Why the automation "stopped working"

Good Morning {{first_name}}!

It’s Fri(yay) - and that means it’s time for your Revenue Play - an issue where I share some stories from my own career as a data nerd.

This week, it's the messy lifecycle data living underneath it. (I know. Not the sexy answer. But it's the right one, and your wallet will thank you later.)

Let’s dive in.

A client once came to me, completely frustrated that a workflow wasn’t working. Nothing was triggering, sequences and numbers were at a nice big 0%.

The idea that they’ve invested in a solid tool, built out the workflows and wanted things to “just work” was becoming a complete nightmare.

And once she flagged it to me, she had built a workflow that eliminated thousands of people. On paper, everything looked like it should be working.

The thing was - everything was fine. The sequences were fine, the tool was fine, and the messaging was spot on.

But the filters? That was the problem. Filtering on top of broken data fields? Even worse.

Lifecycle stages were inconsistently defined. CRM sync was patchy, so customers were not syncing over from their ERP.

Event taxonomy was all over the place. So when the automation fired, it was firing based on signals that didn't actually reflect where the contact was in their buying journey.

We see this constantly in 2026. Teams feel like their automation is "not working" and immediately start blaming the sequence or the platform. Rarely do they look at the foundation.

We went back to basics. Together, we rebuilt their event taxonomy so behavioural signals actually meant something. That included double-checking syncs from ERP and Salesforce (turns out, there were 243 issues - just waiting to be addressed).

Then, we tightened their lifecycle stage definitions so contacts were moving through stages based on real actions, not accidental ones. Turns out, sales were skipping SQL altogether and moving MQLs into opportunities.

And we cleaned up the CRM sync so the data flowing into the automation was actually trustworthy.

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Once the foundation was solid, the existing automation started performing the way it was supposed to. Directionally, the team went from questioning every trigger to actually trusting the data they were seeing. That trust shift matters more than people think.

When you implement something like this, always have a period of 30-60 days where you track and audit - and verify - that things are indeed accurate.

TL;DR

Before you rebuild your sequence or blame your platform, pull your lifecycle stage data and ask: are contacts moving through stages based on real purchase behaviour, or based on whatever someone clicked on a Tuesday? If you can't answer that with confidence, that's your gap.

And always question whether marketing and sales are speaking the same language. In 95% of cases, they aren’t.