Wag’n Tails / Measurement
It started with a dashboard.
I came in to run better marketing, and stayed long enough to learn when data should inform the work instead of becoming the work. Salesforce was involved. So were several questionable spreadsheets.
SourceStage12
01
I thought I was asking for a dashboard.
When I joined Wag’n Tails, meaningful marketing tracking and attribution were extremely limited. I still showed up wanting to do the job I knew. Run better marketing. Give people a clearer path. Make the next decision from something sturdier than a hunch.
Fairly early, I asked a question that felt ordinary.
How are we measuring this?
I thought I was asking for visibility. A way to see whether a campaign, a page, or an offer was earning its keep. I like making things. A dashboard was supposed to be a place I visited, learned something, and left.
Becoming the person who helped rebuild the marketing-data infrastructure was not on my list. I was wrong about how short the visit would be.
I really did think it was a dashboard.
02
Before we could measure it, we needed something worth measuring.
The tracking gap was real. It was also sitting on top of a plainer problem. There was not yet much of a marketing system worth pointing a report at. A dashboard can only describe a thing that exists.
So the first work was the ecosystem. I helped create it. I led the people executing pieces of it. I led major portions of the website that made the strategies possible. And I was trying, in the same season, to figure out how any of it would be measured. I was not simply writing up a funnel someone else had already built.
- Strategy
- Campaigns
- Content / resources
- Website + landing pages I spent a long time here
- Lead capture
- Nurture
- MQL define this, then define it again
- Sales handoff
- Opportunity
- Reporting I thought this was the job
The strategies were aimed at qualified leads, not only at people already raising a hand for sales. That meant educational resources, lead magnets, landing pages, calls to action, financing and purchasing information, website conversion paths, campaign strategy, forms, nurture, and digital journeys with an actual sequence.
I was also part of a much larger Salesforce and marketing-data integration. The goal sounded simple, which is often a warning. Connect marketing activity to meaningful business outcomes.
In practice that meant lead sources, UTMs, campaign attribution, lifecycle stages, MQLs, SQLs, opportunities, website behavior, paid media, Marketing Cloud, Salesforce, Data Cloud, Marketing Intelligence, dashboards, data hygiene, sales handoffs, and reporting. It also meant the question that eventually finds you in an executive meeting.
So... what did marketing actually generate?
03
We could finally see it.
None of that arrived as one build. It was research and learning, then a test, then a dashboard that almost explained the test. It was implementation and QA. It was website architecture and lead strategy. It was cross-functional because the number never lived in only one place. Every time something connected, the next question was already in the room.
The pictures here are sketches I drew for this page. The labels are made up. If a number looks specific, I chose it. It is not a Wag’n Tails result.
Wait, we can see where that came from.
- Lead
- Campaign
- Opportunity
What exactly counts as qualified?
Okay, now we need to know what happened next.
| Source | Stage | Count |
|---|---|---|
| Organic | MQL | 12 |
| Paid | Lead | 9 |
| Referral | SQL | 4 |
Why doesn’t this number match that number?
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Name
How did you hear about us
SubmitCan we connect that?
Technically... yes.
Somewhere in that stretch, the job changed shape. Data stopped being something I used and became something I was responsible for explaining. If a number moved, the question came to me. If two numbers refused to agree, that question came to me too.
I had asked for a flashlight. I was starting to live in the wiring.
04
Then the questions changed.
We had made enormous progress. I want that said plainly, because the rest of this only makes sense if it is true. Marketing could see more of its own work than it could when I arrived. The open questions were no longer really about whether we could collect better data.
What happens after marketing hands someone off? Who owns the integrity of what happens downstream? What does an opportunity actually represent? Which system is the source of truth when two of them disagree? Where does marketing attribution end? Who defines the business logic? And which of those questions is a marketer supposed to answer?
I kept learning because I cared. I got fascinated by what good data makes possible. Clearer creative choices. Handoffs that keep hold of a person. A way to see whether the thing you made actually met someone. I leaned in. Curiosity did a lot of the driving.
I also learned that standing next to a problem does not make you its owner. Caring is not a job description. Proximity is not the same thing as being the right person to govern the system. That was one of the biggest professional lessons in this work. I did not arrive at it quickly, or with much grace.
It’s complicated.
Just build the dashboard.
05
Did I make this worse?
Once the gaps were visible, they were harder to treat as weather. Dashboards are talented at producing the next question. Connecting systems is even better at it. You find the places where a definition, a process, or an owner was never really shared. Those places were easier to walk past when the lights were off.
I asked myself, more than once, whether looking had made the work worse.
The answer I trust is quieter than the fear. Measurement doesn’t create the operational problem. Sometimes it turns the lights on. Light creates responsibility, and responsibility needs owners, governance, technical expertise, and definitions people will actually use. A dashboard cannot supply those. A marketer cannot supply them by caring harder.
Seeing the gap was the progress. Being the owner of every system on the other side of it was a different job, even on the days the question landed in my lap.
06
What we actually built.
I want to be careful here. The flattering version of this story is a clean line from a campaign to revenue, with a bow on it. That version would be a fiction. Here is the version I can stand next to.
Before
- Limited attribution
- Few meaningful lead paths
- Minimal visibility into the digital journey
- Disconnected reporting
- Unclear marketing-to-sales visibility
- Limited ability to connect campaigns to lead progression
After
- Structured lead-generation strategies
- Purpose-built conversion paths
- Clearer lead-source tracking
- UTM discipline
- Defined marketing lifecycle concepts
- Nurture infrastructure
- MQL visibility
- Improved MQL-to-SQL tracking
- Cross-platform reporting
- Dashboards for marketing decisions
- A clearer picture of how people discovered and interacted with the brand
Read as a whole, that is a marketing system you can see. Strategies with a place to land. Paths built for the way people actually decide. Tracking that can say where someone came from, and lifecycle language that can say what happened next. Reporting a person can use when they have to choose.
End-to-end revenue attribution was never the honest ending. Visibility moved a long way. The rest of that view depends on downstream processes that are clean, and on ownership that is shared. We could see much farther than we could at the start. We could not honestly say the view ran all the way to the end.
07
It stopped being only my question.
The longer the work went on, the less it fit in one person’s head. You develop a shorthand. Someone finishes the sentence because you have both been living inside the same definition. Someone sends the screenshot on the afternoon a field finally lands where it was supposed to. You look at a number that should not be doing that, and nobody performs certainty.
There was disagreement, the useful kind, where two people are describing one journey and their systems will not cooperate. There was the slower work of learning how someone else thinks. I got better because I was not doing it alone.
After enough time, I could not pull the project apart from the people who built it with me. I don’t think I should.
What I hated.
- I hated not knowing whether the number was right.
- I hated being asked questions I didn’t understand.
- I hated feeling responsible for systems I didn’t control.
- I hated trying to make decisions outside my expertise.
- I hated disappearing into dashboards when I wanted to make something.
- I hated how often one answer created five more questions.
- I hated becoming afraid of a spreadsheet.
But what I hate the most is that I don’t hate data, not even a little, not even close.
I want to know what people are doing, and what resonates with them. I want to capture behavior, connect a piece of creative work to an outcome, build a journey, and see the step where people fall out. I want to test something weird and find out whether it worked. I want data sitting beside the creativity, close enough to reach without moving in.
My best work is when data is a flashlight, not when I am rewiring the building.
So yes, after everything, I still love data. I just think we’re better as friends.
The data story ended there. The people part didn’t.