AdTech and MarTech SaaS companies process massive volumes of campaign events, advertiser data, and attribution signals — often in real time. Manual ops don't scale when you're handling thousands of active campaigns and dozens of advertiser accounts simultaneously.
Here are 5 n8n automations built specifically for AdTech and MarTech SaaS platforms — with import-ready JSON for each.
Why Self-Hosted n8n for AdTech/MarTech Companies?
AdTech and MarTech SaaS handle advertiser spend data (commercially sensitive), audience segments (GDPR/CCPA personal data), attribution models (trade secrets), and click fraud patterns. Routing any of this through Zapier or Make's cloud means sensitive platform data transits a third-party server.
Self-hosted n8n keeps automation logic inside your own infrastructure — no data egress, git-versionable JSON, full audit trail.
| Feature | n8n (self-hosted) | Zapier | Make |
|---|---|---|---|
| Advertiser data stays on your infra | Yes | No | No |
| High-volume event pipelines | Yes | Limited | Limited |
| GDPR Art.44-46 compliant | Yes | No | No |
| Cost at scale | ~$0/month | $$$+ | $$+ |
Workflow 1: Campaign Budget Alert & Overspend Prevention
Goal: Alert account managers before campaigns breach budget thresholds — not after the advertiser calls in.
{
"name": "AdTech — Campaign Budget Alert",
"nodes": [
{ "name": "Every 15 Min", "type": "n8n-nodes-base.scheduleTrigger",
"parameters": { "rule": { "interval": 15, "unit": "minutes" } } },
{ "name": "Query Budget Status", "type": "n8n-nodes-base.postgres",
"parameters": { "operation": "select",
"query": "SELECT campaign_id, advertiser_name, campaign_name, am_email, daily_budget, spent_today, spent_today/NULLIF(daily_budget,0)*100 AS spent_pct FROM campaign_budgets WHERE active = true AND spent_today > 0 ORDER BY spent_pct DESC" } },
{ "name": "Classify Severity", "type": "n8n-nodes-base.code",
"parameters": { "jsCode": "return $input.all().map(item => { const r = item.json; const tier = r.spent_pct >= 95 ? 'CRITICAL' : r.spent_pct >= 80 ? 'WARNING' : r.spent_pct >= 70 ? 'WATCH' : 'OK'; return { ...r, tier }; }).filter(r => r.tier !== 'OK');" } },
{ "name": "Slack #ad-ops", "type": "n8n-nodes-base.slack",
"parameters": { "channel": "#ad-ops",
"text": "{{ $json.tier }} budget alert: [{{ $json.advertiser_name }}] {{ $json.campaign_name }} — {{ $json.spent_pct.toFixed(1) }}% of ${{ $json.daily_budget }} daily budget spent" } },
{ "name": "Email Account Manager", "type": "n8n-nodes-base.gmail",
"parameters": { "operation": "send", "toEmail": "={{ $json.am_email }}",
"subject": "Budget alert: {{ $json.campaign_name }} at {{ $json.spent_pct.toFixed(0) }}%",
"message": "{{ $json.campaign_name }} for {{ $json.advertiser_name }} has spent ${{ $json.spent_today }} of ${{ $json.daily_budget }} daily budget ({{ $json.spent_pct.toFixed(1) }}%). Consider pausing or reallocating." } },
{ "name": "Log Alert", "type": "n8n-nodes-base.postgres",
"parameters": { "operation": "insert", "table": "budget_alerts" } }
]
}
Why it matters: Overspend is the #1 advertiser complaint. An automated 80% warning gives account managers time to act before crossing the 100% threshold — preventing relationship damage and chargeback disputes.
Workflow 2: New Advertiser Onboarding & Activation Drip
Goal: Guide new advertisers from signup to first live campaign with automated touchpoints — no manual follow-up needed.
{
"name": "AdTech — Advertiser Onboarding Drip",
"nodes": [
{ "name": "New Advertiser Row", "type": "n8n-nodes-base.googleSheetsTrigger",
"parameters": { "sheetId": "YOUR_SHEET_ID", "triggerOn": "rowAdded" } },
{ "name": "Day 0 Welcome + API Key", "type": "n8n-nodes-base.gmail",
"parameters": { "operation": "send", "toEmail": "={{ $json.email }}",
"subject": "Welcome to [Platform] — your API key and quick-start guide",
"message": "Hi {{ $json.advertiser_name }}, here is your API key: {{ $json.api_key }}. First campaign checklist: 1) Upload your creative 2) Set targeting 3) Set your daily budget." } },
{ "name": "CSM Slack DM", "type": "n8n-nodes-base.slack",
"parameters": { "channel": "#advertiser-success",
"text": "New advertiser: {{ $json.advertiser_name }} ({{ $json.company }}) — check in by Day 3" } },
{ "name": "Wait 3 Days", "type": "n8n-nodes-base.wait",
"parameters": { "amount": 3, "unit": "days" } },
{ "name": "Day 3 — First Campaign Tips", "type": "n8n-nodes-base.gmail",
"parameters": { "operation": "send", "toEmail": "={{ $json.email }}",
"subject": "3 campaign settings that 2x ROAS for new advertisers",
"message": "Hi {{ $json.advertiser_name }}, here are the top 3 settings our best-performing advertisers configure in week 1: bid strategy, audience targeting, and frequency caps." } },
{ "name": "Wait 4 Days", "type": "n8n-nodes-base.wait",
"parameters": { "amount": 4, "unit": "days" } },
{ "name": "Day 7 — Optimization Guide", "type": "n8n-nodes-base.gmail",
"parameters": { "operation": "send", "toEmail": "={{ $json.email }}",
"subject": "Your first 7 days on [Platform] — what to optimize next",
"message": "Hi {{ $json.advertiser_name }}, here is how to read your attribution report, adjust your bid floor, and scale your best-performing creative." } },
{ "name": "Mark Onboarded", "type": "n8n-nodes-base.googleSheets",
"parameters": { "operation": "update", "columns": { "onboarding_complete": true } } }
]
}
Why it matters: Most advertiser churn on new AdTech platforms happens in weeks 1-2 — before they get their first ROAS win. Automated touchpoints with specific optimization tips convert trial advertisers into retained customers.
Workflow 3: Attribution Data Pipeline Monitor
Goal: Detect attribution data pipeline degradation before advertisers notice gaps in their reports.
{
"name": "AdTech — Attribution Pipeline Monitor",
"nodes": [
{ "name": "Every 5 Min", "type": "n8n-nodes-base.scheduleTrigger",
"parameters": { "rule": { "interval": 5, "unit": "minutes" } } },
{ "name": "Read Pipeline Endpoints", "type": "n8n-nodes-base.googleSheets",
"parameters": { "operation": "getAll", "sheetId": "PIPELINE_SHEET_ID" } },
{ "name": "Health Check Each", "type": "n8n-nodes-base.httpRequest",
"parameters": { "url": "={{ $json.health_url }}", "method": "GET",
"timeout": 5000, "continueOnFail": true } },
{ "name": "Classify Status", "type": "n8n-nodes-base.code",
"parameters": { "jsCode": "return $input.all().map((item, i) => { const endpoint = $('Read Pipeline Endpoints').all()[i]?.json || {}; const ok = item.json.statusCode === 200; const latency = item.json.responseTime || 0; const status = !ok ? 'DOWN' : latency > 3000 ? 'DEGRADED' : 'OK'; return { ...endpoint, status, latency, statusCode: item.json.statusCode }; }).filter(r => r.status !== 'OK');" } },
{ "name": "Alert #integrations-ops", "type": "n8n-nodes-base.slack",
"parameters": { "channel": "#integrations-ops",
"text": "{{ $json.status }} pipeline: {{ $json.pipeline_name }} ({{ $json.health_url }}) — status {{ $json.statusCode }}, latency {{ $json.latency }}ms" } },
{ "name": "Log to SLA Table", "type": "n8n-nodes-base.postgres",
"parameters": { "operation": "insert", "table": "pipeline_sla_log" } }
]
}
Why it matters: Attribution gaps = advertisers seeing lower conversion counts = incorrect ROAS = budget decisions based on bad data. Catching a pipeline degradation at 5 minutes prevents hours of incorrect attribution accumulating in advertiser dashboards.
Workflow 4: Ad Performance Anomaly Alert
Goal: Surface campaigns with sudden performance anomalies (CTR crash, CPC spike, CVR drop) before they waste budget.
{
"name": "AdTech — Performance Anomaly Alert",
"nodes": [
{ "name": "Hourly", "type": "n8n-nodes-base.scheduleTrigger",
"parameters": { "rule": { "interval": 1, "unit": "hours" } } },
{ "name": "Hourly vs 7d Baseline", "type": "n8n-nodes-base.postgres",
"parameters": { "operation": "select",
"query": "SELECT c.campaign_id, c.campaign_name, c.advertiser_name, c.am_email, h.ctr_pct AS ctr_now, b.avg_ctr AS ctr_baseline, h.cpc AS cpc_now, b.avg_cpc AS cpc_baseline, h.cvr_pct AS cvr_now, b.avg_cvr AS cvr_baseline FROM campaign_hourly_stats h JOIN campaigns c ON h.campaign_id = c.campaign_id JOIN (SELECT campaign_id, AVG(ctr_pct) AS avg_ctr, AVG(cpc) AS avg_cpc, AVG(cvr_pct) AS avg_cvr FROM campaign_hourly_stats WHERE recorded_at > NOW() - INTERVAL '7 days' GROUP BY campaign_id) b ON h.campaign_id = b.campaign_id WHERE h.recorded_at = (SELECT MAX(recorded_at) FROM campaign_hourly_stats) AND c.active = true" } },
{ "name": "Detect Anomalies", "type": "n8n-nodes-base.code",
"parameters": { "jsCode": "return $input.all().flatMap(item => { const r = item.json; const anomalies = []; const ctrDelta = r.ctr_baseline ? (r.ctr_now - r.ctr_baseline) / r.ctr_baseline * 100 : 0; const cpcDelta = r.cpc_baseline ? (r.cpc_now - r.cpc_baseline) / r.cpc_baseline * 100 : 0; const cvrDelta = r.cvr_baseline ? (r.cvr_now - r.cvr_baseline) / r.cvr_baseline * 100 : 0; if (Math.abs(ctrDelta) > 30) anomalies.push({ ...r, metric: 'CTR', direction: ctrDelta > 0 ? 'SPIKE' : 'DROP', delta_pct: ctrDelta.toFixed(1) }); if (Math.abs(cpcDelta) > 30) anomalies.push({ ...r, metric: 'CPC', direction: cpcDelta > 0 ? 'SPIKE' : 'DROP', delta_pct: cpcDelta.toFixed(1) }); if (Math.abs(cvrDelta) > 30) anomalies.push({ ...r, metric: 'CVR', direction: cvrDelta > 0 ? 'SPIKE' : 'DROP', delta_pct: cvrDelta.toFixed(1) }); return anomalies; });" } },
{ "name": "Slack #performance-alerts", "type": "n8n-nodes-base.slack",
"parameters": { "channel": "#performance-alerts",
"text": "{{ $json.metric }} {{ $json.direction }}: [{{ $json.advertiser_name }}] {{ $json.campaign_name }} — {{ $json.delta_pct }}% vs 7d baseline" } },
{ "name": "Email Account Manager", "type": "n8n-nodes-base.gmail",
"parameters": { "operation": "send", "toEmail": "={{ $json.am_email }}",
"subject": "Performance alert: {{ $json.campaign_name }} — {{ $json.metric }} {{ $json.direction }}",
"message": "{{ $json.campaign_name }} for {{ $json.advertiser_name }} shows a {{ $json.metric }} {{ $json.direction }} of {{ $json.delta_pct }}% vs last 7-day average. Review campaign settings." } }
]
}
Why it matters: Performance anomalies that go undetected for hours waste advertiser budgets and erode trust. Account managers can't monitor every campaign manually — automated anomaly detection is the only way to scale.
Workflow 5: Weekly Platform Revenue & Growth Report
Goal: Give leadership a weekly view of ad spend under management, revenue, take rate, and advertiser growth.
{
"name": "AdTech — Weekly Revenue Report",
"nodes": [
{ "name": "Monday 8AM", "type": "n8n-nodes-base.scheduleTrigger",
"parameters": { "rule": { "dayOfWeek": 1, "hour": 8 } } },
{ "name": "Query Platform Metrics", "type": "n8n-nodes-base.postgres",
"parameters": { "operation": "select",
"query": "SELECT SUM(total_spend) AS ad_spend_under_management, SUM(platform_revenue) AS revenue, SUM(platform_revenue)/NULLIF(SUM(total_spend),0)*100 AS take_rate_pct, COUNT(DISTINCT advertiser_id) AS active_advertisers, COUNT(DISTINCT campaign_id) AS active_campaigns, AVG(roas) AS avg_roas FROM weekly_campaign_summary WHERE week_start = DATE_TRUNC('week', NOW()) - INTERVAL '7 days'" } },
{ "name": "Query Advertiser Growth", "type": "n8n-nodes-base.postgres",
"parameters": { "operation": "select",
"query": "SELECT COUNT(*) AS new_advertisers, COUNT(CASE WHEN first_campaign_at IS NOT NULL THEN 1 END) AS activated FROM advertisers WHERE created_at >= DATE_TRUNC('week', NOW()) - INTERVAL '7 days'" } },
{ "name": "Merge", "type": "n8n-nodes-base.merge",
"parameters": { "mode": "mergeByPosition" } },
{ "name": "Build Report", "type": "n8n-nodes-base.code",
"parameters": { "jsCode": "const m = Object.assign({}, $input.all()[0].json, $input.all()[1]?.json || {}); const g = $getWorkflowStaticData('global'); const wow = g.prev_revenue ? ((m.revenue - g.prev_revenue) / g.prev_revenue * 100).toFixed(1) + '%' : 'N/A'; g.prev_revenue = m.revenue; const rows = [['Ad Spend Under Management', '$' + Number(m.ad_spend_under_management||0).toLocaleString(), wow + ' WoW'], ['Platform Revenue', '$' + Number(m.revenue||0).toLocaleString(), ''], ['Take Rate', Number(m.take_rate_pct||0).toFixed(1) + '%', ''], ['Active Advertisers', m.active_advertisers, ''], ['Active Campaigns', m.active_campaigns, ''], ['Avg ROAS', Number(m.avg_roas||0).toFixed(2) + 'x', ''], ['New Advertisers (Week)', m.new_advertisers, ''], ['Activated (First Campaign)', m.activated, '']]; const trs = rows.map(r => '<tr><td>' + r[0] + '</td><td>' + r[1] + '</td><td>' + r[2] + '</td></tr>').join(''); const html = '<h2>Weekly AdTech Platform Report</h2><table border=1 style=border-collapse:collapse><tr><th>Metric</th><th>This Week</th><th>Delta</th></tr>' + trs + '</table>'; return [{ html, summary: 'Ad spend $' + Number(m.ad_spend_under_management||0).toLocaleString() + ' | Revenue $' + Number(m.revenue||0).toLocaleString() + ' | Take rate ' + Number(m.take_rate_pct||0).toFixed(1) + '% | ROAS ' + Number(m.avg_roas||0).toFixed(2) + 'x | ' + wow + ' WoW' }];" } },
{ "name": "Email Leadership", "type": "n8n-nodes-base.gmail",
"parameters": { "operation": "send", "toEmail": "leadership@yourplatform.com",
"subject": "Weekly Platform Revenue Report",
"message": "={{ $json.html }}", "emailType": "html" } },
{ "name": "Slack #revenue", "type": "n8n-nodes-base.slack",
"parameters": { "channel": "#revenue",
"text": "Weekly AdTech metrics: {{ $json.summary }}" } }
]
}
Why it matters: AdTech leadership needs to see ad spend under management, platform revenue, and take rate in the same view every week — and track the week-over-week trend without manually pulling reports.
Get All 5 Workflows (Import-Ready)
All 5 AdTech/MarTech automation workflows — fully configured, import-ready JSON — are available at FlowKit on Gumroad.
Each template includes import-ready JSON, a setup guide, and tested node configurations.
What AdTech or MarTech ops problems are you automating with n8n? Drop a comment — happy to share workflow patterns for your specific stack.
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