When using the Google Trends connector in Catchr, you may notice that the same request can return slightly different values at different times.
This behavior is expected and comes directly from how Google Trends works.
Google Trends does not provide absolute search volumes. Instead, it provides normalized and sampled data. This means:
Data is scaled from 0 to 100.
Values represent relative popularity over the selected time range.
Google may use sampling methods when generating results.
As a result, the same query can produce slightly different results depending on when it is run.
Google officially explains this behavior in their documentation.
Since Catchr relies directly on the Google Trends API, we are fully dependent on the data returned at the time of the request. If Google updates or reprocesses its data, Catchr will reflect those changes.
The level of granularity you request has a strong impact on data stability.
For example:
If you request daily data, Google must distribute the normalized score across many data points.
With more granular data (per day), small variations in sampling can create noticeable differences for the same date.
Running the same daily query at different times may yield slightly different results.
To reduce discrepancies, we recommend requesting broader time granularity whenever possible.
Instead of:
Daily data
Prefer:
Weekly data
Monthly data
Using a larger time granularity:
Reduces the impact of sampling fluctuations
Provides more stable and consistent results
Delivers more reliable trend comparisons
Aggregated data (weekly or monthly) is generally more robust because it is based on a larger dataset.
Avoid comparing daily Google Trends data pulled at different times.
Use weekly or monthly granularity for reporting.
If consistency is critical, export and store your dataset instead of refreshing historical queries repeatedly.
