---
title: "Researching seasonal campaigns with a date range"
url: https://memory.wiki/eSMI1arW
updated: 2026-11-03T01:45:15.858Z
source: "cli"
---
# Researching seasonal campaigns with a date range

Black Friday, back to school, summer sales: competitors repeat their seasonal playbooks, and last year's ads are the clearest preview of this year's.

How to research a season:

1. Pick the competitor and your main region.
2. Set a custom date range covering last year's season plus a few weeks either side.
3. Include inactive ads, since the campaign has ended.
4. Note the start date. How early did they begin?
5. Note the formats and offers. Did they lead with discounts, bundles or free shipping?
6. Repeat for the next competitor.

Build a small calendar from the results. It tells you when to launch and what you will be up against.

ADSoar has an [advanced Google Ads library search](https://adsoar.ai/google-ads-library-pro) with custom date ranges, status and media filters. See [ADSoar](https://adsoar.ai/) for the rest of the toolkit.

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


---

## Summary
To research seasonal campaigns, analyze competitors' past advertisements by setting custom date ranges that include inactive ads. This process helps build a calendar to determine optimal launch timing and competitive strategy.

## Themes
- seasonal campaign research
- competitor ad analysis
- strategic launch planning

## Key takeaways
- Researching seasonal campaigns requires setting a custom date range that includes the period before and after the previous year's season.
- Analyzing competitor start dates helps determine the optimal timing for your own campaign launch.
- Reviewing competitor formats and offers reveals whether they prioritize discounts, bundles, or free shipping.
- Building a calendar based on competitor research clarifies the competitive landscape and launch timing.
- ADSoar provides an advanced Google Ads library search tool with specific filters for date ranges and ad status.

## Insights
- Historical ad data serves as a predictive model for future seasonal campaign performance.
- Including inactive ads is essential to capturing the full lifecycle of a completed seasonal promotion.

## Open questions / gaps
- What specific metrics should be used to evaluate the success of the ads identified during the research process?