> For the complete documentation index, see [llms.txt](https://docs.coupler.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.coupler.io/sources/category/ppc/snapchat-ads/best-practices.md). # Best Practices ## Recommended setup


Use "Daily" split for active campaign monitoring	If you're optimizing campaigns in real time, split your performance data by "Daily" so you can see day-to-day trends. This helps you spot underperforming days and adjust spending quickly.
Pull "List" reports separately for reference data	Create dedicated data flows for "List of Campaigns," "List of Ads," and "List of Creatives." Refresh these monthly or after major campaign changes. Use them to join with performance data to add human-readable names.
Use Join transformations to add dimension context	If you export a performance report without demographic breakdowns, then export a geo&demography report separately, use Coupler's Join transformation to combine them on campaign/ad ID. This gives you rich segmentation in one place.
Set attribution windows to match your sales cycle	If your product has a longer consideration cycle, use 7 or 28 days post-swipe. For immediate-purchase products, 1 day post-swipe is usually sufficient. Consistency across platforms (Snapchat, Facebook, Google) makes multi-channel analysis easier.

## Data refresh and scheduling


Account for Snapchat's 24–48 hour conversion reporting lag	Don't schedule data flows to run within 24 hours of campaign end. Schedule refreshes at least once daily, preferably in the evening or overnight, so you're always working with near-complete data.
Schedule multi-account flows during off-peak hours	If you manage 5+ ad accounts, run scheduled flows outside 9 AM–5 PM to avoid API rate limits. Early morning (before 8 AM) or late evening (after 6 PM) is ideal.
Use incremental date ranges for high-frequency updates	Instead of re-pulling 90 days every hour, create a flow that pulls only the last 3–7 days and appends it to your historical data. This reduces API strain and keeps your dashboard fresh without hitting rate limits.

## Performance optimization


Avoid overly granular dimensional breakdowns in the same flow	If you combine multiple dimensions (e.g., Age + Gender + Country), you'll get one row per combination, which explodes row count. Create separate flows for different dimensional analyses—one for demographic trends, one for geographic performance.
Filter out inactive campaigns to reduce data volume	Use the advanced filter "Campaign Status = Active" to exclude paused or archived campaigns. This keeps your data lean and makes dashboards faster. Create a separate "historical" flow for paused campaigns if you need historical reference.
Pull only the metrics you need	Don't select every available metric. Choose 8–12 core metrics (spend, impressions, clicks, conversions, ROAS, CPC, CPA) relevant to your analysis. Extra metrics you don't use slow down the flow and clutter your destination.

## Common pitfalls {% columns %} {% column %} **Do** * Start with a manual test run to verify your settings before scheduling * Use "Totals" split when comparing against Snapchat Ads Manager to ensure exact alignment * Document your attribution window choice so the team knows how conversions are counted * Create separate flows for different report types (performance vs. lists vs. geo\&demography) * Check Snapchat Ads Manager a day after your campaign ends before pulling final numbers {% endcolumn %} {% column %} **Don't** * Assume zero conversions mean zero data—wait 48 hours and re-run before investigating * Mix multiple ad accounts in one flow if any are in different organizations (authorization may fail) * Use demographic dimensions on campaigns targeting countries where those dimensions aren't available * Schedule hourly refreshes for data from large account portfolios—this will hit API rate limits * Re-run the same flow multiple times in quick succession without waiting for the first to complete {% endcolumn %} {% endcolumns %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.coupler.io/sources/category/ppc/snapchat-ads/best-practices.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.