The Power of Buying Signals: How We Leverage Intent Data for Precise Targeting and Messaging in Sales

At GoZupees, since we target Business-to-Business (B2B) companies, understanding buyer behavior is important for us to target our messaging correctly. 

Until a few years ago, we often relied on demographic and firmographic data, that only provided a static snapshot of our potential prospects list. 

However, with Generative AI, we now use a more dynamic and powerful approach: leveraging intent signals. 

Intent data reveals the active research and consumption patterns of potential buyers, indicating their propensity to buy our service in the near future. 

For our B2B clients too, using these signals in their marketing is no longer a luxury but a necessity for achieving precision targeting and delivering relevant, timely messaging across all channels.

So, What Are Intent Signals Exactly?

In simpler terms, intent signals are digital footprints left by individuals and accounts as they research topics, products, or services online. 

These signals indicate a level of interest or intent related to a specific need or potential purchase. 

Unlike static profile data, intent signals are dynamic, reflecting real-time shifts in buyer behavior and priorities.

We classify intent data into two main categories:

1. First-Party Intent Data: We get this data directly from their engagement with our own digital assets & content. It includes website visits, content downloads (whitepapers, case studies), webinar signups, email opens and clicks, demo requests, and interactions with our sales team. This data is highly specific and indicates direct engagement with us.
2. Third-Party Intent Data: We get this data from activities across the internet, outside of our owned properties. It includes behaviors like reading articles on popular industry blogs, their comments & interactions on relevant industry-relevant forums, visiting competitor websites, searching for specific keywords, and engaging with relevant social media content. This data provides a wider view of an account’s research journey, even before they interact with our team.

The combination of first-party and third-party data provides a holistic view of an account’s intent, allowing for a more accurate assessment of their position in the buyer’s journey.

Detailed Breakdown of Intent Signals

To truly leverage intent data, it’s helpful to understand the specific types of signals that indicate buyer interest. 

These signals can originate from various online and offline sources:

First-Party Intent Signals:

These signals come directly from interactions with your company’s owned properties and provide strong indicators of direct interest:

• Website Activity:
  • Page Views: Visiting specific product pages, pricing pages, or solution pages.
  • Time Spent: Spending significant time on key pages or sections of the website.
  • Repeat Visits: Multiple visits to the website or specific pages over a short period.
  • Navigation Path: The sequence of pages visited, indicating a research journey.
  • Content Consumption: Downloading whitepapers, e-books, case studies, or watching webinars/videos.
  • Form Submissions: Filling out forms for demos, contact requests, or content downloads.
  • Chatbot Interactions: Engaging with website chatbots, asking specific questions.
• Email Engagement:
  • Email Opens: Opening marketing or sales emails.
  • Click-Throughs: Clicking on links within emails to visit landing pages or download resources.
  • Replies: Responding to emails.
• Interactions with Sales/Support:
  • Demo Requests: Explicitly requesting a product demonstration.
  • Contact Requests: Reaching out to the sales or support team.
  • Support Ticket Submissions: While not always purchase intent, can indicate a need that might lead to an upsell or cross-sell.
  • Conversations: Content and context of interactions with sales representatives.
• Event Participation:
  • Webinar Attendance: Registering for and attending webinars related to your solutions or industry topics.
  • Virtual/In-Person Event Attendance: Visiting your booth, attending sessions, or interacting with your team at events.

Third-Party Intent Signals:

These signals come from activity across the broader web and provide insights into research happening outside your direct view:

• Content Consumption (Across the Web):
  • Reading Industry Articles: Consuming content on B2B publications, blogs, and news sites related to your industry, products, or services.
  • Viewing Product Reviews: Reading reviews or comparisons on third-party review sites.
  • Engaging in Forums/Communities: Participating in online forums, communities, or social media groups where relevant topics are discussed.
• Search Activity:
  • Keyword Searches: Searching for specific keywords related to pain points, solutions, product categories, or competitor names on search engines.
  • Long-Tail Searches: Using more specific and detailed search phrases indicating a deeper level of research.
• Website Visits (Across the Web):
  • Visiting Competitor Websites: Researching your competitors’ products, services, and pricing.
  • Visiting Partner Websites: Exploring solutions offered by your partners.
  • Visiting Industry Association Websites: Engaging with content and resources from relevant industry bodies.
• Social Media Engagement:
  • Following Relevant Accounts: Following industry influencers, thought leaders, or companies in your space.
  • Engaging with Relevant Content: Liking, sharing, or commenting on social media posts related to your industry or solutions.
  • Mentioning Relevant Topics/Keywords: Discussing relevant topics or using specific keywords in their social media posts.
• Job Postings:
  • Hiring for Specific Roles: Job postings can indicate a company’s strategic priorities and potential need for certain solutions (e.g., hiring for a “Head of Cybersecurity” might indicate a focus on security solutions).
• Technographic Data Changes:
  • Adopting New Technologies: Changes in the technologies an account is using can signal new needs or integrations required.

The strength and combination of these signals provide a more nuanced nderstanding of an account’s intent. 

For example, an account that visits your pricing page (first-party) and is also actively researching competitor solutions (third-party) is likely further along in the buying journey than an account only reading general industry articles.

Gathering and Processing Intent Data

We collect intent data using a number of technologies, strategies, and methods. 

First-party data is easy, you can typically get it through website analytics, CRM, marketing automation platforms, and sales engagement tools.

Third-party intent data is often sourced from specialized intent data providers like clearbit, 6sense and the likes. 

These companies track online activity across vast networks of websites, including B2B publishers, forums, and review sites. 

They use sophisticated algorithms and IP address matching to identify companies showing intent signals related to specific topics or keywords. 

The data is then aggregated, analyzed, and often scored based on the intensity and recency of the activity.

Processing intent data involves cleaning, organizing, and enriching the raw signals. 

This includes mapping IP addresses to specific companies, identifying the topics or keywords of interest, and assigning scores or tiers to indicate the level of intent. 

Once enriched and scored, we feed this data directly into the CRM and marketing automation platforms. 

At GoZupees, we deploy agentic AI layers on top of these platforms to turn passive data into proactive action—flagging accounts, triggering workflows, and recommending plays based on historical outcomes.

Leveraging Intent Signals for Targeting

One of the most significant applications of intent data in B2B is for precise targeting.

By identifying accounts that are actively researching solutions like ours, we focus our resources on those most likely to convert.

To do this, we divide them in 4 tiers which is a topic for another article.

Key targeting areas where we use intent data, include:

1. Identifying In-Market Accounts: Intent data helps pinpoint accounts that are actively showing signs of being in the market for a solution. This allows both our marketing and sales team to prioritize their efforts on accounts that are further down the funnel, rather than casting a wide net based solely on firmographic data.
2. Account Prioritization within ABM: For Account-Based Marketing (ABM) strategies, intent data is invaluable for prioritizing target accounts. Accounts showing high intent signals become top-tier targets, warranting more personalized and intensive outreach.
3. Segmenting Audiences: Intent data allows for dynamic segmentation of audiences based on their real-time interests. Instead of static segments based on industry or company size, we can create segments of accounts researching specific pain points, competitor solutions, or product categories.
4. Identifying New Prospects: Third-party intent data can uncover potential prospects who haven’t yet engaged with our content but are actively researching relevant topics. This expands the top of the funnel with high-potential leads.
5. Predictive Lead Scoring: We incorporate intent signals into lead scoring models to provide a more accurate prediction of conversion probability. High intent scores indicate a warmer lead, prompting faster follow-up from sales.

By focusing on accounts and individuals exhibiting strong intent, we’ve seen significant improvement in the efficiency and effectiveness of our targeting efforts, reducing wasted resources and increasing conversion rates.

Informing Messaging Across Channels

Beyond targeting, intent data profoundly impacts the messaging strategy across various marketing and sales channels. Knowing what topics an account is researching allows for the delivery of highly relevant and personalized content and communications.

Here’s how intent data informs our messaging across channels:

1. Website Personalization: When an account showing high intent visits our website, we dynamically personalize the content they see. This includes highlighting relevant case studies, displaying tailored calls-to-action, or featuring content related to the specific topics they’ve been researching.
2. Email Marketing: Intent data enables hyper-personalized email campaigns. Instead of generic newsletters, we send targeted emails addressing the specific pain points or interests indicated by their intent signals. For example, if an account is researching “AI agents,” we send them an email highlighting our agentic AI build solutions and relevant resources.
3. Digital Advertising: Intent data is a powerful tool for programmatic advertising too. While we’re not using programmatic in our business yet, we can target accounts showing intent with specific ad creatives and messaging tailored to their research topics. This ensures that those ads are seen by the right people at the right time, increasing click-through rates and reducing ad spend waste.
4. Social Media: Intent signals inform several social media engagement tactics we deploy. Our sales and marketing team monitors social conversations within target accounts that are showing intent, allowing for timely and relevant participation. In some cases we also deploy targeted social media ads based on intent data.
5. Sales Outreach: Our sales team doesn’t work alone. Powered by AI agents embedded in our CRM, we automatically generate intelligent summaries of what each account is researching, suggest opening lines, and even draft email sequences tailored to their observed behavior. For sales teams, intent data provides invaluable context for outreach. Knowing what an account is researching allows our sales reps to initiate conversations with a deeper understanding of the prospect’s needs and challenges. This enables more relevant and valuable sales conversations, moving beyond generic pitches. Sales team also references the topics the account has been researching, demonstrating that they understand the prospect’s current priorities.
6. Content Strategy: Analyzing aggregate intent data reveals trending topics and emerging interests within our target market. We use this insight to inform our content strategy, ensuring we’re creating content that addresses the current needs and research patterns of the ideal customers.

The key principle is to move from generic, one-size-fits-all messaging to personalized, contextually relevant communications that resonate with the buyer’s current research phase and specific interests.

Practical Applications and Use Cases

Let’s explore some practical examples of how some of our clients are leveraging intent data:

• Identifying Upsell/Cross-sell Opportunities: Monitoring intent signals from existing customers to reveal interest in complementary products or services, creating opportunities for upsell or cross-sell.
• Churn Prevention: A sudden increase in research activity related to competitor solutions from an existing customer signals a churn risk, prompting proactive outreach from the customer success team.
• Competitive Intelligence: Tracking intent signals related to competitor names provides insights into which accounts are evaluating competing solutions, allowing you to intervene and highlight your differentiators.
• Event Marketing: Identifying accounts showing intent related to topics discussed at an upcoming event allows for targeted invitations and personalized follow-up.
• Sales Play Prioritization: Sales leaders use intent data to prioritize accounts for specific sales plays, ensuring reps are focusing on the most receptive prospects for a particular offering.

These cases highlight the versatility of intent data in driving various marketing and sales initiatives, leading to improved efficiency and effectiveness across the board.

Challenges and Considerations

While the benefits of intent data are clear, there are also some challenges and considerations:

• Data Quality and Accuracy: The accuracy of third-party intent data depends heavily on the provider’s methodology and data sources. It’s important to evaluate providers and understand their data collection processes.
• Integration Complexity: Integrating intent data into existing marketing and sales technology stacks can be complex, requiring technical expertise and careful planning.
• Actionability: Raw intent data needs to be processed and presented in an actionable format for marketing and sales teams. This requires clear workflows and defined triggers for action.
• Privacy Concerns: As with any data collection, privacy is a concern. You have to ensure you’re collecting and using intent data in compliance with relevant privacy regulations (e.g., GDPR, CCPA).
• Over-reliance: Intent data should be used in conjunction with other data sources (firmographics, demographics, behavioral data) for a comprehensive view of the buyer. Over-reliance on intent data alone can lead to a narrow perspective.

Integration complexity is a common barrier. That’s why we’ve designed our AI agents to be modular and plug-and-play with most CRM and engagement platforms—reducing the friction between data insight and sales action.

Addressing these challenges requires careful planning, investment in the right technology, and a clear strategy for integrating intent data into existing processes.

In the end

Intent signals, especially when powered with AI have revolutionized B2B marketing and sales by providing unprecedented insight into buyer behavior. 

By understanding what accounts are actively researching, you can move beyond static segmentation and deliver highly targeted messaging across all channels. 

This leads to more efficient resource allocation, improved engagement rates, faster sales cycles, and ultimately, increased revenue. 

As the Agentic AI ecosystem continues to evolve, the ability to effectively leverage intent data will become an even greater differentiator for companies seeking to connect with their ideal customers at the moments that matter most. 

That’s why at GoZupees, we don’t just surface raw intent signals—we activate them.

Our agentic AI systems plug directly into your CRM and marketing stack, continuously ingesting both first-party and third-party intent data, scoring accounts in real time, and triggering intelligent next-best-actions—automated or human-assisted—based on your sales playbooks.

Imagine a system where the moment a target account increases its research activity around your competitor or a pain point you solve, your CRM is updated with a dynamic insight tile: “3 new signals this week – high intent on [topic] – Suggest messaging variant B3 and route to SDR for follow-up.”

Our AI agents don’t just recommend—they execute. 

From personalized outbound sequences and LinkedIn engagement to in-CRM prioritization and ABM play activation, the system becomes your co-pilot, surfacing real-time insights, nudging action, and closing the gap between signal and revenue.

This is no longer futuristic thinking. It’s how modern B2B companies are winning today—by combining intent signals with agentic execution logic that plugs directly into the systems they already use.

If your CRM is still just a static database, and your SDRs are guessing who to call next, we should talk.

Embracing an intent-driven approach is key to building stronger relationships, closing more deals, and achieving sustainable growth in the competitive B2B market.