Use Linear with an AI Browser for Campaign Research

If you use Linear and you regularly need to research a campaign or partnership before it ships, the bottleneck is usually the same: Linear holds part of the context, but campaign research also needs signals that live outside it - on the public web, in LinkedIn, in news, in other connected apps. Strawberry is built to combine the Linear context with the rest of the browser, and run the full workflow as a companion you can re-trigger every week.

This page describes specifically how Strawberry handles campaign research when Linear is one of the inputs. It names the Linear surfaces involved, the signals the workflow actually needs, an example prompt you can paste, and what a good output looks like.

The job a marketer, founder, partnership manager is trying to do

The goal of campaign research is to gather the context needed to brief, target, and de-risk a campaign before spending budget. The success metric is concrete: campaign launches on time, CAC within target, and creative does not need a rewrite mid-flight. That definition matters because it shapes what Linear needs to contribute to the workflow.

What signals campaign research actually needs

For each signal below, here is whether Linear can contribute directly or whether Strawberry has to find it via the browser:

• Audience segmentation (who exactly buys this) - Linear does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Competitor messaging already in the space - Linear does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Winning ad examples (Meta Ad Library, LinkedIn) - Linear does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Channel-fit (does the audience even read this channel) - Linear does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Creative reference library - Linear does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Compliance constraints (data, privacy, claims) - Linear does not contain this directly. Strawberry uses the browser plus public sources to fetch it.

What Strawberry can do inside Linear

Strawberry can read recent issues, create new tickets from external sources, and link related issues.

Linear surfaces Strawberry uses for this workflow: issues, projects, cycles, labels, states.

How Strawberry runs campaign research with Linear

1. Strawberry opens the Linear issues that contains the relevant context.
2. The companion pulls related context from Linear (projects, history, attached files) where it exists.
3. For the parts Linear does not store, Strawberry uses the browser - web search, LinkedIn, news, the prospect's website.
4. Strawberry synthesises the output in the shape this workflow needs: A campaign brief.
5. A human reviews before any external action (send, update, post). Then the approved output is saved back to Linear or your system of record.

Example Strawberry prompt

Paste this in a new Strawberry chat with Linear connected. Adjust the specifics to your actual ICP, role, or topic.

Read this Linear issues and any linked context.
Then run a full campaign research workflow on it. Use the browser to fill any gaps not in Linear.
Return the output in the shape we use for campaign research: A campaign brief: audience, channels, messaging hypotheses, creative refs, KPIs, risks.
Do not send anything externally. Save the draft to me to review.

What a good campaign research output looks like

Here is what a finished output for campaign research should look like in practice. The specifics will change for your use case, but the shape should look similar:

• Campaign: AI browser launch on Meta Ads - Nordic ICP
• Audience: founders + ops leads at 10-200 person SaaS companies in SE/DK/NO
• Channels: Meta Ads (primary), LinkedIn (secondary), founder LinkedIn organic
• Messaging: 'The browser that does the boring work' - 3 variants
• Risks: Meta still needs Business Verification stable; budget capped at €500/wk in test phase

Why Linear for this, and where to use a different tool

Linear is strong for this workflow because Strawberry can read recent issues, create new tickets from external sources, and link related issues.

Where Linear falls short Linear permissions are team-scoped; bulk create requires GraphQL mutations.

Consider also a CRM for go-to-market follow-up.

Common mistakes when running campaign research

• Skipping competitor analysis and rebuilding a positioning someone else already won
• Guessing at audience instead of pulling real segmentation
• No creative references so the team designs in a vacuum

Connecting Linear to Strawberry

Linear OAuth. Once connected, the companion can read the surfaces above without re-authenticating, and any write action still requires explicit human approval the first time the workflow runs.

Caveats

Do not let any AI agent send emails, update CRM records, or change shared systems without a clear approval step. Strawberry is strongest when the workflow combines browser context with connected-app context and a human review for sensitive actions.