SendRoq

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What is SendRoq?

SendRoq is an AI-powered LinkedIn outreach automation platform that identifies target buyers based on ideal customer profiles, generates personalized messages, and manages follow-ups until replies arrive. It's designed for sales teams and agencies who prioritize reply rates and want to automate repetitive outreach work.

SendRoq pricing

Pricing model: Freemium

Starter $59/mo (3000 credits), Growth $119/mo (20,000 credits), Enterprise $249/mo (40,000 credits); 20% discount for yearly billing; free trial available

SendRoq pros

  • AI-driven profile research before outreach (analyzes posts, role, company moves, mutual connections)
  • Personalized message generation that maintains user's tone and voice
  • Automated follow-ups with human-like behavior and unified reply inbox
  • Supports multiple LinkedIn accounts (up to 10 on Enterprise plan) for scaling
  • ICP scoring and AI lead targeting (1000-12000 profiles/month depending on tier)
  • Free trial available with no credit card required

SendRoq cons

  • Monthly credit and profile limits restrict outreach scale (even Enterprise limited to 12,000 profiles/month)
  • Requires manual approval of messages for some workflows, reducing full automation
  • LinkedIn account dependency means compliance risks if LinkedIn terms of service restrict automation

Frequently asked questions about SendRoq

How does SendRoq personalize messages?

It generates personalized messages based on recipient's profile research (posts, role, company info, mutual connections) and your specified tone, with option to approve before sending or let it run automatically.

What is the typical workflow?

Three steps: describe your ideal customer in plain English, review AI-generated personalized messages, and approve for SendRoq to send and auto-follow-up until replies arrive in your unified inbox.

Who should use SendRoq?

Sales teams, agencies, and individual SDRs/founders running LinkedIn outreach campaigns who want to save time on lead research and message writing while maintaining personalization and reply rates.

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