Email does not have only one outcome. A single message can be accepted, queued, delivered, deferred, bounced, or marked as a complaint, sometimes with delays and retries in between. If your app stores only “sent=true,” your team cannot answer support questions properly, cannot build reliable follow-ups, and cannot detect deliverability issues early.
This guide is for SaaS and development teams handling transactional email event tracking. It explains how to ingest webhook events safely, store raw payloads without losing important detail, normalize data into a queryable structure, and build a message timeline your support team can trust.
It also covers the two webhook realities that break most pipelines: duplicate events and out-of-order events. If you design for both from day one, you can run automations and analytics without double-counting metrics or triggering actions twice.
MailCub Documentation can help you confirm your first send appears in logs in about 15–25 minutes, which is a useful starting point before you build webhook-based tracking.
Quick Answer
- Store raw webhook payloads first, then normalize into tables for reporting and dashboards.
- Verify webhook authenticity and return a 2xx response quickly to avoid retry storms.
- Dedupe events using a provider event_id or a computed event hash.
- Correlate events to a message identity you control, using message_id and correlation IDs.
- Assume events can be replayed and arrive out of order, and design state logic for that.
- Use event data for support timelines, analytics, and idempotent automations.
Why This Matters
Without event tracking, email becomes guesswork. When a customer says, “I never got the reset email,” your team cannot quickly tell whether it bounced, deferred, or delivered late. That leads to slow support loops and frustrated users.
Event tracking also makes deliverability measurable. A sudden increase in deferrals or bounces is often an early warning signal. If your team can see that trend early, you can reduce impact before it becomes a larger incident.
Webhook data also powers product automation. But if dedupe and ordering rules are weak, automations can fire twice or use the wrong state. Your storage model is what makes automation safe and reliable.
What to Store From Webhook Data
Start small and expand as your use cases grow. Most teams begin with:
- Delivery outcomes: delivered, deferred, bounced
- Complaints: spam complaint events
- Engagement (optional): opens and clicks if you track them
- Operational events: dropped, blocked, or rate-limited (provider-specific)
The best long-term approach is to always store the raw payload, even if you only normalize a subset of fields today. This keeps your system forward-compatible as event schemas evolve or your reporting needs change.
Step-by-Step Solution
1) Create a message identity you control at send time
Every webhook event needs a stable way to link back to the exact message in your system. Create your own message_id (such as a UUID) when you create the send intent.
Store useful context alongside it, such as tenant/account, template, recipient, user_id, and a correlation ID (like order_id or request_id). This makes debugging and reporting much easier later.
2) Build a webhook endpoint that is fast, verified, and simple
Your webhook handler should do minimal work in the request path:
- Verify method and content-type
- Verify authenticity (shared secret or signature)
- Enqueue or store the payload
- Return 200 OK quickly
This prevents retries from piling up when your database is slow or a deployment is in progress. It also reduces the blast radius during incidents.
3) Store raw payloads with a dedupe key
Raw storage is your source of truth. It protects you from provider schema changes and preserves fields you may not normalize yet.
A simple raw event table or collection can include:
- raw_id
- provider
- received_at
- event_id (if provided) or event_hash (computed)
- raw_json
Make event_id or event_hash unique so repeated webhook deliveries do not create duplicate rows.
4) Normalize events into a queryable schema
After storing raw payloads, map the fields you care about into normalized tables for fast queries and dashboards.
A clean baseline structure is:
- messages (one row per send intent)
- message_events (many rows per message)
Minimum fields for message_events:
- message_id
- event_type (delivered, bounced, deferred, complaint, open, click, etc.)
- provider_timestamp
- received_at
- provider_message_id (if available)
- meta (JSON: smtp_code, bounce reason, clicked URL, etc.)
This normalized layer is what your support pages, analytics views, and internal tools will query.
5) Handle out-of-order delivery by computing current state
Webhooks do not arrive in perfect order. Instead of overwriting a single status field, design the current state as a computed view.
Safe rules include:
- Store every unique event
- Compute state using precedence (for example: complaint > bounce > delivered > deferred)
- Show a timeline sorted by provider_timestamp, with received_at as a fallback
This keeps your UI and automations stable even when events arrive late.
6) Build a message timeline view for support
Create a single timeline view that answers:
- What did we attempt to send?
- When was it accepted or queued?
- What events happened after?
- What is the latest state and why?
Once support can see a message timeline, email issues become diagnosable instead of unclear. If you are setting up transactional sending now, the Transactional Email Service page is the right place to connect sending and event tracking workflows.
You can also review MailCub Pricing while planning webhook-based tracking across environments and send volumes.
7) Use events for automations, but keep actions idempotent
Event-driven automations are powerful, but they must be safe under event replays.
Examples:
- Bounce → mark address invalid and suppress future sends
- Complaint → suppress sending and alert the team
- Delivered but no login in X days → trigger a reminder (optional)
Treat every automation as an idempotent write. If the same event is delivered twice, the action should still run only once.
Webhook Event Storage Checklist
| Event type | Must store | Helpful extras | Typical use |
|---|---|---|---|
| Delivered | message_id, provider_timestamp | provider_message_id | Proof for support, SLA checks |
| Deferred | message_id, reason/smtp_code | retry_after | Incident detection, retry tuning |
| Bounced | message_id, bounce_type, reason | smtp_code, domain | List hygiene, suppression |
| Complaint | message_id, reason | user_id, tenant_id | Compliance, suppression |
| Open/Click | message_id, provider_timestamp | url, user_agent | Engagement analytics (optional) |
Common Mistakes
- Storing only normalized fields and losing the raw payload permanently.
- Skipping a dedupe key, which inflates metrics and double-triggers automations.
- Doing heavy database work inside the webhook request path and causing timeouts.
- Assuming provider timestamps are always perfectly ordered.
- Correlating by recipient only, which causes collisions quickly at scale.
Troubleshooting
We are missing events
Check the following:
- Does your webhook endpoint return 2xx quickly?
- Are raw events stored, but normalization is failing due to a mapping bug?
- Is your correlation key stable (message_id created at send time)?
If you can confirm sends in logs but not in your app timeline, compare your normalization and correlation logic against what you sent. MailCub Documentation is useful here because it helps confirm send activity in logs before you debug your webhook pipeline.
We see duplicates and inflated metrics
Fixes:
- Enforce a unique constraint on event_id or event_hash
- Make automations idempotent
- Rebuild reports using deduped queries
Events do not match our dashboard
Compare:
- received_at vs provider_timestamp
- Your mapping logic vs raw payload
- The message identity linked at send time
FAQ
What is email event tracking and why store webhook data?
Email event tracking records what happened after a send attempt, such as delivered, bounced, deferred, complaint, and optional engagement events. Storing webhook data gives you a reliable message timeline for support, analytics, and automations.
Should I store raw webhook payloads or only normalized fields?
Store raw payloads first, then normalize the fields you need for reporting. Raw storage preserves details for debugging and protects your pipeline from schema changes.
How do I dedupe webhook events safely?
Use a provider event_id when available. If not, compute a stable event_hash from message identity, event type, provider timestamp, and recipient, then enforce uniqueness in your database.
How do I handle out-of-order webhook delivery?
Assume events can arrive late or out of order. Store all unique events, compute current state using precedence rules, and show a timeline sorted by provider timestamp with received_at as a fallback.
What keys should I use to correlate events to one message?
Use a message_id you control, created at send time, plus provider references when available. Also store correlation IDs such as user_id, order_id, or request_id for faster debugging.
How long should I retain webhook event data?
Keep raw events long enough for support and compliance needs, and retain normalized summaries longer if needed for reporting. The right retention window depends on your product and support workflow.
Conclusion
Email event tracking turns “we think it sent” into a traceable timeline. Store raw payloads, normalize them into a stable schema, dedupe events reliably, and compute state in a way that survives replays and out-of-order delivery.
Once your message timeline is trustworthy, you can safely build dashboards and automations on top. Use MailCub Documentation to ship a minimal webhook ingest and storage pipeline, then expand it step by step as your reporting and automation needs grow.