Give Marketing Teams Campaign Memory That Holds Every Brief, Asset, and Result
Marketing campaigns generate enormous context — briefs, audience insights, creative directions, performance data — that mostly vanishes once the campaign ships. MemoryLake gives small marketing teams persistent campaign memory across briefs, assets, and results.
Give Marketing Teams Campaign Memory That Holds Every Brief, Asset, and Result
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The problem: marketing teams lose campaign learnings the moment campaigns end
The lessons from the Q1 product launch should inform the Q3 launch. They live in old Notion docs nobody opens. Audience insights from one campaign don't transfer to the next. Every campaign re-derives context that the team already had.
How MemoryLake captures marketing campaign memory
Campaign event memory
Each campaign with brief, audience, creative, results.
Reflection memory for what worked
Patterns across campaigns surface.
Asset memory
Approved creative serves as reference for future work.
Cross-tool retrieval
Brief docs, creative tools, analytics platforms unified.
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How it works for marketing campaign memory
- Connect — Authorize brief, creative, and analytics tools.
- Structure — Each campaign becomes typed memory.
- Reuse — New campaign planning loads relevant prior campaign memory.
Before vs. after: marketing campaign memory
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Cross-campaign learning | Manual | Reflection memory |
| Audience insight reuse | Lost | Memory-driven |
| Creative reference recall | Manual archive | Memory-loaded |
| New campaign cold start | Common | Inherits learnings |
Who this is for
Small marketing teams at SMBs and agencies where campaigns happen frequently and the cumulative learning never compounds because nothing captures it.
Try this use case on your own account
Campaign memory keeps every number the team wrote down in its retros, and every one of them is true. But the email platform, the ads manager and the search console each count conversion their own way, and the creator's 12% comes from the creator's own recap. Ranked side by side, they say: lead the next launch with creators and email, trim search. A yardstick check traces each number back to the export it came from; measured one way, the ranking turns over.
What you will see
- One project holds the campaign exports from four tools (.xlsx), seven results as the team wrote them, pinned word for word with
fact add, and a "what worked" reflection built from them.fact update --metadatarecords each result's campaign, channel and source report - Planned from
search, the reflection comes first and the numbers agree with it: creators 12%, email 9.4%, paid social 3.8%, search 2.4%. The brief says lead with creators and email, trim search - The yardstick check reads each export as table rows with
search --types document, cited by cells, and finds the column whose cell equals the remembered number. Its header is the yardstick: 6 of 7 numbers true, measured 4 different ways, one of them unknown - Re-ranked from the one export that measures every campaign the same way, the brief turns over: lead with search and email. Written back as
yardsticklabels, one fact per campaign on the common yardstick, and the reflection rewritten in place withfact update --text. Plus a local web UI
You need: a MemoryLake API key · the memorylake CLI · Python 3.9+
Run it
git clone https://github.com/memorylake-ai/memorylake-usecases.git cd memorylake-usecases/campaign-results-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
Watch it run · real, unedited recordings
Related use cases
Frequently asked questions
Integrations?
Integrations?
Notion, Figma, Google Analytics, custom — all supported.
Privacy?
Privacy?
AES-256 E2E.
Free tier?
Free tier?
Yes — for small marketing teams.