Every business is seasonal. Sometimes seasonality is obvious, like skis or swimsuits. Other times it’s more subtle - tied to seasonal energy consumption, school calendars, tax deadlines, or regional weather. Either way, quantifying seasonality is critical to forecasting demand, timing promotions, brand events and media spend.
In a past life, a team I was part of spent a LOT of money with Google and our reps had access to an internal tool they called “Wildcat”. They used Wildcat to quantify changes in baseline demand in our category and used it to give us historical data to help us forecast future demand. It was powerful, but slow. Every data pull had to go through a rep, people went on vacation, calls were missed, timelines dragged, etc. As a result, it became difficult to regularly incorporate the data into our workflows and reporting.
Out of curiosity (and impatience), we back-tested Wildcat data against what’s available publicly via Google Trends. The results were nearly identical.
Seasonality patterns? Spot on. Weekly changes? Close enough.
And unlike Wildcat, Google Trends is always on, free, and self-serve.
Since then, I’ve used Trends to model consumer behavior, identify early shifts in interest, and confirm timing for campaigns. It’s not perfect, but it’s quite good - and for early & growth stage demand modeling, that’s often exactly what you need.
Bonus: no need to wait on anyone to pull it for you.
