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Seasonal Demand Analysis and Marketing Budget Calendar
Everyone knows the season: back to school, November sales, shopping before the holidays. What nobody knows is the exact week demand starts to move for your brand and when ad costs begin to climb. We build a season curve specific to your brand from past years' data and place the budget week by week along it. AI models help us build the curve faster; our team approves the final calendar.
Purpose and appearance
What do we use this service for?
With seasonal analysis we see in advance which week demand rises for your brand and prepare budget, creative and stock on time. The goal is not to walk unprepared into expensive weeks, to reach new audiences before the season and to cut unnecessary spend off-season. The resulting calendar becomes the basis for budget and campaign timing in the 90-day plan.
Funnel stages it serves
- Awareness
- Consideration
- Conversion
- Loyalty
Why the Türkiye calendar cannot be read with an off-the-shelf model
Ramadan Feast and Feast of Sacrifice follow the Hijri calendar, so they move about 11 days earlier every year. A model that stacks past years by calendar week puts the holiday effect in the wrong week every year. We flag the holidays, Mother's Day and the November sales period as moving dates and align demand by the number of days left until them.
We also look at cycles within the month. In some categories demand fluctuates during the month; we check in your own data whether order counts or average basket value change after the days when salary payments cluster. This breakdown disappears in an annual curve but translates directly into the weekly budget plan.
What data we use to build the seasonality curve
Seasonal analysis that looks at a single source often misleads: site traffic can rise while sales stay flat, and searches can climb before cost per click has moved. We combine sources in your BigQuery project and fix in writing, before the analysis, what each source does and does not show. Timing questions are answered from Trends and Search Console, budget size from sales and cost data.
- GA4 history: weekly sessions, conversions and revenue. GA4's BigQuery export does not work backward, so the earlier we connect it, the longer the raw data history.
- Sales data: real revenue including marketplace, store and phone sales. Site data alone hides part of demand.
- Google Trends: relative interest in category searches. Values are relative scores from 0 to 100, so we use them to read timing, not search volume.
- Ad cost history: the weeks when cost per click and CPM rose. Demand and cost may not rise in the same week; the gap between them is the real window of opportunity.
- Search Console: query data for the last 16 months; shows which searches rise before the season.
What we ask the AI models, and how we check the result
We give OpenAI and Claude models the weekly data from past years and the moving-date flags, and ask each separately for the season start, peak and decline weeks. If the models show different start weeks, our team goes back to the raw data and checks which year caused the deviation. If sales dropped one year because of a stock problem or a site outage, that year does not enter the curve uncorrected.
- How many weeks before the peak do category searches start to rise?
- Does cost per click climb before or after demand?
- Does budget spent after the last shipping day before a holiday turn into sales?
- In the week we raised budget last year, did the extra spend bring extra sales?
From the seasonal calendar to a 90-day plan
The output has two parts: a season calendar showing which week campaign, creative and stock preparation should start, and a weekly budget curve. Budget increases are loaded not onto the peak week but a few weeks earlier; weeks that stay cheap before demand in the cost history are the best window for reaching new audiences. We also add competitors' past campaign periods from our competitor analysis to the calendar.
The calendar goes straight into the 90-day marketing plan. During the season we compare actual demand with the curve each week and update the curve if the deviation is large. We use seasonality adjustments in Google Ads only for short events of 1-7 days; Google does not recommend them for long periods either. When the season ends, we add the actual curve to next year's data.
Seasonal Behavior Analysis: when do we recommend it?
We recommend it
- E-commerce, tourism and education brands whose sales swing clearly during the year
- Accounts that spend most of their budget around holidays, back to school or November sales
- Brands that want to align stock and production plans with the ad calendar
We don't recommend it
- New brands with less than two years of history; in that case we build a rougher calendar from Google Trends and category data
- Subscription or B2B services whose demand stays flat all year
How we manage Seasonal Behavior Analysis
- Moving-date flagsWe flag past years' holiday, Mother's Day and November sale dates and align data by days left until them.
- Cleaning problem yearsWe note weeks with stock shortages, site outages or major price changes and keep them out of the curve until corrected.
- Demand and cost curveFrom Trends, Search Console and sales we find demand weeks, and from ad cost history the weeks when CPC and CPM rose.
- Timing with two modelsWe have two models calculate start, peak and decline weeks separately and check diverging weeks in the raw data.
- In-season deviation trackingWe compare actual demand with the curve each week and update the budget curve if the gap grows.
Seasonal Behavior Analysis: metrics and requirements
Metrics we track
- Season start week
- Weekly demand deviation
- Cost per click index
- Extra sales from extra budget
- Pre-season new audience share
What you need to get started
- At least two years of weekly sales and traffic data
- Google Ads and Meta account access for cost history
- Dates of past campaigns, stock issues and price changes
- A BigQuery project to combine historical data
Official sources
The Brand Move Data and AI Team
Contact us nowTo learn more about Seasonal Behavior Analysis or other AI-assisted analysis services, contact The Brand Move Data and AI team today.
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Frequently asked questions
Seasonal Behavior Analysis: frequently asked questions
What is seasonal demand analysis?
It is working out, from your own past data, when demand for your products starts rising, peaks and falls each year, and when ad costs move. The result is a season calendar and a weekly budget curve that tell you when to prepare creative, stock and spend.
Isn't Google Trends enough on its own?
No. Trends shows relative interest in a category; its link to your sales is indirect. Searches may rise early while your customers buy in the last weeks. We use Trends mainly for direction in brands with short data history.
If holiday dates move every year, is past data still useful?
Yes, if aligned correctly. We stack past years by days left until the holiday, not by calendar week. That way the pre-holiday shopping curve lands in the same place every year and can be moved to this year's date.
Does it make sense to stop ads completely off-season?
In most categories we do not recommend it. Campaigns that are fully stopped restart learning when reopened, and remarketing audiences shrink. We recommend lowering budget off-season while keeping brand and profitable keywords on, then raising it gradually before the season.
When should seasonal budget increases start?
A few weeks before the peak, not in the peak week. The cost history usually shows weeks where demand starts rising while costs are still low; that is the best window to reach new audiences before competition pushes prices up.
Let's run a first Seasonal Behavior Analysis review together.
In a free call we look at your competitors and seasonal data together and show you, with a concrete example, what AI-assisted analysis can deliver first.
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No sales pressure. Just findings.