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Introduction

Google Trends measures the relative popularity of search terms over time and across locations. It returns a normalized index from 0 to 100 rather than absolute search counts — 100 represents the peak popularity of the term in the selected timeframe and location, and other values scale relative to that peak. This connector fetches Trends data through an Adriel-operated AWS Lambda proxy and stores the resulting series in a per-asset cache for reporting in Adriel widgets. As a research-style connector, Google Trends is best used to compare search interest across keywords, track seasonality, and detect emerging topics — not to estimate absolute search volume. To connect this data source, see How to connect Google Trends to Adriel.

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Data refresh strategy

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Architecture data

This connector does not import a campaign hierarchy. Each Adriel asset represents one Trends query configuration — the set of keywords, timeframe, location, category, search type, and timezone. Any change to the configuration creates a distinct asset.

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Reports data

Adriel re-fetches Trends data on a scheduled cadence via the Lambda proxy and rewrites the per-asset cache on each run. Refresh schedule. Twice daily — approximately 06:00 and 16:00 UTC. Refresh strategy. Atomic full replace — the cached series is rewritten in full on each successful run; partial or in-place updates are not used.

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Metrics

How to read the columnsData type uses this vocabulary: Number, Currency, Percentage, Ratio, Duration, Date, Text, URL, Array, Boolean.API Key in code style refers to the field name in the underlying pytrends payload.

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Interest over time

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By region

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Breakdowns

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Query configuration

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Time & date grouping

Adriel groups Trends results into these standard views for reporting alongside other connectors.

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Limitations

  • Normalized index, not search volume. Values are scaled 0–100 relative to peak popularity within the query window; absolute counts are not exposed (Google Trends Help — FAQ about Google Trends data).
  • Re-normalization across timeframes. The same keyword returns different index values depending on the requested timeframe, because Trends re-normalizes each query (Google Trends Help).
  • Up to 5 keywords per query. The connector fetches up to 5 keywords per comparison query.
  • Granularity depends on timeframe. Trends selects the returned granularity automatically — daily for short windows, weekly or monthly for longer ones. Adriel time breakdowns are constrained to what the source returned.
  • Rate-limited unofficial source. Data is fetched via the unofficial pytrends library through an Adriel-operated Lambda proxy; heavy or repeated queries may be rate-limited by Google Trends.
  • Low-volume keywords may return empty. Google Trends suppresses data for terms below its internal search-volume threshold (Google Trends Help).

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API references

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See also