Seasonal Campaign Brief Generation
A new campaign brief for this year's holiday season gets written mostly from scratch, with someone loosely recalling which offers performed well last November without pulling the actual numbers, because digging through last year's campaign reports across email, paid and social platforms takes real time that a brief deadline doesn't leave room for. The result is a brief built on general impressions rather than what the data actually showed — which subject lines had the strongest open rates, which offer converted best, which send time and day performed worst — so the same avoidable mistakes and missed opportunities from last year quietly repeat.
STARTING PRICE
From €299
Standard tier · Multi-step workflow with AI extraction/decisioning and 2-3 integrations.
Get a quote →Saves roughly 3-5 hrs per campaign cycle in manual historical research and first-draft writing.
How the automation works
We pull the prior year's performance data for the same or a comparable seasonal campaign — email open and click rates by subject line and send time, paid channel performance by creative and audience, top-converting offers and underperforming ones — and draft a starting brief grounded in what actually happened rather than general recollection. The draft brief highlights specific, actionable callouts: which send time to avoid repeating, which offer structure converted best, which audience segment underperformed and might need a different approach this year. A marketer reviews and adjusts the draft against this year's specific goals and constraints, starting from an evidence-based first draft instead of a blank page.
Process flow
- 01
Seasonal planning cycle begins trigger
The seasonal campaign planning window opens, triggering a pull of the prior year's comparable campaign performance data as the basis for this year's brief.
- 02
Pull prior year performance data integration
Email, paid and social performance data from the prior year's comparable campaign is pulled together, covering subject lines, send times, creative performance and offer conversion rates.
- 03
Identify what worked and what didn't ai
Top-performing and underperforming elements from last year are identified specifically — not a general summary, but concrete callouts like which subject line pattern drove the highest open rate or which send time underperformed.
- 04
Draft the campaign brief ai
A draft brief is generated incorporating the prior year's data-backed callouts alongside a standard brief structure — goals, audience, offers, channel plan, timeline — ready for a marketer to refine against this year's specific context.
- 05
Marketer review and refinement output
A marketer reviews the draft, adjusting for this year's specific goals, budget and any changes in product or market conditions that the historical data alone wouldn't capture.
Inputs
- Prior year campaign performance data across channels
- Current year goals and budget parameters
- Brief template structure
- Product or offer changes since last year
Outputs
- Historical performance summary with specific callouts
- Draft seasonal campaign brief
- Identified top-performing and underperforming elements
- Marketer-refined final brief
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- A prior year's campaign performance that was shaped by conditions no longer true this year — a supply chain issue that limited a top-selling product's availability, a competitor promotion that pulled attention away — needs that context noted alongside the raw numbers, or the brief risks treating an anomalous result as a repeatable pattern.
- Historical data pulled from a year with meaningfully different total marketing spend or audience size will show performance rates that aren't directly comparable to this year's planned scale, so callouts about 'what worked' need framing against relative performance, not raw absolute numbers that don't scale proportionally.
- A draft brief that leans too heavily on repeating exactly what worked last year risks creative staleness — a subject line pattern that performed well once can fatigue an audience that's now seen it before, so the brief should flag proven patterns as a starting point for iteration, not a formula to repeat verbatim.
- Seasonal campaigns tied to a specific calendar date rather than a day-of-week pattern can have their historical send-time data misapplied if this year's seasonal date falls on a different day of the week, since day-of-week performance patterns don't automatically transfer to a different weekday.
Frequently asked questions
Does this replace the marketer's planning input?
No — it produces a data-grounded first draft that a marketer refines against this year's specific goals, budget and market context, replacing the blank-page starting point rather than the strategic decisions.
What if last year's campaign was affected by an unusual event?
That context should be flagged alongside the historical data so an anomalous result — a stockout, an unrelated market disruption — isn't mistaken for a repeatable performance pattern in this year's brief.
Can this work for a seasonal campaign we've never run before?
Only partially — without directly comparable historical data, the brief draft relies more on general campaign performance patterns from other campaigns, with less specific seasonal grounding.
Does it account for changes in product lineup or pricing since last year?
It surfaces the historical data as a starting point, but current product, pricing and offer changes need to be factored in during the marketer's review, since the automation can't know about changes not yet reflected in the data.