Maja Voje

The following is a newly written, style-informed example using the observable structural characteristics of the reference set. It is not a claim that this is an original historical post.

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Most GTM teams don't have a lead problem.

They have a decision problem.

The signals are everywhere. Job changes, product usage, pricing-page visits, funding announcements, champion moves.

But nobody has decided which ones should actually change what the team does.

🔷 𝟭. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝘀𝗶𝗴𝗻𝗮𝗹

A signal is not just an event. It is an event that changes the probability of a useful action.

A new VP hire might matter. A new VP hire plus an open RevOps role plus a spike in product usage matters more.

If you can't explain why the signal should change your next move, it is noise.

🔶 𝟮. 𝗥𝗮𝗻𝗸 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗻𝘁

Not every signal deserves the same response.

Separate them into three groups: fit, timing, and intent. Fit tells you who the account is. Timing tells you why now. Intent tells you what they might do next.

The best accounts usually have all three.

🟢 𝟯. 𝗣𝘂𝘁 𝗶𝘁 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗽𝗹𝗮𝗰𝗲

Signals need an owner.

→ Sales owns account-level changes that require a conversation.

→ Marketing owns patterns that should change the audience or message.

→ Customer success owns product usage, renewal risk, and champion movement.

If a signal has no owner, it is just an interesting notification.

🔷 𝟰. 𝗔𝗱𝗱 𝗰𝗼𝗻𝘁𝗲𝘅𝘁

One signal rarely tells the whole story.

Pull in the account's industry, size, current stage, open opportunities, previous conversations, and existing relationship. The rep should not have to reconstruct the account from six tabs before deciding what to do.

Why this account?

Why now?

What should we do next?

🔶 𝟱. 𝗧𝘂𝗿𝗻 𝗶𝘁 𝗶𝗻𝘁𝗼 𝗮 𝗽𝗹𝗮𝘆

A play has four parts: trigger, evidence, action, escalation.

→ Trigger: a champion changes jobs.

→ Evidence: the new company matches your ICP and has the problem you solve.

→ Action: create a warm introduction request and draft the relevant message.

→ Escalation: if the account is above your strategic threshold, route it to the named owner before anything goes out.

The escalation line is what keeps automation from becoming spam at scale.

🟢 𝟲. 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿

Don't measure how many signals your system finds.

Measure what happens after it finds them.

Track response rate, meeting rate, opportunity creation, time to action, and pipeline created by signal type. If job changes create activity but no pipeline, the signal may be interesting but not useful.

A dashboard should tell you which signals deserve more attention and which ones should be switched off.

🔷 𝟳. 𝗖𝗹𝗲𝗮𝗻 𝘂𝗽 𝘁𝗵𝗲 𝗺𝗲𝘀𝘀

Every quarter, remove signals nobody acts on.

Update the owners. Rewrite the plays. Kill the automations that create more review work than revenue.

The goal is not to build the biggest signal library.

The goal is to help the right person make the right move while the information is still fresh.

I run this from the CRM, with enrichment, product data, and call intelligence feeding the same account view.

No giant command center. No 47-tab operating model.

Just a small number of signals the team trusts enough to act on.

Which signal has actually created pipeline for your team?

🔗 Save this for your next GTM systems review.

♻️ Repost it to the person building another dashboard nobody checks.

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