[Steve Side Hustler]

Hi — I run a small business and manage my own email list. I’m not technical, but I want a reliable, low-effort way to clean contacts so I don’t waste time on bad leads or trigger spam traps.

By “spam traps” I mean addresses that can harm deliverability, and by “bad leads” I mean outdated, fake, or uninterested contacts. I’m looking for practical, safe steps I can follow.

My questions:

• What beginner-friendly AI tools or services help detect spam traps and low-quality leads?
• What simple workflow would you recommend (check, score, remove/quarantine)?
• How do I avoid removing real customers by mistake?
• Any prompts, settings, or integrations with common CRMs that work well for non-technical users?

I’d appreciate examples, short checklists, or links to tutorials that are easy to follow. Please share what worked for you and any precautions to keep deliverability and privacy safe.

[Jeff Bullas]

Nice start — wanting to remove spam traps and bad leads is the single best move for improving deliverability. Here’s a practical, low-tech approach you can start in under 5 minutes and a step-by-step plan to scale it safely.

Quick win (under 5 minutes): Export a small sample (1,000 rows) from your email list as CSV and sort by last activity. Remove addresses with hard bounces in the last 30 days and mark role accounts (info@, admin@) for review. That one action often reduces immediate risk.

What you’ll need

• Your email list CSV (email, first_seen, last_open/click, bounce history if available).
• Your email service provider (ESP) or SMTP logs.
• A simple validation tool or free online disposable-domain list, or an AI (ChatGPT) to help triage.
• A suppression list to quarantine suspected spam traps.

Step-by-step: practical workflow

1. Filter out hard bounces and recent complaints immediately. These are the highest-risk and cheapest wins.
2. Flag role accounts and department emails for low-priority campaigns or manual review.
3. Check domain validity: do MX records exist? If no, mark as risky.
4. Detect disposable domains using a list or validator; move them to a suppressed segment.
5. Use engagement signals: if no opens/clicks after 6–12 months, run a re‑engagement campaign — don’t delete right away.
6. Feed suspicious records into an AI or validation API for a second opinion (see prompt below).

Copy-paste AI prompt (use in ChatGPT or your LLM):

“I have a CSV with columns: email, first_seen_date, last_open_date, last_click_date, total_sends, total_bounces, domain, MX_valid (yes/no), role_account (yes/no), disposable_domain (yes/no). For each row, label it ‘good’, ‘risky’, or ‘spam_trap’ and give a one-sentence reason. Prioritize: hard bounces and known disposable domains = spam_trap; role accounts & no engagement = risky. Show 3 example rows and the labels.”

Example outcome

• john@example.com — good — recent opens and clicks.
• admin@old-domain.com — risky — role account, no opens in 14 months.
• user@disposablemail.xyz — spam_trap — disposable domain and prior bounces.

Common mistakes & fixes

• Deletions too fast — Fix: re-engage inactive users with a 3-step win-back before deleting.
• Relying on one signal — Fix: combine bounce, domain, and engagement to decide.
• Legal/privacy slip-ups — Fix: keep consent records and respect opt-outs.

Action plan (next 30 days)

• Week 1: Remove hard bounces and complaints; run quick disposable-domain filter.
• Week 2: Re-engagement for 6–12 month inactives; flag role accounts.
• Week 3–4: Use AI or a validation API for a second pass; build an ongoing suppression list.

Closing reminder: Test changes on small segments, measure deliverability (opens, bounces), and iterate. Small, consistent cleanups beat one big purge every year.

[Fiona Freelance Financier]

Quick win (under 5 minutes): Export a 1,000-row sample, filter for hard bounces in the last 30 days, and add those addresses to a suppression list. You’ll immediately cut your highest-risk senders and calm deliverability alarms.

Nice concise checklist in your original post — I especially like the emphasis on hard bounces and role accounts. Building on that, here’s a calm, repeatable routine you can use to let AI help without over‑relying on it.

What you’ll need

• Your CSV export (email, first_seen, last_open, last_click, total_sends, total_bounces, complaints if available).
• Access to your ESP or SMTP logs and a suppression list you can update.
• A disposable-domain list or validator and an MX-check tool (many free checkers exist in dashboards).
• Optional: an AI or validation API to help triage higher-volume uncertainty — use it as a second opinion, not the only decision-maker.

Step-by-step: a simple scoring workflow

1. Export and clean: remove obvious duplicates and normalize emails (lowercase, trim spaces).
2. Apply hard rules: immediately suppress hard bounces, known complaints, and addresses on disposable-domain lists.
3. Enrich basic signals: check MX existence, mark role accounts, and calculate inactivity (months since last open/click).
4. Score each address with a small rule set (example: hard bounce+high weight, disposable+high, no MX+medium, role+low, long inactivity+medium). Use the total to bucket into Keep / Re‑engage / Suppress.
5. Run a small re‑engagement for the ‘Re‑engage’ bucket (3 short, polite touches over 2–4 weeks). Move non-responders to Suppress rather than outright delete — keep an audit trail.
6. For the top uncertain rows, run a validation API or ask an AI for a one-line reason per row and review a sample manually before bulk actions.

What to expect

• Immediate drop in bounce and complaint rates after suppressing hard bounces and disposables.
• A temporary hit to open counts from removing noise, but steady long-term deliverability gains.
• Safer sending reputation if you automate this weekly or biweekly and keep a small manual review pool.

Quick safeguards & tips

• Don’t delete until you’ve tried a re‑engagement sequence; keep consent records.
• Test changes on small segments and monitor bounces/complaints for one send cycle before scaling.
• Keep a rolling suppression list and a separate ‘manual review’ tag for suspicious role accounts or valuable-but-inactive contacts.

Small, regular routines reduce stress and give you predictable improvements: suppress the obvious risks quickly, score the uncertain ones, re‑engage before you remove, and automate the rest.

[Jeff Bullas]

Quick win (2 minutes): Export 1,000 rows, sort by total_bounces and last_open_date, and immediately add addresses with a hard bounce or a complaint in the last 30 days to your suppression list. You’ll stop the highest-risk sends right away.

Context: you already have a solid routine. Use AI as a smart second opinion — not the decision-maker. Below is a practical scoring system, step-by-step actions, an AI prompt you can paste, examples, common mistakes and a 30-day action plan.

What you’ll need

• CSV export: email, first_seen_date, last_open_date, last_click_date, total_sends, total_bounces, complaints, domain, MX_valid, role_account, disposable_domain.
• Access to your ESP for suppression updates and sending small re-engagements.
• Simple validator (MX check) or a disposable-domain list and optional AI/validation API.
• A suppression list and a manual review tag.

Step-by-step: scoring & action

1. Clean and normalize: lowercase emails, remove duplicates, trim spaces.
2. Hard rules first: suppress immediately if total_bounces >=1 with hard bounce flag, or complaints >0, or disposable_domain = yes.
3. Enrich: check MX_valid, mark role_account, compute inactivity_months = months since last_open_date.
4. Score each row (example weights): complaints=60, hard_bounce=50, disposable=45, no_MX=30, inactivity>12m=20, role_account=10.
5. Bucket: score >=50 = Suppress; 25–49 = Re-engage (3-touch winback); <25 = Keep for normal sends.
6. Run re-engagement: 3 short emails over 2–4 weeks. Non-responders → Suppress (don’t delete; keep audit).
7. For uncertain rows, run an AI validation or API and manually review a sample (100 rows) before bulk changes.

Scoring example

• john@example.com — no complaints, no bounces, MX ok, last_open 1 month → score 0 → Keep
• admin@old-domain.com — role_account(10) + inactivity 14m(20) + no_MX(30) = 60 → Suppress (or manual review if high value)
• user@disposablemail.xyz — disposable(45) + hard_bounce(50) = 95 → Suppress

Copy-paste AI prompt (use in ChatGPT or your LLM):

“I have a CSV with columns: email, first_seen_date, last_open_date, last_click_date, total_sends, total_bounces, complaints, domain, MX_valid (yes/no), role_account (yes/no), disposable_domain (yes/no). For each row, return a JSON list where each item has: email, score (0–100), label (Keep / Re-engage / Suppress), reason (one sentence), suggested_action (one line). Use weights: complaints=60, hard_bounce=50, disposable=45, no_MX=30, inactivity>12m=20, role_account=10. Show 3 example rows and their outputs.”

Common mistakes & fixes

• Deleting too fast — Fix: always run a 3-step re-engagement before permanent deletion; keep consent records.
• Using a single signal — Fix: combine bounces, domain checks and engagement into a score.
• Blindly trusting AI — Fix: treat AI as a second opinion; sample-check 100 rows before bulk actions.

30-day action plan

• Week 1: Suppress hard bounces/complaints/disposables; normalize list; run MX checks.
• Week 2: Score full list, segment Keep/Re-engage/Suppress, start re-engagement for Re-engage bucket.
• Week 3: Review re-engagement results, move non-responders to Suppress, run AI on uncertain pool.
• Week 4: Automate weekly scoring and suppression; keep a 100-row manual review sample each run.

Closing reminder: small, regular cleanups protect your sending reputation. Start with the quick win now, then automate the scoring and use AI as a helpful assistant — not the final judge.

[aaron]

Fast win (3 minutes): In your ESP, filter the last two campaigns for addresses with a hard bounce OR a complaint OR no MX record, then bulk-add them to Suppression. Next, create a tag “Recheck-30” for any address with zero opens across 8+ sends. That one-two move lowers trap risk and stabilizes sender reputation immediately.

Your weighted scoring is on point — especially giving the heaviest weight to complaints and hard bounces. Let’s layer in two high-yield tactics AI handles well: domain-cohort risk and pattern anomalies that catch traps and toxic leads before they bite.

Why this matters now

• Trap hits are silent but expensive: they tank inbox placement for weeks.
• Bad leads inflate list size, depress engagement, and get you throttled.
• AI can spot risky clusters (by domain, local-part patterns, and velocity) faster than manual checks.

Lesson from the field: Lists don’t fail from one bad address — they fail from patterns you miss. The fix is a two-pass system: rules first, AI patterning second, then a low-risk re-engagement before suppression.

What you’ll need

• CSV with: email, domain, first_seen_date, last_open_date, last_click_date, total_sends, total_bounces (hard/soft), complaints, MX_valid, role_account, source (lead form/import), created_at.
• ESP access for suppression lists, tags, and segment sends.
• Basic MX check and the ability to export per-domain stats.

Step-by-step: rules + AI patterning

1. Normalize: lowercase emails, trim spaces, dedupe. Tag records with their acquisition source (web form, import, event).
2. Apply hard rules (immediate Suppress): any hard bounce, any complaint, disposable domain = Suppress. No MX = Suppress unless the contact is high value → Manual Review.
3. Role and inactivity into quarantine: role_account=yes or last_open > 12 months → tag “Re-engage” (don’t delete).
4. Domain-cohort check: group by domain. If a domain shows bounce rate > 5% OR 0% opens across 200+ sends, quarantine that entire domain cohort to “Re-engage” pending review.
5. Velocity anomaly: if created_at shows a spike (e.g., 5x the daily average) from a single source and engagement is near-zero, quarantine that batch. This catches list bombs and typo traps.
6. AI pass for patterns humans miss: run the prompt below on a 2,000-row sample to label Good / Re-engage / Suppress with reasons and confidence. Spot-check 100 rows, then bulk-apply.
7. Run a trap-safe re-engagement: 3 messages over 10–14 days with a clear “Confirm you still want updates” CTA. Only keep those who open or click. Non-responders → Suppress (retain audit trail).
8. Ramp sends carefully: after cleanup, cap reactivated segments at 10–20% of normal volume for 1–2 sends to avoid reputation shocks.

Copy-paste AI prompt

“You are my deliverability analyst. I will paste CSV rows with columns: email, domain, first_seen_date, last_open_date, last_click_date, total_sends, total_bounces, hard_bounce (yes/no), complaints, MX_valid (yes/no), role_account (yes/no), disposable_domain (yes/no), source, created_at. For each row, output JSON with: email, label (Good / Re-engage / Suppress), confidence (0–100), reason (1 sentence), risk_signals (array), cohort_flags (domain_risk / velocity_spike / role / inactivity / no_mx / disposable / complaints / hard_bounce), suggested_next_action. Rules: complaints or hard_bounce or disposable = Suppress; MX_valid=no = Suppress unless role+known contact (reduce confidence); role_account or inactivity>12m = Re-engage; domain with cohort risk = Re-engage; otherwise Good. Also output a summary listing domains with >=10 rows and their aggregate open rate, bounce rate, and recommended cohort action. Return only JSON.”

What to expect

• Immediate: bounce rate and complaints drop within the next send.
• 7–14 days: inbox placement and reply rates improve; fewer throttles.
• 30 days: smaller list, higher revenue per thousand emails (RPME) and cleaner domain reputation.

KPIs to track (per send)

• Hard bounce rate < 0.5% (pause and investigate above 1%).
• Complaint rate < 0.08%.
• Re-engagement open rate > 8% (below 5% → your list is still dirty).
• RPME: trend up 10–25% after cleanup.
• Domain cohorts with 0% opens over 200+ sends → must be quarantined.

Common mistakes & fixes

• Mistake: Treating all inactives as trash. Fix: Run the 3-touch confirmation; only suppress non-responders.
• Mistake: Ignoring cohort patterns. Fix: Always review domain-level open/bounce before bulk sends.
• Mistake: Bulk reactivation at full volume. Fix: Ramp at 10–20% volume for 1–2 sends.
• Mistake: Trusting a single signal. Fix: Combine rules + AI + manual spot-check.

1-week action plan

• Day 1: Apply hard rules and suppress bounces/complaints/disposables; normalize data.
• Day 2: Run MX checks; tag roles and 12m+ inactives; build domain cohorts.
• Day 3: Use the AI prompt on 2,000 rows; spot-check 100; implement labels; set “Recheck-30.”
• Day 4: Launch 3-step re-engagement to the Re-engage segment (confirm intent CTA).
• Day 5: Review KPIs; quarantine any domain cohorts underperforming; adjust segments.
• Day 6: Ramp sends to Good segment; keep Re-engage capped at 10–20% volume.
• Day 7: Compare RPME, bounce, complaint deltas vs. last week; document rules; schedule a weekly automation.

Make this routine weekly: rules first, AI patterning second, then cautious re-engagement. Cleaner list, safer sends, better revenue. Your move.

— Aaron

[Rick Retirement Planner]

Nice follow-up — good systems catch patterns before they hurt you. Domain-cohort risk is the one concept I’d simplify for you: in plain English, it means watching groups of addresses that share the same domain (the part after the “@”). If a whole domain behaves badly — lots of bounces, zero opens, or many new signups at once — that domain can hide spam traps or toxic batches. Catching the cohort keeps one bad domain from dragging down your entire sender reputation.

What you’ll need

• CSV export: email, domain, first_seen_date, last_open_date, last_click_date, total_sends, total_bounces (hard/soft), complaints, MX_valid, role_account, source, created_at.
• Access to your ESP for creating suppression lists, tags, and small test sends.
• Basic tools: MX checker and a way to group or pivot by domain (spreadsheet, BI tool, or your ESP cohort reports).

Step-by-step: run a safe domain-cohort review

1. Normalize and group: lowercase and dedupe your list, then group rows by domain and count rows per domain.
2. Compute simple metrics per domain: open rate, bounce rate (hard bounces %), complaint rate, and number of recent signups (last 7 days).
3. Flag risky cohorts: mark domains with either (a) hard bounce rate > 5%, (b) 0% opens across >=200 sends, or (c) a signup velocity spike (e.g., 5x daily average) with near-zero engagement.
4. Quarantine, don’t delete: move flagged cohorts to a “Re-engage” or “Quarantine” tag. Run a low-risk confirmation sequence (three polite emails over 10–14 days). Only keep addresses that open or click.
5. Spot-check high-value addresses: if a flagged domain includes customers or VIPs, review those rows manually instead of bulk-suppressing.
6. Use AI as a second opinion: ask your AI to summarize domain-level patterns and list the top 25 risky domains with reasons; then manually sample 100 rows before bulk action. Treat AI suggestions as guidance, not gospel.

What to expect

• Immediate reduction in bounce and complaint volume after quarantining cohorts.
• Short-term drop in raw opens (you’ve removed noisy non‑openers) but steady improvement in inbox placement and engagement rates over 2–4 weeks.
• Ongoing: schedule this cohort check weekly and keep a 100-row manual review sample each run so you build confidence in automated decisions.

Small, regular checks of domains and unusual signup spikes are the simplest way to stop traps quietly and protect your reputation. Start with the quick cohort report today and run the safe re‑engagement next — you’ll see cleaner metrics within a week.

[Author]

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