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Sales / SDR

SDR Pipeline Automation

A B2B SaaS company ($50M ARR, 15-person SDR team)

3.2x increase in qualified meetings with 60% less SDR time spent on research

KEY RESULTS

3.2x
Increase in qualified meetings per SDR. From 8/month to 26/month average.
81%
Improvement in email response rates. Personalization at scale actually works.
$378K
Pipeline generated per SDR per month, up from $180K. Same headcount, 2.1x output.
8 weeks
SDR ramp time, down from 6 months. AI handles the research learning curve.

Before & After

✗ Before

  • •Research time per prospect: 45 minutes
  • •Email response rate: 2.1%
  • •Qualified meetings per SDR per month: 8
  • •Pipeline generated per SDR: $180K/month
  • •SDR ramp time: 6 months to full productivity
  • •SDR turnover: 45% annually

✓ After

  • •Research time per prospect: 8 minutes
  • •Email response rate: 3.8% (81% improvement)
  • •Qualified meetings per SDR per month: 26
  • •Pipeline generated per SDR: $378K/month
  • •SDR ramp time: 8 weeks to full productivity
  • •SDR turnover: 22% annually

The Challenge

This B2B SaaS company targeted enterprise accounts—complex sales requiring deep account research before outreach. Their 15 SDRs spent 3+ hours daily on manual prospect research, leaving little time for actual selling. Response rates were stuck at 2% because outreach felt generic, not personalized.

PAIN POINTS

  • ×SDRs spent 45 minutes researching each prospect—LinkedIn, company news, tech stack, recent funding—before writing one email
  • ×Generic outreach templates produced 2.1% response rate despite high activity volume
  • ×Top performers were 5x more productive than average—but the firm couldn't figure out what they did differently
  • ×CRM was 30% stale: wrong contacts, old titles, dead emails wasting SDR time
  • ×45% annual turnover meant constantly training new reps who took 6 months to ramp

Technology Stack

  • →Prospect enrichment pulling from 20+ data sources (LinkedIn, news, tech installs, job postings)
  • →AI writing assistant generating personalized first lines and value props
  • →Lead scoring model trained on 3 years of conversion data
  • →CRM enrichment and hygiene automation
  • →A/B testing framework for messaging optimization

Implementation Approach

1

Phase 1: Prospect Intelligence Layer (Weeks 1-3)

Built automated enrichment pulling from 20+ sources: company firmographics, recent news, funding events, job postings (signal of growth), tech stack (from job listings and technographic data), and LinkedIn data. A prospect profile that took 45 minutes to build manually now generates in 30 seconds.

2

Phase 2: Personalized Outreach Generation (Weeks 4-6)

Trained AI to generate personalized first lines and value propositions using prospect context. Not mail merge ('Hi {FirstName}')—genuine personalization based on recent company events, role responsibilities, and likely pain points. A/B tested against templates: 81% higher response rates.

3

Phase 3: Lead Scoring & Prioritization (Weeks 7-8)

Built scoring model on 3 years of closed-won and closed-lost data. Identified signals that predict conversion: company growth indicators, tech stack compatibility, decision-maker tenure, engagement patterns. SDRs now work accounts most likely to convert, not just most recently added.

4

Phase 4: CRM Automation & Insights (Weeks 9-12)

Automated CRM hygiene: validate emails, update titles, flag churned contacts, merge duplicates. Built analytics identifying what top performers do differently: timing, subject lines, follow-up cadence. Codified winning patterns into playbooks for the team.

Results Breakdown

3.2x

Increase in qualified meetings per SDR. From 8/month to 26/month average.

81%

Improvement in email response rates. Personalization at scale actually works.

$378K

Pipeline generated per SDR per month, up from $180K. Same headcount, 2.1x output.

8 weeks

SDR ramp time, down from 6 months. AI handles the research learning curve.

Key Learnings

  • 1.Personalization at scale is the unlock. The math never worked before—45 minutes per prospect meant only top accounts got real personalization. Now everyone does.
  • 2.Lead scoring prevents wasted effort. SDRs used to treat all leads equally. Prioritization means working smarter, not just harder.
  • 3.CRM hygiene is foundational. 30% stale data meant 30% wasted outreach. Clean data compounds—every email actually reaches someone.
  • 4.Top performer patterns can be taught. Once we identified what worked, we systematized it. The gap between best and average collapsed.
  • 5.Turnover dropped because the job got better. Research grind → strategic selling. SDRs became closers, not researchers.

Industry Context

Research shows AI can save recruiters 4.5 hours per week on repetitive tasks. Companies using AI for personalization see 6-10% revenue increases (Gartner). The SDR role is evolving: AI handles research and initial drafting; humans handle relationship building and complex objection handling. Teams that don't automate research are competing with one hand tied behind their back.

TIMELINE

60 days from audit to full rollout

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