---
title: "Using a custom date range to study seasonal campaigns"
url: https://memory.wiki/-aUAK3iL
updated: 2026-10-30T01:30:01.865Z
source: "cli"
---
# Using a custom date range to study seasonal campaigns

Seasonal campaigns repeat. If you know what a competitor ran last holiday season, you can predict a lot about the next one.

The ADSoar library lets you set a custom date range together with region, ad status and media type. A simple seasonal study:

- **Pick last year's window** for the season you care about, for example the weeks before a big shopping date.
- **Filter to one region** so you compare like with like.
- **Sort what you find into offers, formats and messages.** Discount-led? Gift-guide copy? Video or image?
- **Repeat for the same window this year** once the season starts, and note what changed.

The historical tracking helps here: the database caches ad data over time, so older campaigns are still there to study.

Learn how to [filter ads by custom date range](https://adsoar.ai/google-ads-library-pro) in the library, or get the overview on [ADSoar](https://adsoar.ai/).

ADSoar is an independent ad research tool, not affiliated with Google or Meta.


---

## Summary
The ADSoar library enables users to analyze seasonal advertising campaigns by setting custom date ranges, regions, and filters to compare competitor ads year-over-year and identify patterns in offers, formats, and messaging.

## Themes
- seasonal campaign prediction
- competitive ad analysis
- custom date range filtering

## Key takeaways
- Seasonal campaigns repeat in predictable patterns that can be studied by examining competitor activity from previous years.
- ADSoar allows filtering ads by custom date range combined with region, ad status, and media type.
- A seasonal study workflow involves selecting last year's seasonal window, filtering to one region, categorizing offers/formats/messages, then repeating for the current year to identify changes.
- The ADSoar database caches ad data over time, preserving older campaigns for historical study.
- ADSoar is an independent tool not affiliated with Google or Meta.

## Insights
- Understanding competitor seasonal patterns enables predictive modeling of future campaigns rather than reactive analysis.
- Historical ad caching creates an asymmetric advantage for researchers studying multi-year campaign evolution.
- Regional filtering is presented as essential for valid comparative analysis, suggesting geographic variation in seasonal strategies is significant.