For the last couple of weeks, I’ve been pretty deep in what has turned out to be a surprisingly fun new project.

Building out a Financial Analysis tool for Paperboy.

No...unfortunately that is not a joke. (Turns out i'm more of a nerd than I realized.)

Now, I’ll be honest...over my nearly 10 years as an agency owner, this is something I’ve had the luxury of not worrying about for a long time.

Ya see, when I was running my last agency—a much leaner operation— we’d pretty consistently be operating at an 85–90% profit margin. If you've been following along for a while, you know that's kind of the idea behind the microagency model:

If you can pull it off, the margins are insane.

And because of that, I didn’t really need to obsess over the numbers because we had so much damn wiggle room.

Now, Paperboy’s another beast.

My goal for this agency is different, and as a result, we have a bigger team, which means a little more complexity and more moving pieces.

Yes, we still have healthy margins (healthy enough that I've delayed this process for the last 2 years without going bankrupt.)

However, I decided it was finally time to get a handle on the numbers.

Now, I don't think all of you necessarily need to do this right now. But if you're at least a little bit interested, I want to break down the basics of how I did it.

(At the end I recorded a video of the dashboard so you can see how it works)

Here's how I built the dashboard

You all know that I've been obsessed with AI coding for the last six months and have built numerous internal tools for our agency.

And while yes, there are probably pre-existing tools out there that do something close to what I was looking for (ie quickbooks), I just decided I was going to build it myself. More control. More flexibility in the long run.

So that's what I did.

I built a custom app that piped in all of our data and automated dashboards / reporting.

It's awesome.

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Step 1: Import costs data

At Paperboy, our largest cost —as with most agencies— is going to be labor, so I started there.

I started by piping in all our timesheet data from Harvest (where our hourly team tracks time) and then layered in the fixed monthly costs for our flat-fee team members.

Yes, we pay for some tools and software, but that's about 5% of our overall monthly expenses. So I'm holding off on that. This labor data is going to provide us a great start for now.

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Step 2: Import revenue data

Once I had our costs accounted for, I switched to revenue.

The Harvest data already had a complete list of our clients, so we used that to build a client database. From there, I plugged in pricing structures, and created some basic rules to allocate labor costs and revenue across accounts.

Now in our case, because we only have two or three consistent pricing structures, this was pretty easy to apply to clients. But if you do custom pricing for every project, this will be a little bit more manual work for you.

Id recommend just exporting whatever you have in your invoicing or time tracking tools and go from there.

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Step 3: Data cleaning

As with any data project, you'll probably need to do a little bit of cleaning.

For example, we had a few clients that had multiple records under different email addresses that we had to merge together. And there were also a few oddball client records that were sort of outliers that we had to clean out because they were skewing the data.

Just give everything a once over and look for anomalies that might cause problems. Duplicate entries. Misspellings. etc.

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Step 4: Analysis & dashboards

Now, at this point, the hard part is done.

Once you've got the data in your system, the world is your oyster. I made a list of the top three or four dashboards and metrics that I knew would be helpful, and let the AI code builder (in my case, I'm using Claude code) go to work.

Here are the dashboards we set up:

• Overall agency profitability by month
• Profitability by client
• Costs by client
• Cost breakdown by team member
• Retention & churn analysis
• Most profitable clients
• Least profitable clients
• And how profit margins evolve from Month 1 to Month 12 of a client relationship

In like 8 days, I now have a fully functioning financial analysis application. And there's so much more we can do.

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The Results

Now, I wouldn't say this has been glamorous work, but it’s been eye-opening for sure.

I’ve already caught a few things that surprised me:

• clients that looked profitable but weren’t…
• team members spending a bit more time on certain projects than I realized…
• and patterns that explain why some relationships scale so smoothly while others stay stuck.

Now, I don’t think this kind of system is necessary for everyone.

If you’re running a tiny shop with monster margins, your gut might be all you need.

But for me, it’s been really helpful.

Because even though I used to feel how we were doing, there’s something powerful about seeing it— about having a few real numbers you can trust, instead of vibes and memory.

Not only is it peace of mind, but it's real clarity that can and will likely lead to better decisions for the company.

Anyway, thanks for tuning in to another random week under the hood at Paperboy. Hopefully this will give you some ideas and direction if you decide to go down the same rabbit hole soon.

See you next time!

— Shane
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PS — Here's a quick video walking through a demo of the tool for inspiration.

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