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Inbox overload and uninterested subscribers are big problems for marketers. Common methods like mass email campaigns or buying lists of contacts focus on getting as many names as possible. But these approaches bring in leads who aren’t a good fit—most of which don’t turn into sales or useful contacts.
Intent data fixes this problem.
It helps you find people who are actively looking for solutions, comparing different options, or getting ready to buy. This enables businesses to build email lists that actually work and are full of leads ready to take action.
This article explains how intent data works and details how to collect this data the right way, avoid mistakes, and keep up with changing privacy rules.
The Role of Intent Data in Modern List Building
Email marketing works really well when you reach the right people. Common methods like locked content (e.g., free eBooks) or mass promotional emails bring in subscribers who aren’t really looking to buy. These subscribers make your list bigger but don’t help you convert. More pressingly, they also lead to low interaction and slow sales.
Intent data looks at what people do online to determine whether they’re really interested. For example, a software company trying to reach IT managers would focus on people who check pricing info, download how-to guides, or look at competitor reviews. These actions show they’re ready to take the next step, helping marketers build lists of people more likely to buy.
Ethical Intent Data Collection
Intent data comes from two main places: Your own sources, and tools from other companies. Each one needs a different plan to make sure you use the data fairly.
Leveraging Direct Interactions
First-party data comes from sources you own, like your website or app, that follow strict privacy laws, such as the general data protection regulation. Using this data lowers risks and provides useful information to improve decision-making.
Tools like Google Analytics 4 help you closely examine your website and uncover important data like which pages they visit, how long they stay, or what they click. If someone visits the pricing page many times or spends a lot of time on tools that show cost vs. benefits, they might be planning to buy.
Locked (or gated) content like reports, customer stories, or webinar sign-ups is another effective approach. These forms collect contact info and show what topics visitors care about. For example, if someone downloads a “How to Choose a Vendor” checklist, they’re probably looking to buy.
This means they should be added to email lists—fast.
In a nutshell, email activity helps you know who to focus on. People who regularly open emails about new products or click on pricing links are more likely to buy than those who ignore emails. Explore tools like HubSpot to automatically rank these active leads and help your team contact the most interested people first.
Harnessing External Insights
Third-party intent data unlocks untapped audiences by aggregating behavioral signals across external platforms. Bombora, for instance, tracks content consumption across thousands of B2B sites, flagging companies actively searching terms like “cloud migration tools.” Paired with platforms like ZoomInfo—which layers firmographics such as job titles or company revenue—this data pinpoints decision-makers in buying mode.
But proceed with caution: Not all third-party data is created equal.
The Pitfalls of Overreliance
Marketers who overestimate third-party data accuracy will see wasted budgets and misaligned campaigns. That’s why industry professionals must build trust while keeping analytics sharp, according to Victoria Smith, head of data & analytics at Threepipe Reply.
To avoid counterproductive outcomes, you should do the following:
Audit providers rigorously and demand transparency on data sources and compliance with regulations like the general data protection regulation or the California Consumer Privacy Act.
Cross-reference signals to validate external insights against first-party data (e.g., CRM engagement, webinar attendance).
Mitigating Risks in Intent Data Utilization
Intent data carries inherent risks if misapplied. Misinterpretation or misuse can alienate prospects and waste resources.
Signal Accuracy
Not all behavioral signals indicate buying intent. Filtering irrelevant activity requires combining intent data with firmographics like industry, job role, or company size. For instance, a startup founder researching “enterprise CRM pricing” for a blog post might trigger false positives. Layering intent signals with LinkedIn profile data or company revenue filters minimizes such errors.
Contextual Analysis
Sudden interest spikes, such as “cloud security” searches post-breach, may reflect curiosity rather than purchasing plans. Historical trend analysis helps distinguish temporary interest from genuine intent. Tools like 6sense analyze search patterns over 6–12 months to identify sustained intent, reducing reliance on isolated spikes.
Adapting Intent Strategies for Future Challenges
Third-party cookie deprecation and stricter privacy laws call for modern, more efficient tactics. This has driven decision-makers to embrace new tools, technologies, and capabilities such as:
AI-Driven Predictions
Machine learning models analyze sparse first-party data to predict intent. Salesforce’s Einstein AI identifies high-potential leads based on historical engagement patterns. Studies show that companies using AI for sales predictions can get very accurate results. Many reports show these tools can be more than 75% accurate, and some even show an 82% boost in the precision of the forecasts.
Zero-Party Data
Interactive tools like quizzes or diagnostic assessments capture intent directly from consumers, fostering trust while gathering insights. Adding online quizzes can boost engagement three times more than regular content. This hands-on method gets visitors involved instead of just watching, helping them feel more included and appreciated. Because of this, time spent on the site can grow by up to 50%, as interested customers tend to stick around and look further.
Privacy-Centric Tools
Platforms like Google’s Privacy Sandbox try to hide user identities while still giving marketers useful data, though it’s unclear how well this will work in the long run. This effort marks a big change in online advertising, as it plans to remove third-party cookies but still allow targeted ads using privacy-focused tools. By using methods like federated learning and differential privacy, the Privacy Sandbox keeps user data on their own devices, lowering the chance of leaks or unwanted tracking.
Still, the idea has caused disagreement. Privacy supporters say it’s a good start but may not fully protect user privacy, since it lets some data be grouped and studied. Meanwhile, advertisers and publishers worry about losing targeting precision and insight quality, which could hurt ad income and the success of marketing campaigns.
To Sum Up
Intent data redefines email list building by emphasizing precision over volume. Marketers prioritizing signals like page visits, content downloads, and competitor research create lean, high-converting lists. Ethical first-party data collection, balanced third-party supplementation, and relevance-focused targeting from the foundation of success.
This approach demands greater effort than traditional methods but delivers measurable rewards: Improved engagement, faster sales cycles, and sustained customer loyalty. In an era of inbox saturation and privacy scrutiny, intent data proves indispensable for competitive marketing.
