Every year, somewhere, an SEO looks at a falling traffic chart in January or August and wonders whether Google has turned against them. Sometimes it has. Often, though, people simply stopped searching for that topic for a few weeks, exactly as they did the year before. Because Google releases several confirmed updates a year, there is almost always an update near any dip, which makes the confusion easy.
This guide shows you how to separate seasonal demand from algorithm losses using three sources: year-over-year data in Search Console, Google Trends, and query-level demand checks. By the end you will have a simple test you can run in under an hour before you escalate anything.
Key Takeaways
- A seasonal dip shows up as fewer impressions while your average position for core queries stays roughly stable.
- An algorithm loss usually shows up as worse positions for the same queries, with impressions falling as a result.
- Year-over-year comparisons, aligned by weekday, are the fastest seasonality check.
- Google Trends shows relative interest (scaled 0 to 100), not search volume, so use it to compare shapes, not totals.
- If both demand and positions fell, you may have both problems at once; measure them separately.
Why seasonality and updates get confused
Google lists both "algorithmic updates" and "seasonality and changing interests" as separate causes in its documentation on debugging traffic drops. They can look identical on a clicks chart. The difference lies underneath: did fewer people search, or did fewer people see you when they searched?
“make sure it's not a drop that happens every year due to a festivity or a trend.”
— Google Search Central, Debugging drops in Google Search traffic
Timing adds to the confusion. The Search Status Dashboard shows, for example, that the December 2025 core update ran from 11 to 29 December 2025, right across the holiday season, and that the June 2025 core update ran from 30 June to 17 July 2025, at the start of summer in the northern hemisphere. Many sites have natural demand swings in exactly those weeks.
The signals that tell them apart
| Signal | Points to seasonality | Points to an algorithm update |
|---|---|---|
| Average position for core queries | Stable or nearly stable | Clearly worse for many queries |
| Impressions | Down, in line with lower demand | Down because you rank lower or for fewer queries |
| Same period last year | Similar dip at the same time | No comparable dip last year |
| Google Trends for main topics | Interest falls in the same weeks | Interest flat or rising while your traffic falls |
| Competitors' visibility | Everyone in the niche dips | Some sites rise while you fall |
| Branded queries | May dip with general demand | Usually unaffected |
| Timing | Repeats on a calendar pattern | Starts within a known rollout window |
A step-by-step test
- Pull year-over-year data. In the Search Console Performance report, compare the affected weeks with the same weeks last year, shifted by 364 days so weekdays line up. See our Performance report guide for the compare settings.
- Split brand and non-brand. Filter queries with a brand regex. Seasonal effects often hit both; update effects mostly hit non-brand. Our article on filtering branded queries explains the setup.
- Check position for your top 20 non-brand queries. If positions held steady while impressions fell, demand is the likely cause.
- Check Google Trends. Enter your three to five most important topics, set the range to five years and look at the shape of the line around the same weeks each year.
- Check competitors. Use a visibility tool or manual searches to see whether the sites around you also dipped.
- Overlay update dates. Mark confirmed rollout windows on your chart. A drop that starts before an update began is not caused by it.
- Decide. Label the dip as seasonal, update-related, mixed or unclear, and write down the evidence for each label.
Using Google Trends correctly
Google Trends is excellent for seasonality, but it is easy to misread. The numbers are relative, not absolute.
“The resulting numbers are then scaled on a range of 0 to 100 based on a topic's proportion to all searches on all topics.”
— Google Trends Help, FAQ about Google Trends data
That means a fall from 80 to 60 does not mean 25% fewer searches in a way you can directly compare with your clicks. Use Trends to answer one question: does interest in this topic normally drop at this time of year? For more techniques, read our guide to using Google Trends for SEO.
- Compare the same topic across several years to see whether the pattern repeats.
- Set the correct country; seasonality differs between hemispheres and markets.
- Prefer topics over exact search terms where Trends offers them, as they group related queries.
- Remember that Trends reflects Google searches broadly, not only searches where your site appears.
Query-level demand checks
Seasonality is rarely uniform across a whole site. A garden retailer may see "lawn feed" collapse in winter while "Christmas tree stand" peaks. Group your queries into topics, then compare impressions year over year for each group. When impressions fall in one group while position holds, that group is seasonal. When impressions fall and position worsens, look deeper. Our query-level analysis guide walks through grouping methods, and seasonal marketing planning helps you prepare content before the next peak.
| Topic group | Impressions vs last year | Position vs last year | Verdict |
|---|---|---|---|
| Group with a known calendar peak | Down | About the same | Seasonal: plan content for the next peak |
| Evergreen how-to topics | Down | Worse | Possible update loss: review those pages |
| Brand queries | About the same | About the same | No issue |
| New topic (no last-year data) | Not comparable | Worse since rollout | Unclear: check competitors and Trends |
The worksheet above is a template rather than real data: fill in your own groups and label each one. Groups whose verdict is unclear deserve a second look after the next few weeks of data, and any group showing no change in Search Console but a drop in analytics should be checked against our tracking error checklist.
When it is both
Sometimes an update lands during a seasonal low and you suffer both. Estimate them separately: last year's change over the same weeks gives you the expected seasonal effect, and the extra change this year is the part you may attribute to the update or to other causes. Present both numbers, with caveats. Our before-and-after analysis guide explains how to build that year-over-year control.
Common mistakes
- Comparing with the previous month instead of the same period last year.
- Reading Google Trends values as search volumes.
- Ignoring moving holidays (such as Easter, Diwali or Ramadan) that shift between years.
- Assuming a dip during a rollout window was caused by the update without checking positions.
- Changing content in response to a seasonal dip, then mistaking the natural recovery for the effect of the change.
If the evidence points to a real ranking loss, move on to our guides on ranking drop diagnosis and core updates.
Related Guides
- Is It an Update or a Tracking Bug? Ruling Out Analytics Problems
- How to Recover From a Google Spam Update
- Recovering From a Link Spam Update: What You Can and Can't Get Back
Frequently Asked Questions
How can I tell quickly if a traffic dip is seasonal?
Compare the same weeks last year and check whether positions for your main queries held steady. Stable positions with fewer impressions usually means lower demand.
Does Google Trends show search volume?
No. It shows relative interest scaled from 0 to 100, so use it to compare patterns over time rather than exact numbers.
Can a core update and seasonality hit at the same time?
Yes. Estimate the seasonal effect from last year and treat any additional change as possibly update-related.
What if I have less than a year of data?
Use Google Trends for the topic, industry knowledge and competitor visibility as substitutes, and label your conclusion as lower confidence.
Should I change content during a seasonal dip?
Only if you were planning to anyway. Changes made during a natural dip make it hard to know what caused the later recovery.
Know the cause before you act
Separating seasonal demand from algorithm losses keeps your team from fixing the wrong thing. If you want help building year-over-year dashboards or diagnosing a dip, our SEO services team can help. Request a free quote today.
