Sometimes dashboards are ugly, messy, even incomplete. But they teach you more than polished ones ever do. Here’s what I’ve learned building dashboards for email sends, segment validation, and client reporting.
The first dashboards I built were simple, ugly, sometimes broken. Rows missing, charts not aligned, colors off. But it was real data. I could see trends, bounces, engagement. The key is learning fast.
Charts don’t need to be perfect. They need to answer: “Is the list clean?” “Are we hitting thresholds?” I focused on tables and bar charts first, later added graphs once logic was solid.
+------------------+--------+---------+ | Segment | Opens | Clicks | +------------------+--------+---------+ | Welcome Series | 45% | 12% | | Nurture A | 30% | 7% | | Triggered Promo | 55% | 20% | +------------------+--------+---------+
Every day I’d get new metrics, tweak SQL queries, add a column, fix a label. On the next day, after a team review, I’d adjust thresholds or segment calculations. It felt messy, but each iteration improved understanding and trust.
Ugly dashboards spark conversations. I shared early with stakeholders, got feedback, fixed edge cases. The dashboard became a communication tool as much as an analytics one.
Don’t overload with everything. I tracked deliverability, bounces, unsubscribes, open and click rates, segment health. That was enough. Everything else was noise.
[Raw Email Data] --> [SQL Checks] --> [Dashboard]
| | |
v v v
[Segment Validation] [Metrics] [Stakeholder Review]
Dashboard insights link back to processes. SQL segment checks? See SQL Patterns for Email Segmentation. Scaling sends to millions? See Scaling SFMC to 6M Contacts. CDP data prep? Tealium vs mParticle.
The takeaway: dashboards are tools to see problems, not to impress. Ugly ones teach more, faster.