I’ll be blunt: you don’t fix bounces once, you prevent them and maintain that prevention. In every account I’ve taken over, the dramatic drops in hard/soft bounces never came from a single “big clean,” but from a routine.

A three-phase routine, before the campaign, during send, after the campaign, builds long-term hygiene. That cadence, more than any isolated hack, stabilizes sender reputation, keeps you in the inbox, and ultimately protects revenue.

This article is the backbone of a series on verification and list management. Here I spell out when and how to verify effectively: preparation, real-time monitoring, post-campaign cleanup, the right frequency, and the tooling (API, automation). It’s field-tested, with real cases.

My goal: you leave with a complete runbook you can put in play this week that will mechanically lower bounces without sacrificing growth.

Pre-campaign verification (preflight)

Before any meaningful send, I run a preflight. It’s your first line of defense against hard bounces (no such user, bad domain, disabled mailbox) and a big chunk of softs (chronically full mailboxes, predictable throttling when you hammer one domain, etc.).

What to pre-verify:

  • Large imports (CRM, partners, events): never blast without preflight.
  • Old segments (long-inactive): the older they are, the higher the decay risk.
  • Setup changes (new sending domain/IP, ESP migration): sanitize the base to minimize risk.

What I actually evaluate:

  • Syntax validity (RFC) to catch trivial fat-fingers.
  • Domain & MX: can this domain receive email?
  • Common typos (gmial.com, yaho.fr…) with suggested fixes.
  • Disposable / trash domains: filter them out.
  • Role accounts (info@, sales@…): exclude or route to a separate flow per policy.
  • Catch-all & risk scoring: catch-all ≠ “good.” Mark risky, not invalid.
  • Local history: if it hard-bounced with you before, it doesn’t come back.

Simple, effective method:

  1. Export the target segment with helpful metadata (source, collection date, last open/click).
  2. Batch-verify via a serious tool (I use BounceStrike, but any reputable service that returns status + reason is fine).
  3. Return three actionable buckets: valid, risky, invalid.
  4. Decide:
    • Valid → normal send.
    • Risky → light re-confirmation path or reduced cadence + sober content.
    • Invalid → hard exclude.

For volume: orchestrate by domain

The same volume hits mailbox providers differently. Profile your domain mix (gmail/outlook/yahoo + your top B2B domains). Then apply per-domain caps and time-staggering. Outcome: fewer 4.7.0 (throttling), fewer softs, smoother delivery curves.

Client story:

An e-commerce brand wanted to re-activate 600,000 sleepers. Preflight: 9.1% invalid, 13.7% risky. Instead of going full send:

  • Excluded invalids,
  • Pushed riskies through a re-confirmation tunnel,
  • Staggered valid sends over 48h per domain.

Result: hard < 0.7%, softs under control, Inbox Rate +8 points the following week. Without preflight, we’d likely have cratered IP/domain reputation for weeks.

In-flight verification (real-time monitoring)

This is the most underrated link. Teams prep well before, then fly blind during. Bounces speak in real time, that’s your chance to stop snowballing (jammed queues, waves of 4.7.0, rising complaints).

Watch live:

  • SMTP/ESP event stream: accepts, defers, rejects by code (3-digit + Enhanced Status Codes).
  • Bounces by domain per minute: if one domain goes red, slow that one, not the whole campaign.
  • Delivery latency: growing queues often foreshadow throttling.
  • Soft 4.7.0 / 421 rate: if > ~2–3% over the last 10 minutes, lower throughput for that domain.
  • Message too large (5.3.4) or mailbox full (4.2.2): lighten upcoming batches accordingly.

Automation = your guardrail

Set if/then rules:

  • Hard (5.x.x “user/domain”) → immediate suppression + source logged.
  • Soft (4.x.x) → backoff ladder (e.g., +15m, +1h, +4h, +12h, +24h), max 3–5 tries.
  • Throttling (4.7.0) → dynamic per-domain rate reduction (not global).
  • Too large (5.3.4) → targeted pause and swap to a lighter template for the rest.

Operational orchestration:

  • Per-domain queues: keep independent pipes so you can tune without punishing everyone.
  • Send windows: some providers absorb better at certain hours; if mornings are tight, shift to afternoon.
  • Transactional priority: during pressure (big launch), route transactional over a cleaner path so critical flows don’t suffer from noisy marketing.

Recent case:

A weekly newsletter had spikes of 4.7.0 at Mon 08:30 across two providers. We added minute-level monitoring, per-domain caps, and an automatic “light template” fallback when softs > 2% over 10 minutes. Soft 4.7.0 dropped from 1.9% → 0.3%. The team barely noticed, but reputation did.

Post-campaign verification (cleanup)

Post-send is upkeep. Turn signals into list actions and feedback to teams.

What I always do:

  • Normalize bounce text
    Map the zoo (“user unknown”, “no such user”…) into ~10 standard reasons (e.g., user_unknown, domain_not_found, mailbox_full, throttled, message_too_large, policy_block, auth_failed). A shared taxonomy keeps everyone sane.
  • Automated decisions by reason
    • user_unknown / domain_not_found / mailbox_disabled → delete (hard).
    • mailbox_full → grace (soft) with limited retries; if recurring across campaigns, mark inactive.
    • throttled → adjust per-domain caps.
    • message_too_large → fix template + isolate test, no mass resend.
    • policy_block / auth_failed → loop back to infrastructure (SPF/DKIM/DMARC, links, tracking) before the next blast.
  • Acquisition-source feedback
    Publish a mini-report: bounces by source (site, partner, event, import). That’s where leaks show up (partner pushing disposables, permissive forms).
  • Database updates
    • hard → status deleted,
    • recurrent soft → status to re-qualify,
    • catch-all domains → risk tag.
  • Docs & playbooks
    One incident = one page (cause, action, prevention). Avoid re-living it in 3 months.

Thanks to BounceStrike, you skip the tedious normalization/decision steps, the API does it for you.

Another case:

After a quarterly B2B campaign, 0.6% user_unknown clustered in partner-sourced contacts. The post-mortem alone was enough to renegotiate: proof of opt-in, verification at the partner’s source. Next quarter, user_unknown: 0.1%.

The right cleaning cadence

No dogma, just cadences that work. I recommend a hybrid approach: a steady base with bursts when signals flare.

My default frame:

  • Continuous (at source): real-time verification on all forms. This is the best prevention spend you can make.
  • Before every large send: preflight the targeted segment, especially when re-opening inactives.
  • Monthly: mini-clean on new, low-engagement contacts (e.g., no open/click at 30 days).
  • Quarterly: re-validate a sample of long-term inactives; progressively sunset.
  • Ad hoc: when a threshold trips (e.g., hard > 1% across 2–3 campaigns, soft 4.7.0 rising on a domain), trigger a targeted clean.

Adjustment factors:

  • B2C vs B2B: B2C personal addresses “churn” more; B2B sees turnover-driven hards after 6–12 months.
  • Acquisition channel: contests/aggressive promos → shorter cleaning intervals.
  • Country/MBP: some environments are stricter; shorten intervals if you see frequent softs with a given provider.

Tooling & APIs to automate

Without an API, verification stays artisanal. With it, it becomes a reflex, invisible to your team.

Useful building blocks:

  • Front-end validation (forms)
    • Robust RFC-aware regex (not toy patterns),
    • DNS/MX check (beware front-end timeouts, prefer a fast service),
    • Typo suggestions (“gmail.com?”),
    • Honeypot + rate limits for bots.
  • Real-time verification API (server)
    • Input: email + context (ip, user_agent, source).
    • Output: status (valid | risky | invalid), reason, risk_score, advice (accept | confirm | reject).
    • Latency < 150 ms so UX isn’t hurt.
    • Handle catch-alls with scores, not blanket “invalid.”
  • Batch preflight (pre-send)
    • Upload CSV → distributed processing → enriched CSV out.
    • Job-complete webhook + report (rates by source/domain).
  • Bounce webhooks (during/after)
    • Receive events (3-digit + Enhanced Codes).
    • Auto-map to taxonomy.
    • Rules executed online (hard delete, soft backoff, etc.).
  • Dashboards
    • Bounces by domain and by source,
    • 4.7.0 curve minute-by-minute,
    • Revenue / 1,000 inboxed (to speak business).

Guardrails:

  • Respect verification policies: no reckless SMTP pinging. Prefer non-intrusive methods and/or providers that handle SMTP dialogues cleanly.
  • Rate limit your checks (front/back).
  • Lean logs (GDPR): log the minimum, pseudonymize when possible.
  • Idempotency: processing the same event twice must not break state (e.g., deleting an already-deleted record).

The BounceStrike angle:

On a media site with heavy organic sign-ups, adding BounceStrike’s real-time API (syntax+MX+typos+disposable) blocked 2–3% of problematic emails at the source. Six weeks later, hard rate on acquisition campaigns was 3.8x lower, with zero extra workload for marketing.

If you want to try it: start with your two main forms, plug a Preflight into your next big send, and enable in-flight bounce-driven decisions. The point isn’t more dev or process, it’s moving the work to the right moment (before / during / after) and automating ~90%.

Sustained bounce reduction isn’t a stunt or luck. It’s a rhythm:

  • Block upstream (forms + preflight) anything that will become tomorrow’s hard.
  • Act in flight (per-domain throttling, smart backoff) so incidents don’t become crises.
  • Learn after (normalize bounces, fix the source, document) so it doesn’t recur.

That’s exactly what I’m democratizing with BounceStrike: a before / during / after process that lets you clean without friction and shows up in the only metric that ultimately matters: more emails in the inbox, lower costs, more revenue.