Playbook

Go from 25% to 58% acceptance rates using LinkedIn intent signals

Ben layered traxy's real-time LinkedIn engagement data on top of HeyReach campaigns — and watched reply rates nearly double in two months.

Connection acceptance rate (up from ~25%)
58%
Reply rate from a single LinkedIn sender account
28%
Qualified meetings booked in 2 months from 1 profile
19

"Take your HeyReach performance into the next level and actually create value and generate the best leads from LinkedIn."

Ben Buaron
traxy
traxy

How it works

The problem: great tool, average list

Ben was running LinkedIn outreach through HeyReach before the traxy integration. The campaigns worked fine — connection requests going out, sequences running on schedule. But the results were stuck at 20–25% acceptance rates and similarly middling reply rates.

The issue wasn't the outreach. It was the targeting. He was pulling lists from LinkedIn Sales Navigator like most people do, which meant sending cold connection requests to people who matched a profile but weren't necessarily active, engaged, or thinking about anything relevant right now.

He needed a way to find people already showing up on LinkedIn and actually paying attention.

Step 1: Define who you're actually looking for

The intent-based approach traxy enables works best when you've first locked in the profile of who matters to you: the roles, the industries, the signals that indicate they're a live buyer rather than just a name on a spreadsheet. Ben's filtering wasn't just demographic — it was behavioral. People engaging with relevant LinkedIn content (competitors, industry influencers, market conversations) were the starting point.

Step 2: Connect traxy to HeyReach

The setup is a few minutes, not a few hours.

Inside your traxy account, navigate to the Integrations tab and click HeyReach. Drop in your HeyReach API key, select the campaign you want leads pushed into (or let traxy create a new one), and set delivery to auto. Save the settings.

That's the full setup. From that point forward, traxy identifies people engaging with the LinkedIn content you're tracking — your own posts, competitors' content, influencer conversations, relevant market discussions — qualifies them against your ICP, and pushes them directly into your HeyReach sequence.

The key part: outreach goes out while intent is still fresh. Not days later. Not after a manual export and re-import. Right now.

Step 3: Run the HeyReach campaign

Ben ran the campaign from a single LinkedIn sender account, capped at 27 connection requests per day — well within safe limits. The first three weeks used Sales Navigator targeting only, which created a useful before/after benchmark. When traxy came into the mix mid-campaign, the results shifted immediately.

The 45% all-time average acceptance rate includes those slower early weeks. The last 30 days came in at 56% acceptance and 27% reply rate. The last seven days hit 58% and 28% respectively.

From one profile. Two months. 19 qualified meetings.

Step 4: Let it improve over time

traxy doesn't just push leads once — it keeps learning. As it processes more of your LinkedIn activity data, its qualification gets more accurate. The lead quality compounds as the system gets smarter about which signals actually predict a good conversation for your specific ICP.

Ben's numbers were still trending up at the time of the walkthrough. That's the point.

Key takeaways

  • Targeting active LinkedIn users — people already engaging with relevant content — dramatically outperforms static list-based outreach. The signal precedes the conversation.
  • The HeyReach + traxy integration removes the manual step between intent and outreach. Most teams lose momentum between "this person looks relevant" and "we reached out." That gap closes here.
  • One sender account running at safe volume limits still produces real pipeline. Ben's 19 qualified meetings came from 27 connection requests a day — the volume wasn't the lever, the targeting was.
  • A two-phase campaign (Sales Navigator first, then traxy) gave Ben clean data to prove the lift. If you're testing a new targeting layer, the before/after structure makes the ROI obvious.

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