Case Study: Relay’s New Prospecting Email Machine
TL;DR
Relay, a leading network of local social media creators entering their 3rd city, built a full-stack outbound engine that (1) scrapes ICP-fit accounts via Apify, (2) enriches and decision-maker maps with Clay, and (3) sends 2,000 highly personalized cold emails/month through Instantly. The system has delivered up to a 3.8% positive-reply rate (booked meetings), producing up to 24 new meetings/month and a repeatable top-of-funnel for city expansion.
Context
Relay helps great local businesses grow with a community of creators and a tight, ROI-driven playbook. To scale into new markets efficiently, Relay needed a predictable way to identify right-fit prospects, personalize at volume, and book meetings with decision-makers—without ballooning headcount.
Objectives
Precision targeting: Only message businesses that match Relay’s ICP by category, revenue band, and geography.
True personalization at scale: Reference signals that matter to each company’s likely objectives.
Throughput with deliverability: Sustain 2,000+ emails/month while protecting domain health.
Closed-loop learning: Feed outcomes back into targeting, copy, and channel strategy.
Tech Stack
Apify – Scrapes companies meeting ICP filters (profile traits, financial performance proxies, geo).
Clay – Enriches firmographics, pulls decision-makers, and generates personalized insights tied to assumed goals.
Instantly – Multi-inbox sending, warmup, rotation, sequencing, throttling, and deliverability guardrails.
CRM/Data Hub (optional) – Syncs meetings, outcomes, and suppression lists.
Workflow (End-to-End)
Define ICP & Filters
NAICS/category, local city/ZIP radius, revenue/employment bands, review velocity, ad presence, social activity, location count.
Data Acquisition with Apify
Scrape target lists from curated sources.
Normalize names/URLs; dedupe against CRM and suppression lists.
Output:
company_id, name, url, city, state, phone, source, confidence_score.
Clay Enrichment & Insight Generation
Append exec contacts (Owner/GM/Marketing/Ops).
Pull signals: recent promos, hiring, reviews, menu/site updates, content cadence.
Insight builder: “Given this company’s category, size, and signals, what’s their likely Q4 objective?”
Examples: “Lift weekday breakfast traffic,” “Stabilize staffing costs,” “Increase review volume before holiday season.”
Message Assembly & QA
Personalization tokens:
{FirstName},{City}intake trend,{1-line compliment/observation},{Assumed Objective},{Tiny Win we can deliver first},{Proof/creator stat},{2-slot CTA}.Human spot-check on the top 50 per batch before scale.
Instantly Sending & Deliverability
Rotating warmed inboxes + domain pool.
Daily caps, randomized send windows, inbox health monitoring, auto-pauses on spikes.
Automatic unsubscribe handling and hard-bounce suppression.
Reply Handling & Meeting Bookings
Positive replies routed to calendar link or human hand-off.
Auto-tag reasons for “not now,” “already have,” “wrong contact” for learning.
Push all outcomes to CRM with source/batch IDs.
Learning Loop
Weekly review: subject performance, hook lines, objective hypotheses that converted, industry segments to scale/stop.
Update ICP, angle library, and suppression rules.
Results
Volume: 2,000 cold emails/month (stable without reputation damage).
Quality: Up to 3.8% positive-reply → scheduled meetings up to (27 meetings/month).
Consistency: Performance sustained across waves by enforcing data hygiene and messaging QA.
Focus: Higher meeting density in categories where assumed objectives matched real operator priorities (e.g., weekday day-part lift, review velocity lifts before seasonal demand).
Personalization That Mattered (Patterns)
Local proof + micro-win: “Pittsburgh breakfast lift play” coupled with a specific micro-pilot offer.
Operational empathy: Referencing staffing realities, review cadence, or menu rotation—no generic “growth” talk.
Two-slot CTAs: “Tue 3:30 or Thu 10:00?” beat open-ended asks.
Short, non-salesy tone (Relay’s style).
Sample first touch (3 lines):
“Hey {FirstName}—noticed {Location} is pushing weekday mornings. We help neighbors like {Peer} lift breakfast traffic with creator-driven reels + reviews (fast, low lift). Worth a 12-min look Tue 3:30 or Thu 10:00?”
Governance & Guardrails
Compliance: Clear opt-out, honoring do-not-contact, and source transparency; respect local email laws.
Data Ethics: Use only publicly available/compliant data; promptly delete upon request.
Deliverability: SPF/DKIM/DMARC, dedicated domains, consistent warmup; suppress bounces/complaints immediately.
Brand Safety: Human QA on insights to avoid incorrect assumptions or awkward references.
What Made It Work
Right data → right angle: The “assumed objective” hypothesis unlocked credible, specific value props.
Tight message constraints: 2–4 sentences, a single micro-outcome, and a binary CTA.
Relentless hygiene: Dedupe, suppress, and audit. Bad data kills domains and confidence.
Weekly iteration: Keep the 3–5 best hooks, retire the bottom half.
Next Experiments
Multi-channel boosts: Add LinkedIn profile-view nudges or a 15-second “why now” IG reel for warm touches.
Event-based triggers: Auto-spin campaigns when reviews spike, a location opens, or hiring surges.
Offer testing: Swap “quick audit” vs “creator sprint” vs “review surge” to find category-winners.
Pricing pathways: Pre-price good/better/best in follow-ups to accelerate self-selection.
Post-meeting nurtures: Auto-send proof packs (case clips, creator rosters) if no-show or “not now.”
Implementation Notes (Reusable)
Data schema:
company_id, domain, category, geo, revenue_band, review_velocity, signals[], dm_name, dm_title, dm_email, assumed_objective, personalized_hook, batch_id.Quality gates: A/B subject caps, 10% manual review/sample, and “yellow-flag list” for risky angles.
Reporting: Daily deliverability dashboard; weekly cohort report (opens, replies, positives, meetings, wins).
