Google Trends only lets you compare 5 search terms at a time, and every comparison is rescaled to its own 0-100 range. So if you have a list of 50 product ideas or blog topics, you can't just compare them in groups of five: a "60" in one group means something different from a "60" in another.
Here's how to get any number of keywords onto one comparable scale, plus growth numbers (year-over-year change, momentum, seasonality) you can sort by, without writing any code.
The trick: chain comparisons through a shared reference
Google Trends values are relative. In any comparison, the most-searched term in the period gets 100 at its peak and everything else is scaled against it. To compare more than 5 terms:
- Compare a reference keyword with 4 new keywords.
- Record each keyword's average interest relative to the reference.
- Make the biggest keyword so far the new reference, and compare it with the next 4.
- Multiply the ratios along the chain, so every keyword ends up relative to the same top keyword.
Using the biggest keyword as the reference keeps the numbers above Google's rounding for as long as possible. Google rounds very small values to 0, so a term under about 1% of the reference reads as 0.
Real example: coffee vs tea vs 5 trendier drinks (US, past 5 years)
I ran 7 drink keywords through the Google Trends Scraper on Apify. Data pulled on 28 October 2026:
| Keyword | Popularity (coffee = 100) | YoY change | Trend |
|---|---|---|---|
| coffee | 100 | +9.6% | rising |
| tea | 54.73 | -2.8% | rising |
| espresso | 10.66 | +17.1% | rising |
| matcha | 6.14 | +10.0% | rising |
| chai | 4.08 | +5.4% | rising |
| kombucha | 1.74 | -14.0% | stable |
| yerba mate | 0.29 | -13.3% | rising |
To check the chaining, I compared coffee, tea, espresso, matcha and kombucha directly in one native Google comparison. The ratios were identical: tea 54.73, espresso 10.66, matcha 6.14, kombucha 1.74.
What the numbers say:
- Matcha is about 6% of coffee's search interest in the US but still growing year over year, with a seasonal peak in August.
- Kombucha and yerba mate are shrinking year over year. Yerba mate is "rising" over the full 5 years (the fitted line slopes up) while falling over the last 12 months. That's worth knowing before you build a product around it.
- Breakout searches (Google's label for growth above 5000%) for matcha included specific brand names. Those are often the earliest signal of a new competitor.
The metrics, and how they're calculated
Each keyword gets:
-
relativePopularityandpopularityRankon the shared scale -
yearOverYearChangePct: the latest 4 data points vs the same points one year earlier (needs a period longer than 12 months) -
recentChangePct: the latest quarter of the period vs the quarter before it -
trendDirection: a least-squares line over the whole period; a total fitted change above +15% is "rising", below -15% is "falling" -
peakMonth: the calendar month with the highest average interest (periods of 2+ years) -
topRegions,relatedQueriesTop,relatedQueriesRisingandbreakoutQueries - the full
timeline, with each point also on the common scale
How to run it
- Open the Google Trends Scraper on Apify.
- Paste your keywords, one per line. There's no 5-term limit.
- Pick a country code (
US,IN,ES, or a region likeUS-CA) or leave it empty for worldwide, then pick a time range. - Export to CSV, Excel or JSON, or send the results to Google Sheets, Make, Zapier or n8n.
You can also add country codes under Trending now to get today's trending searches with approximate traffic and linked news articles.
It's pay per keyword report with data. Keywords with too little search volume are not charged.
Caveats
- Trends values are relative, not absolute search counts. "6% of coffee" is a ratio, not a number of searches.
- Chaining keeps precision best when the keywords are within about two orders of magnitude of each other. Very small terms next to a giant reference round to 0.
- Google adjusts its data collection from time to time, so compare like with like: same country, same period.
Disclosure: I built this Apify actor. The data in the table comes from a real run on the date shown.
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