Video summary

How to Get 10%+ Reply Rates on Cold Email

Main summary

Key takeaways

Business

Core idea (what drives high cold-email reply rates)

The speaker argues that reply rates typically plateau around 2–3% when everyone uses the same B2B lead databases (e.g., Apollo/ZoomInfo) with similar filters and shared data sources. That means recipients are increasingly “cold-email hardened” and often route/ignore messages via filters.

To reach 10%+ (with cited results of 12–13% consistently), you must change the data pool: email people from unique lead sources nobody else is emailing. This increases the odds that recipients are genuinely receptive.

Key metrics / KPIs mentioned

  • Benchmark target: 10%+ reply rate (explicit goal)
  • Examples / case studies:
    • Community audiences/campaigns: 20–30% reply rates (“cold email porn” campaigns)
    • Peptide company client study: 16% reply rate
    • Another benchmark: “about 6% reply rates consistently,” with first sale within 1,000 emails (included a webinar funnel)
    • Lower-performing but still potentially profitable baseline: 2–3% reply rates
  • Timeline milestone:
    • First sale within 1,000 emails (no exact timeframe given, but framed as a throughput goal)

Framework / playbook (implied process)

1) Problem diagnosis

  • If you use Apollo/ZoomInfo plus common filters, you’re contacting the same decision-makers everyone else contacts.
  • Those recipients get high cold-email volume and are more likely to filter messages to dedicated folders.

2) Strategy shift (the “secret”)

  • Stop relying on widely shared B2B database data.
  • Use proprietary / less common datasets so you email people nobody else is emailing.

3) Offer + message readiness

  • Continue optimizing copy and offer, but the speaker emphasizes the primary lever is:
    • unique contact data, paired with
    • an “easy yes” offer (low friction)

Actionable tactics: where to source leads instead of Apollo/ZoomInfo

1) “Local Leads” (Google Maps / local business owner emails)

Replace B2B database leads with Google Maps-local leads.

Targeting filters mentioned:

  • Industry
  • Business keywords
  • Location
  • Ratings/reviews

Lead types:

  • Owner email (named owner; aims at a personal inbox)
  • Role-based email (e.g., contact@, info@)
    • Claimed logic: more likely to reach an owner in small businesses; less likely in larger ones

Concrete example:

  • A restaurant (“Seviche”) using the owner’s name + owner email (Juan) to reach the right person directly.

Claimed uplift rationale:

  • Less frequently contacted recipients → higher likelihood of response
    • (“99% higher chance” is stated, though no detailed methodology is provided.)

2) “Creator Leads” (influencers/podcasters/YouTubers)

Use creator platforms where emails aren’t commonly derived from LinkedIn/Apollo-style datasets.

Targeting approach:

  • Find channels with a minimum subscriber threshold (example: ≥100 subscribers)
  • Filter by category (example: fitness)

Why it works:

  • Creator/community audiences are less bombarded by generic B2B cold email.

Stated use case:

  • Best for pitching creator/influencer offers (collaboration, promotion), not classic B2B lead gen.

3) “Instagram database” (scraped bios + interest signals)

Advanced tactic: scrape Instagram bios using keywords (e.g., fitness, gym, health).

  • Claimed dataset size: 1.5M+ people with those keywords in bios
  • Claimed benefit: tailor messaging based on what the lead already signals in their bio

Example angle (peptide/health-themed campaign):

  • Include a highly relevant “free welcome box” / gift
  • Ask only for a simple yes (an easy “conversion step”)

Extra “superpower” mentioned:

  • Scrape what they’re actively posting to craft a more unique message others can’t easily replicate.

4) If doing it yourself (scraping alternative data sources)

Use third-party scraping infrastructure (example given: Apify), and scrape alternative business directories such as:

  • Crunchbase
  • Clutch.co

Principle:

  • Find companies/leads present on these sites but not listed in Apollo (or not commonly targeted using standard filters).

Scalability caution:

  • Scraping at scale “can get pretty expensive.”

Operational guidance (how to improve results without “deep personalization”)

The speaker downplays:

  • Deep enrichment
  • Heavy personalization

Instead, prioritize:

  • Data uniqueness (who you email)
  • Offer ease (make the action a low-friction “yes”)
  • The usual fundamentals: copywriting and offer quality (Even so, the “secret lever” is repeatedly framed as data uniqueness.)

Practical recommendations checklist (condensed)

  • Don’t build your list solely from Apollo/ZoomInfo contacts + common filters.
  • For a 10%+ reply goal, build from:
    • Local Leads (Google Maps owner-focused lists where possible)
    • Creator Leads (YouTubers/podcasters/influencers)
    • Instagram scraped bios (interest keywords + relevant context)
  • Lead-type rules:
    • If small business: try role-based emails and/or owner contact when possible
    • If larger: try to find owner email
  • Improve conversion with an “easy yes” offer (e.g., free box/gift, webinar funnel, simple call-to-action).
  • Then optimize copy/offer based on results—but don’t expect generic B2B data to reliably hit 10%+.

Presenters / sources

  • Presenter: LeadGen Jay (speaker; references his Insiders community and coaching brand: LeadGen Jay Insiders)
  • Platform/source mentions (not presenters): Apollo, ZoomInfo, Google Maps, Consultia AI, Apify, Crunchbase, Clutch.co, Instagram, YouTube, podcasts.

Original video