their own tools, or lean on experienced partners who have built flexible, powerful AI assistants, such as ZoomInfo Copilot.

Overcoming Data Quality Challenges

Implementing AI-driven strategies has several challenges, and data quality remains a critical factor in the success of any AI integration. “GTM data quality concerns can take many forms, from duplicate records to inconsistent revenue values,” Beetham says.

Poor-quality data can also lead to misguided decisions and missed opportunities, making it essential to ensure that your data is accurate, complete, and relevant. This includes standardizing records, eliminating duplicates, and continuously validating the data to maintain its quality.

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ZoomInfo’s GTM Plays: Turning Signals into Action

ZoomInfo’s own signals-based GTM Plays serves as a strong example of how to use signals effectively. The open-source playbook includes strategies for upsell, cross-sell, retention, and win-back plays.

To maximize effectiveness, marketers should map key signals to specific points in the sales funnel. By aligning signals with funnel stages, companies can better understand where a prospect or customer is in their journey.

Let’s consider a company that sells a SaaS platform for project management as an example. They might use the following signals:

Website Behavior: Tracking visits to specific product pages or the “Request a Demo” page.
Content Engagement: Monitoring downloads of whitepapers, engagement with case studies, or attendance at webinars.
Intent Data: Purchasing signals from third-party sources that indicate the prospect is researching project management solutions.

They might map these signals to the sales funnel stage and trigger actions as follows:

Awareness Stage: If a signal indicates that a prospect is consuming a lot of top-of-funnel content (like blog posts or introductory videos), this could trigger an automated email campaign that offers further educational content.
Consideration Stage: If a prospect starts engaging with product comparisons or case studies, this might trigger a notification to the sales team to reach out with a personalized offer or a demo.
Decision Stage: If a prospect visits a pricing page multiple times or downloads a detailed product guide, this could trigger an accelerated sales outreach motion or a targeted ad campaign.

“Organizing all of your signals around points in the funnel allows you to use those signals as triggers to run plays or motions across marketing and sales channels, creating demand more effectively,” Beetham says.

Instead of a one-size-fits-all marketing strategy, the company can deliver personalized messages and offers based on actual customer behavior. This alignment between marketing and sales ensures that efforts are focused on the right people at the right time, ultimately improving conversion rates and reducing wasted effort.

Achieving a Competitive Edge with Signals and AI

By integrating different types of buying signals and harnessing the power of AI, companies can gain a comprehensive understanding of their market, predict customer behavior, and implement data-driven strategies that yield measurable results.

“Combining insights from different signals to build comprehensive plays is what sets successful organizations apart,” Taneja says.

Action Steps for Marketers

To maximize the impact of your marketing strategy with signals and AI, consider the following steps:

Identify and Categorize Signals: Recognize various buying signals, like job changes, website visits, and earnings announcements, to uncover opportunities.
Implement AI Strategically: Use AI tools to analyze signals and predict customer behavior, enhancing your GTM strategies.
Maintain Data Quality: Ensure data accuracy to support effective AI integration and reliable decision-making.
Develop a GTM Playbook: Create a structured approach, like ZoomInfo’s GTM Plays, that aligns sales and marketing efforts to capture growth opportunities.

By following these steps, marketers can transform their strategies, achieve better alignment with sales teams, and drive growth in increasingly competitive markets.

“Marketers who successfully combine signals and AI gain a clear edge, enabling them to predict trends, prioritize opportunities, and deliver value at every stage of the customer journey,” Beetham says.