You’ve run ads, optimized your landing page, and set up retargeting. But conversions are low, and your cost per acquisition is climbing. In many B2B campaigns, the issue isn’t your creative or your ad platform. The real problem is that you’re treating all users the same – instead of using what you already know about them. In this article, we’ll show you how first-party data and intent signals can double your conversion rate and lower your cost per lead by up to 30% – with a real-life example from our work at Todays.
What exactly is first-party data?
First-party data refers to the information you collect directly from your users – via your website, CRM, newsletter, or campaign interactions.
That includes:
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the pages they visit
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the forms they complete
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the emails they open
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their on-site behaviors
This data belongs to you. It’s not borrowed from Google, Meta, or LinkedIn. It’s part of your own digital ecosystem – and yet, it’s one of the most underused assets in B2B performance marketing.
What are intent signals?
Intent signals are behavioral clues that someone is actively researching or considering your product or service.
Clear examples include:
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repeated visits to the pricing page
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scrolling past 75% of a blog post
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viewing a case study more than once
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clicking “Request a Quote” but not submitting the form
These signals show that the user is engaged and in a decision-making mindset – and yet most campaigns ignore them.
Simulation: How Todays uses this approach for a B2B client
Step 1: Collecting first-party data
The client already has:
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blog articles on delivery process optimization
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a cost calculator on their website
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a list of leads collected from previous campaigns
Todays steps in to:
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connect all data sources (Google Analytics, CRM, website behavior) into a unified system
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tag users based on intent (e.g., “interested in pricing”, “interested in API integration”)
Step 2: Identifying intent signals
For example, a user:
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visited the site three times in one week
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read an article about warehouse automation
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used the ROI calculator
At this point, we don’t treat this person as cold traffic. They’re a warm lead and should be approached differently.
Step 3: Launching segmented campaigns
For cold users:
Awareness campaigns featuring relevant case studies, such as “How X reduced costs by 27%”
For warm users (with intent signals):
Direct-response campaigns with clear offers like “Request a free demo” or “Get your custom tech audit”
For users who abandoned forms:
Retargeting campaigns with testimonials and soft CTAs like “See what our clients are saying”
Step 4: Personalized nurturing
We build automated email flows or targeted LinkedIn ads based on user behavior:
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If they visited the API integration page → they receive a guide titled “How to integrate seamlessly without downtime”
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If they showed interest but didn’t convert → they receive a time-sensitive offer or a follow-up call
The result?
Lead acquisition becomes more cost-efficient because you’re no longer targeting at random.
Conversion rates improve because your messaging aligns with actual intent.
Campaigns become more predictable, working as a system, not a guessing game.
Conclusion: Not all campaigns fail. Only those that ignore real behavior do.
In B2B, the decision-making process is long, and trust takes time to build. But if you use the data you already have – and respond to real engagement signals – you can create campaigns that match real needs, not assumptions.
