Okay, so, let’s talk about how I tackled retail foot traffic tracking for sales forecasting. It was a wild ride, let me tell you.
First off, I needed data. Loads of it. I started by looking at what we already had. Turns out, we had some pretty decent camera footage from our security system. But it wasn’t exactly… organized. Hours and hours of footage, no way I was manually counting people.
So, I spent a good week researching different software options. Some were ridiculously expensive, others looked like they were built in the 90s. Finally, I found something that seemed manageable, a cloud-based solution that could analyze video footage and give me headcounts. It wasn’t perfect, trust me. I had to adjust the settings a million times to filter out things like shadows and stray dogs that kept tripping up the system.
Then came the data cleaning. Oh boy. The software spat out some pretty messy CSV files. There were inconsistencies, missing data points… you name it. I spent days meticulously cleaning the data, checking for errors, and filling in gaps using some basic interpolation techniques. I felt like a data janitor, but hey, someone’s gotta do it.
Once I had clean data, I started building the forecast model. I tried a few different approaches. First, I tried a simple linear regression, but it wasn’t really capturing the nuances of our sales patterns. Then I moved on to ARIMA, which was a bit more complex but provided a much better fit. It took some tweaking, playing with different parameters, but eventually, I got something that looked reasonable.
Finally, the moment of truth: testing the model. I split my data into training and testing sets, and ran my predictions. The results were… mixed. The model wasn’t perfect, but it was giving me a significantly better forecast than our previous guesswork. There were still some discrepancies, especially during peak seasons, which suggested I needed to refine the model further by incorporating external factors like holidays and promotions.
I added these factors, and you know what? The accuracy went up. Not perfect, still some room for improvement, but it was definitely a massive step up from where I started.
The whole process was a rollercoaster. It involved lots of trial and error, late nights hunched over my laptop, and plenty of moments where I wanted to throw my computer out the window. But the satisfaction of finally having a decent sales forecasting model, all built from scratch, was pretty awesome. It wasn’t just about the numbers, it was the sense of accomplishment, the learning curve, the journey. You know?