Google Trends is the fastest read on public demand for a topic, but its site gives you a chart, not data. For SEO, product, or market research you want the underlying interest-over-time numbers in JSON so you can compare terms, spot seasonality, and feed dashboards.
Who uses search-interest data
Search interest is the cleanest public read on demand, and several teams use it. SEO and content teams size opportunities and time content to seasonal peaks. Product and marketing teams gauge interest in a category or a competitor before committing budget. Market researchers and analysts track the rise and fall of topics over months. And trend and consumer-insight teams compare related terms to see which framing is winning attention.
What the data looks like
One record per data point. A real row from a live run:
| Field | Example |
|---|---|
| keyword | coffee |
| date | 2025-06-29 |
| interest | 61 |
| geo | US |
| timeframe | today 12-m |
How to get it
Run the Google Trends Scraper on Apify with one or more keywords, a region, and a timeframe. Output is JSON, CSV, or Excel, priced per result.
Run it on a schedule
{
"keywords": ["your search term"],
"geo": "US",
"timeframe": "today 12-m"
}Common workflows
The usual jobs are: a multi-term comparison to see which of several keywords has the most momentum; a seasonality pull over twelve months to plan campaigns around peaks; a regional read to compare interest across markets; and a recurring pull that feeds a dashboard so a term’s trajectory is always current. Because the output is clean JSON or CSV, each of these plugs straight into a spreadsheet or a BI tool.
Why pull it as data
The Google Trends site gives you a chart, not a dataset. Exporting by hand is slow and loses the structure you need to compare terms or chart seasonality programmatically. Pulling the interest-over-time points as JSON keeps the keyword, date, region, and value on every row, so the data drops into analysis rather than a screenshot.
Reading the numbers correctly
The interest value is a relative 0-to-100 scale, not an absolute search count: 100 marks the peak point in the requested window and every other point is scaled against it. That means the same term pulled over different windows or regions is scaled differently, so compare within a single run rather than across runs, and use the shape of the curve — the peaks, troughs, and direction — rather than treating the number as a volume.
Cost and control
Pricing is per result with no subscription, so a single-term twelve-month pull is inexpensive and a multi-term comparison scales in proportion. You choose the terms, region, and window on every run, which keeps cost predictable and the output focused on the comparison you actually care about.
FAQ
Can I compare multiple terms? Yes — pass several keywords and compare their interest curves.
Can I choose the region and window? Yes, by geo code and timeframe.
Is it good for seasonality? Yes — a 12-month window shows peaks and troughs per term.
Is the interest value an absolute search count? No — it is a relative 0-100 index scaled to the peak in the requested window.
Can I compare regions? Yes — run the same term across geo codes, comparing within each run.
A practical way to use the curve: identify the peak month over a twelve-month window, then plan content, launches, or campaigns to lead that peak by a few weeks so you are visible as interest climbs rather than after it crests.
Comparing related terms in a single run is often more revealing than any one term alone. If two framings of the same idea diverge over time, the winning phrasing tells you how your audience actually describes the need — which feeds directly into copy, page titles, and keyword targeting.
Finally, store the pulls over time. A single run is a snapshot; a recurring run stored to a sheet or database becomes a longitudinal record you can chart, annotate with events, and use to separate a durable trend from a passing spike.
One caution worth repeating: because the index is relative, resist the urge to compare a value from one run against a value from another run taken over a different window or region. Keep comparisons inside a single run, and when you need a longer view, pull a longer window rather than stitching separate pulls together.
Used consistently, the interest curve becomes a low-cost early read on demand — useful for deciding what to build, what to write, and when to ship it, all from a public source you can refresh on your own schedule.
Treated this way — as a shape to read, not a number to quote — search interest is one of the most reliable free indicators of where attention is heading.
Related: PR Newswire Scraper for news indicators and LinkedIn Jobs Scraper for hiring trends.
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