Automating Billboard Budgets with Python Scripts and IoT Sensors

You ever stare at a massive billboard in the middle of nowhere and wonder—who’s actually tracking how effective that is? Yeah, same.

A few months ago, I was helping a friend with a local ad campaign in Chicago. We were brainstorming ways to stretch a pretty lean budget and somehow landed on the idea of tracking billboard impressions. But here's the kicker—we had no idea how to do that without breaking the bank.

The billboard budget problem no one talks about

Billboards are like the grandpas of advertising—sturdy, old-school, and not very talkative. They don’t tell you how many people saw them or if your campaign worked. You’re often stuck with vague estimates and rough traffic data, hoping your investment didn’t just vanish into the wind.

That’s where a bit of nerdy creativity came in. I figured—what if we could make billboards smarter? Like… not AI-level smart, but just enough to give you real data. I mean, Python scripts + some off-the-shelf IoT sensors? Why not.

And guess what? It actually worked. Not perfectly, but surprisingly well.

So, what’s this all about?

Let me break it down, like I would if we were grabbing coffee:

Here are the 5 pieces of the puzzle:

IoT sensors – basic motion detectors, thermal cams, or even sound sensors, depending on the location.
Python scripts – lightweight code to read and log sensor data.
Cloud storage – Google Sheets, Firebase, or even a good ol' SQLite DB.
Traffic estimations – based on sensor triggers + known hourly traffic patterns.
Budgeting logic – formulas to estimate cost per impression and compare against digital ads.

Python Script Example

import time
from gpiozero import MotionSensor

pir = MotionSensor(4)

while True:
    pir.wait_for_motion()
    with open("log.txt", "a") as log:
        log.write(f"Motion detected at {time.ctime()}
")
    time.sleep(1)

This simple script logs motion events using a PIR sensor on a Raspberry Pi. You can later use the data to analyze traffic patterns and estimate billboard impressions.

Real-world curveball

During setup, I had to install the sensor on a fence next to the billboard. Not just any fence—a Wrought Iron Fence Chicago IL kind of deal. You know, sturdy, stylish, and perfect for mounting tech without it looking sketchy.

Turns out, those fences are ideal for small sensor rigs. That’s a weird bonus I never thought I’d appreciate.

Cool tools I found helpful

If you’re thinking of trying this, here’s what I ended up loving:

Thonny IDE – super beginner-friendly Python IDE for Raspberry Pi setups
Google Sheets API – for logging and sharing data live
Blynk – free app to track IoT devices remotely
Zip ties – because mounting stuff on fences is a whole science, right?

Also, shoutout to Osceola Fence—they helped us with installing gear on a Wrought Iron Fence in Chicago without voiding any rental agreements.

Why should you care?

Okay, let’s keep it real. You’re probably not managing 30 billboards across the Midwest. But if you’ve got any sort of offline advertising—or you’re just the curious type—this kind of DIY approach can totally level up your insights.

Here’s what you’ll love about it:

💡 No more guessing how many impressions your billboard got
💸 Helps you compare costs with digital ads (surprise: billboards can beat Google Ads sometimes)
🧠 Learn something new about traffic flows, neighborhoods, even your audience
🛠️ You can tweak and improve it over time without much coding knowledge

Final thought (and one more sneaky tip)

If I’ve learned one thing through this setup, it’s that low-tech doesn’t mean low-impact. You don’t need a Silicon Valley budget to make your advertising smarter. You just need curiosity, a few lines of Python, and maybe a solid Chicago Wrought Iron Fence to hold it all in place.

Give it a try this week—you’ll see!