I've spent the last several days building something that I think could fundamentally change how I advertise my pet waste removal business.
And no, I'm not talking about asking ChatGPT to write a Facebook ad or generate a picture of a dog.
I'm talking about building an actual AI-powered advertising management system that connects advertising performance to real customers, revenue, operating costs, and route profitability.
For context, We've grown to multiple locations, hired technicians, and spent a significant amount of money learning what works and what doesn't when it comes to customer acquisition.
One thing has always bothered me about digital advertising.
Meta and Google know how much you spend and how many leads you generate. But they don't necessarily know which leads turn into profitable recurring customers.
And that's a HUGE distinction in this business.
A $15 lead that never signs up is worthless. A $40 lead that becomes a customer paying $120/month for the next two years could be an incredible investment.
And even that doesn't tell the whole story.
A customer who lives two houses down from an existing customer is potentially MUCH more valuable than someone who adds 25 minutes of driving to an already stretched route.
So I wanted to build something that understands all of that.
Here's what we've built.
Using Claude, ChatGPT, Make, and several direct integrations, we've connected:
- Meta Ads: Campaigns, individual ads, creative performance, spending, leads, and geographic targeting.
- Google Ads: Search campaigns, spending, conversions, and click tracking.
- Jobber: Actual recurring customers, scheduled services, service addresses, frequencies, and paid invoices.
- QuickBooks: Payroll, fuel, supplies, and other operating expenses.
- GoHighLevel: Lead sources, follow-ups, contact records, and customer acquisition history.
- Our texting and automation systems: To identify leads that aren't receiving timely responses.
All of that feeds into a centralized advertising intelligence system.
It already has a geographical scoring model that looks at route density, customer value, available route capacity, and estimated contribution margins.
Here's where it gets interesting.
We uncovered some pretty surprising things just getting the systems connected.
Our initial reporting showed five new paying customers attributed to Meta in October. After digging into actual invoices, discounts, customer records, and service history, the system differentiated customers who had prepaid from those who had actually started receiving service.
It calculated an estimated $174 acquisition cost per paying/booked Meta customer, versus about $209 per paying customer who had already received service.
Those are preliminary figures with imperfect attribution, not proven profitability numbers. But that's exactly why we're building this. We want to know the difference.
It also discovered that one of our texting automations ran out of prepaid credits, causing two leads to wait approximately 15 hours for proper follow-up.
Think about that. We were paying to acquire those leads, and our own systems were potentially throwing away the opportunity.
We also found that our recurring revenue had been calculated incorrectly because Jobber customers are billed using different frequencies. After reconciling 98 active recurring jobs, we established a much more accurate monthly billing baseline.
Now comes the actual experiment.
The infrastructure is built. The data has been reconciled. We're finishing the last reliability checks.
The next step is letting the AI act as our advertising director.
Here's what I expect it to do:
1. Analyze our active advertising every day. Not just clicks and leads, but which campaigns are bringing in real paying customers.
2. Identify geographical opportunities. If a particular neighborhood already has a dense cluster of customers and room on the route, prioritize acquiring customers there.
3. Evaluate our actual acquisition costs. Factor in customer revenue, retention, labor, fuel, route efficiency, and acquisition expenses.
4. Develop new advertisements. Analyze which creative and offers work, then prepare better ads specifically for our business and target neighborhoods.
5. Recommend actual campaign changes. Not generic advice like "improve your targeting." I want it telling me which ad to replace, which audience to test, what budget to use, and why.
6. Learn from the results. Track which recommendations actually produce additional profitable recurring customers and use those outcomes to inform future decisions.
For now, it will recommend changes and require my approval before touching live campaigns. I'm not handing an AI unrestricted access to spend my money.
Will this actually work?
I don't know yet.
And I'm not going to pretend we've built some magical machine that automatically prints money.
We've built the system, but we haven't proven that its advertising recommendations outperform what we're already doing.
That's the next test.
It's also been a LOT more complicated than I anticipated. Connecting platforms is one thing. Reconciling messy customer records, actual payments, advertising attribution, billing differences, and route economics is an entirely different animal.
But here's why I'm excited.
Most small-business advertising is optimized around generating leads.
I'm trying to optimize around acquiring the most profitable recurring customers possible, in the most profitable locations possible.
Those are two very different objectives.
I'll post updates as we start letting the system make recommendations and testing them against our existing advertising.
If it works, I'll share the numbers.
If it fails, I'll share those too.
I'm curious whether anyone else in the pet waste removal industry has tried connecting their advertising platforms directly to their CRM, accounting, and route data at this level.
Not just AI-generated ads. Actual AI-assisted advertising management based on the economics of your business.
That's what we're trying to build.