How We Automated 80% of Our Client's Outbound Process (Case Study)

In early 2025, a B2B SaaS client came to us with a predictable problem: two SDRs working full days, 15 meetings booked per month, and a cost structure that made the math painful. They were spending roughly $980 per meeting when you factored in salaries, tools, and manager time. They needed more meetings at lower cost. They also needed a system that would scale without hiring a third SDR.

This is the case study of how we built their automated outbound pipeline, what each component does, and the results three months in.

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The Starting Point

Before we did anything, we documented their existing process in detail. What we found:

Their SDR workflow before automation:

• 2 SDRs working 45 hours per week each
• Time allocation per SDR per week:

- 25 hours: Manual prospecting (Apollo searches, LinkedIn lookups, building lists in spreadsheets) - 8 hours: Data cleanup and deduplication - 5 hours: Writing and sending emails manually - 4 hours: Logging activity in HubSpot - 3 hours: Actual reply handling and meeting booking

Out of 45 hours per week per rep, roughly 38 hours -- 84% -- were going to tasks that require no judgment. Research and data work that a systematic process would handle in minutes per contact was consuming entire days.

Their monthly output:

• Contacts reached: ~800 per month (both SDRs combined)
• Reply rate: 2.8%
• Positive replies: 22 per month
• Meetings booked from positives: 15 per month (7 didn't convert due to slow follow-up, lost in inbox, wrong contact)
• Monthly cost: $14,700 (2 SDRs fully burdened + tools)
• Cost per meeting: $980

The obvious problems:

1. 84% of SDR time on administrative work
2. Losing 7 of 22 positive replies due to disorganized inbox management
3. No A/B testing -- same sequence running unchanged for 6 months
4. List quality was inconsistent -- some months the sourcing was tight, others it wasn't

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The Automation Architecture We Built

We rebuilt their outbound process in five layers. Each layer automated a specific part of what the SDRs had been doing manually.

Layer 1: Automated Lead Sourcing

Before: SDRs spent 25 hours per week manually searching Apollo, filtering results, exporting to CSV, and building lists in Google Sheets. Quality was inconsistent because what they searched for changed based on mood, available time, and whoever set up the search that week. After: We defined their ICP precisely -- SaaS companies, 50-500 employees, Series A or B funded, US-based, using Salesforce or HubSpot (indicating sales maturity and budget), with at least one SDR or BDR on staff (indicating active outbound investment).

We built an Apollo-based list-building script that runs weekly and automatically exports 400-600 new contacts matching these criteria to a staging table in their Supabase database. The script deduplicates against their existing contact history and suppression list before adding new contacts.

Time saved: 25 hours per SDR per week reduced to 0. The list shows up ready for the next stage every Monday morning. List quality improvement: Because the ICP filters are consistent and precise, list quality improved immediately. Contacts per month increased from 800 to 1,800 with the same tool budget.

Layer 2: Automated Enrichment and Verification

Before: SDRs manually verified emails using Hunter.io one contact at a time, occasionally added direct dials from LinkedIn, and frequently sent to unverified emails that bounced. After: We built a waterfall enrichment pipeline:

1. Step 1: Run all contacts through Prospeo to find LinkedIn-sourced emails. Finds verified emails for ~65% of contacts.
2. Step 2: Pass the remaining 35% through Hunter.io for domain-pattern matching. Adds another 15-20%.
3. Step 3: Run all found emails through Zerobounce for deliverability verification. Remove anything with a confidence score below 85%.
4. Final result: ~75-80% of contacts reach the verified stage. The rest are discarded rather than risking bounces.

This runs automatically each night on new contacts staged the previous day. SDRs wake up to verified, enriched contacts ready for qualification. Bounce rate before: 4.2% (domain reputation was suffering) Bounce rate after: 0.6% (well within safe thresholds)

The domain reputation recovery alone, over 60 days, improved inbox placement rates and lifted reply rates by approximately 0.8 percentage points. According to Zerobounce's 2024 Email Hygiene Report, every 1% reduction in bounce rate improves inbox placement by 2-4%.

Layer 3: AI-Powered Qualification and Personalization

Before: SDRs wrote the same 3-4 template emails to everyone. Occasionally they would manually research a company if the deal size seemed large enough, but this was inconsistent. After: We built two AI agents running in sequence for every contact that passes enrichment: Qualification Agent:

• Reads company data (size, funding, tech stack, recent job postings)
• Scores the contact against their ICP on a 0-100 scale with reasoning
• Routes contacts scoring below 60 to a lower-touch sequence (2 emails only)
• Routes contacts scoring 60-80 to the standard 5-email sequence
• Routes contacts scoring 80+ to a priority sequence with manual research flagged

Personalization Agent:

• For 80+ scored contacts: pulls company news, recent LinkedIn posts, and job posting changes, then writes a custom opening line (e.g., "Saw you just posted a VP of Sales role and expanded your BDR team -- the timing on what I'm about to say might be relevant.")
• For 60-80 scored contacts: generates an industry-specific opener using company vertical and typical pain points for that segment
• All opening lines are reviewed by the system for factual accuracy before being added to the contact record

What this produced:

• High-priority contacts (80+): 4.1% positive reply rate
• Standard contacts (60-80): 2.4% positive reply rate
• Lower-priority contacts (below 60): 0.9% positive reply rate -- still worth sending, but with a lighter sequence

The system routes 18% of contacts to high-priority, 55% to standard, and 27% to lower-priority.

Layer 4: Automated Sequence Execution and A/B Testing

Before: One sequence, unchanged for 6 months. No testing. Emails sent manually in batches by SDRs. After: Instantly.ai configured with:

• 4 active sequences running simultaneously, each targeting a different ICP segment
• 2 variants per sequence: different subject line, different opening paragraph
• Automated A/B testing: after 100 emails sent per variant, Instantly automatically identifies the higher-performing variant and routes remaining contacts to it
• Daily sending limits: 35 emails per inbox, across 6 sending domains (210 emails per day maximum)
• Volume ramp: We started at 50/day in week one, hit full speed by week six

Sending schedule:

• Emails go out Monday through Thursday only (Friday has 20-30% lower open and reply rates per Salesloft's 2024 data)
• Send window: 7am-11am in recipient's timezone
• Automatic follow-ups at day 3, day 7, day 12, and day 18 if no response

Sequence structure (standard priority):

• Email 1: Hook on company-specific angle or industry trigger, 85 words, one soft question
• Email 2: Social proof with a relevant client result, 70 words, direct ask for 15-minute call
• Email 3: Challenge framing (here is the problem companies like yours face), 90 words, offer to share a framework
• Email 4: Quick bump, 30 words ("Did this get buried?")
• Email 5: Final break-up email, 40 words, leaves the door open

The combination of better copy structure, A/B testing, and improved deliverability (from the verified lists and proper warmup) lifted overall reply rate from 2.8% to 4.3%.

Layer 5: Reply Handling and Meeting Booking Automation

This was the biggest single improvement. The client was losing 32% of their positive replies due to slow response, inbox chaos, and inconsistent handling.

Before: Two SDRs shared an inbox, manually read every reply, decided on a response, typed it out, and hoped the prospect was still interested by the time they got back. After: A reply classification agent reads every incoming reply within 15 minutes of receipt and:

• Positive: Creates a Slack notification with the reply text and prospect company brief, auto-sends a response with a Calendly link, marks the deal in HubSpot as "Meeting Requested"
• Conditional interest / objection: Routes to a pre-written, contextually appropriate response template (we wrote 8 objection-handling responses covering their most common objections)
• Not interested: Removes from all sequences, adds to suppression list
• Not the right contact: Queries Apollo for the correct decision-maker title at that company, creates a new task for the SDR to research and re-outreach
• Out of office: Pauses all sequences for that contact until 3 days after the return date mentioned in the OOO

The Calendly integration auto-books meetings directly without SDR involvement for 70% of interested responses. The SDR only touches the meeting booking workflow for the 30% that involve back-and-forth or unusual scheduling needs. Meeting booking rate improvement:

• Before: 15 meetings booked from 22 positive replies (68% conversion)
• After: 32 meetings booked from 47 positive replies (88% conversion)

The improvement comes from both more positive replies (better targeting and sequences) and a much higher conversion rate from positive reply to meeting booked (68% to 88%).

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Results: Three Months In

Here is the full before/after comparison three months after the system went live:

By month six, with sequences further optimized based on A/B test winners and ICP refinements, meetings per month hit 38 and cost per meeting dropped to $367 -- a 63% reduction from the starting point.

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What the SDRs Do Now

This is the part that matters most for how you think about headcount.

The two SDRs did not lose their jobs. They changed jobs. Instead of spending 38 hours per week on manual data work, they now spend their time on:

• Reply handling and warm prospect nurturing: 15 hours/week each -- the 30% of interested responses that need a human touch
• Pre-call research: 8 hours/week -- going deeper on high-priority accounts before calls
• Meeting follow-through: 8 hours/week -- post-call summaries, next-step follow-up, ensuring handoff to AE is clean
• Campaign oversight: 4 hours/week -- reviewing A/B test results, flagging unusual patterns, making judgment calls on borderline suppression cases

The output improvement is not just a cost story. The client's AEs reported that meetings coming in were better qualified, the SDR handoff notes were more detailed (because SDRs now had time to write them), and the prospects who showed up to calls had warmer context because the reply-handling system engaged them within minutes of their initial interest.

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What This System Cost to Build

We want to be transparent about this. Building a system like this is not instantaneous.

Setup time: 4 weeks (discovery, ICP definition, infrastructure build, sequence writing, testing) Our fee for the build: This was part of a retained engagement. For clients who want a similar build from scratch, the setup cost is in the $5,000-$8,000 range depending on complexity. Ongoing monthly cost (tools): $1,850 -- covering Instantly ($97), Apollo ($99), Prospeo ($49), Zerobounce ($49), HubSpot Professional ($450), Calendly Teams ($20), Zapier ($49), Supabase ($25), and various AI API costs (~$1,000/month at their sending volume). Break-even vs. manual process: The improved output and lower cost per meeting produced ROI-positive results by month two.

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What Would Make Results Even Better

Three months in, these are the areas we are still improving:

ICP refinement: The qualification agent's scoring improves as we feed it feedback from which meetings actually converted to pipeline. We are building a feedback loop where closed-won data adjusts the scoring weights over time. LinkedIn layer: We have not added LinkedIn touchpoints yet. Adding a coordinated LinkedIn connection request at step 2 or 3 typically adds 15-25% more meetings from the same contact list. Intent signal layer: Adding Bombora or G2 intent data to the qualification scoring would let us prioritize contacts whose companies are actively researching relevant topics. Early tests suggest this could improve positive reply rates by another 30-40%.

For a full overview of how this fits into a broader go-to-market system, see what is a go-to-market engine. If you want to understand the AI agent layer in more depth, agentic workflows explained covers the architecture we used.

If you want a system like this built for your team, our B2B lead generation services page has the details on how we work.

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• Agentic Workflows Explained: How AI Agents Run Your Sales Pipeline
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