Keyword Seasonality Forecaster
Publishing in the peak month is publishing too late. This tells you when to start.
The lead-time model behind the seasonal content calendar. Runs entirely in your browser — nothing you type is sent anywhere.
Your search volume
Paste 12 values (or 24 for two years, which get averaged month-on-month). Google Trends gives relative interest rather than absolute volume — that is fine here, because the index only depends on the shape.
How long a new page on your domain takes to reach a position that earns clicks. On a young domain with no links this is longer than most planning assumes.
When to start
Publish by
So the page is ranking when demand peaks in Oct.
Jul
Brief and draft in
Jun
Peak month
Oct (2.89×)
Trough month
Feb (0.35×)
Peak-to-trough ratio
Strongly seasonal — lead time matters.
8.3×
Seasonality index
| Month | Volume | Index | Shape |
|---|---|---|---|
| Jan | 320 | 0.39 | |
| Feb | 290 | 0.35 | |
| Mar | 340 | 0.41 | |
| Apr | 410 | 0.49 | |
| May | 520 | 0.63 | |
| Jun | 610 | 0.73 | |
| Jul← publish | 720 | 0.87 | |
| Aug | 880 | 1.06 | |
| Sep | 1,900 | 2.29 | |
| Oct | 2,400 | 2.89 | |
| Nov | 1,100 | 1.32 | |
| Dec | 480 | 0.58 |
1.00 is an average month. The index is volume divided by the 12-month mean, so it is unaffected by whether your source reports absolute volume or relative interest.
The common failure is publishing in the peak month, which means arriving after the demand has been served by pages that ranked months earlier. The lead time is the whole point: at 12 weeks, a page for Oct demand has to exist by Jul.
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Seasonal content published during the peak is content published too late. Here is how to read a seasonality curve, the lead-time maths between publishing and ranking, and a Python script that turns 24 months of Google Trends data into publish dates.
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Want this fitted to your actual numbers?
The defaults here are illustrative. Fitted to your own data — real hazard rates, real margins, real conversion — the same models tell you what to do next rather than what is theoretically possible.
Get it modelled properly