# Best Practices

## Recommended setup

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th></tr></thead><tbody><tr><td><strong>Start with preset reports, then customize</strong></td><td>Use "Channel detailed statistics" or "Video detailed statistics" first to understand the data. Once you're familiar, build custom reports with specific metrics and dimensions tailored to your analysis. This avoids overwhelm from too many options upfront.</td></tr><tr><td><strong>Plan your date range for data availability</strong></td><td>YouTube Analytics data takes up to 48 hours to become available. For scheduled reports, set the end date at least 2 days in the past (e.g., use {{60daysago}} instead of {{today}}). This ensures complete data every time the flow runs.</td></tr><tr><td><strong>Split data by period for trend analysis</strong></td><td>When building dashboards, enable "Split data by period" (day or month) to see trends over time. Without splitting, you get only totals. Daily splits are useful for catching viral moments; monthly splits are better for seasonal trends.</td></tr><tr><td><strong>Use additional dimensions strategically</strong></td><td>Breaking data into too many dimensions spreads traffic across many rows, making individual values small and less useful. Start with one breakdown (e.g., by video or country), then add more only if needed for specific insights.</td></tr></tbody></table>

## Data refresh and scheduling

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th></tr></thead><tbody><tr><td><strong>Schedule reports for early morning (off-peak)</strong></td><td>Run data flows between 1 AM and 6 AM your local time to avoid hitting YouTube API quota limits during peak hours when other users' flows may be running. This improves reliability.</td></tr><tr><td><strong>Run the flow manually first before scheduling</strong></td><td>Always execute the data flow once manually to confirm settings are correct and data flows as expected. Only after a successful manual run should you set up a schedule. This prevents silent failures from bad configurations.</td></tr><tr><td><strong>Stagger multiple channel flows if you have many</strong></td><td>If you're pulling data from 5+ YouTube channels, space out their scheduled run times by 10-15 minutes each. This prevents simultaneous API calls that could trigger quota limits.</td></tr></tbody></table>

## Performance optimization

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th></tr></thead><tbody><tr><td><strong>Limit date ranges for faster pulls</strong></td><td>Pulling 2+ years of historical data takes longer and uses more quota. For ongoing reports, use a rolling 90-day or 180-day window instead. Keep a separate historical export in your destination for archive purposes.</td></tr><tr><td><strong>Use custom reports instead of multiple flows for different metrics</strong></td><td>Creating 10 separate flows (one for each metric) is slower and uses more quota than one custom flow pulling all 10 metrics at once. Combine related metrics into a single data flow when possible.</td></tr></tbody></table>

## Common pitfalls

{% hint style="danger" %}
**Don't pull data for today or yesterday.** YouTube Analytics has a 48-hour delay. Reports that include "today" or "yesterday" will be incomplete or missing data. Always use date ranges from 2+ days in the past.
{% endhint %}

{% hint style="danger" %}
**Don't mix incompatible metric-dimension combinations in custom reports.** For example, "Subscribers gained" at the video level doesn't exist—it's only available at the channel level. If a metric doesn't populate, check the YouTube help center to confirm it supports the dimensions you've selected.
{% endhint %}

{% hint style="danger" %}
**Don't run data flows for all channels simultaneously if you have many.** Batch requests will fail or timeout if quota is exceeded. Schedule them 10-15 minutes apart instead.
{% endhint %}

{% columns %}
{% column %}
**Do**

* Set end dates 2+ days in the past to ensure complete data
* Use preset report types as templates before building custom reports
* Split data by day or month to see trends
* Run flows during off-peak hours (early morning)
* Combine related metrics into one flow
* Check YouTube's API status if flows fail unexpectedly
  {% endcolumn %}

{% column %}
**Don't**

* Use today or yesterday in date ranges
* Run 10+ flows simultaneously across many channels
* Pull 5+ years of historical data in one flow
* Add too many dimensions (breaks down data too much)
* Assume Coupler.io data matches YouTube Studio exactly within hours
* Skip the manual run before scheduling
  {% endcolumn %}
  {% endcolumns %}