How do you determine what caused a reputation loss?

Your open rates fell off a cliff, complaints ticked up, and now you're staring at a wall of data trying to figure out what actually happened. The good news is that reputation loss almost always has a traceable cause. The investigation just has to happen in the right order.

Start by pinning down when the drop started, not just that it happened. Pull your inbox placement data or open rates by day and find the exact point things shifted. That date is your anchor. Everything else you check gets measured against it.

Step 1: Check your complaint data first. Pull your feedback loop reports and look at which campaigns generated complaints and when. If you see a spike that lines up with your anchor date, you've likely found your trigger. Ask yourself which segment those complainers came from. A burst of complaints from one campaign or one list source tells a very different story than complaints spread evenly across all sends.

Step 2: Look for sending events near that date. Did anything change in the two to four weeks before the drop? Common culprits are new list additions (especially from co-registration, a lead magnet, or a purchased source), a reactivation campaign that woke up dormant addresses, a spike in sending volume, a new content type, or a change in sending frequency. Any one of these can shift how mailbox providers read your behavior. The timing rarely lies.

Step 3: Check engagement by segment. If your overall open rate dropped but some segments held steady, that's a clue. The segments that dropped first are usually the ones closest to the problem. Recent imports that never engaged, cold lists you re-mailed, or audiences who'd been quiet for months before a campaign hit them again are all worth isolating.

Step 4: Check for blocklist activity. Run your sending domain and IP through a blocklist checker and cross-reference any listing date against your sending history. If you got listed on Spamhaus or Barracuda right after a specific campaign, that campaign is your prime suspect. What was sent just before the listing? That's your focus.

Step 5: Cross-reference everything on a timeline. The real detective work is mapping your complaint data, engagement drop, blocklist dates, and sending events onto a single timeline. When two or more of those signals converge on the same window, you've found your cause. A spike in complaints from a reactivation campaign that coincided with a blocklisting three days later is a clear pattern. Scattered signals with no overlap usually point to a slow-burn hygiene issue rather than a single event.

If you're not sure whether you're looking at a list hygiene problem, an engagement problem, or something technical, that's worth working through separately. Our free blocklist checker is a good first stop, and if you're still stuck after running through these steps, our SOS hotline is free and we actually pick up.