The Company Trigger-Event Reference: Proof and Shelf Life

This is a lookup table for the person deciding which company-level event in front of them is worth an email today. It is for founders selling their own product, account executives, SDR leads, and partnerships teams who already know how to write a sequence and need to know, per trigger, what it actually proves, the false positive that makes it misfire, how many days it stays warm, and how fast to move. Jump to the row you need and leave.

Most published answers stack 15 to 35 trigger events with copy-paste templates and never state, event by event, the specific way each one lies or a defensible decay window in days. This reference does the opposite. It carries fewer events, each with its shelf life, its failure mode, and its required response speed, so you can act on the one signal you are looking at without wading through templates.

The core reference: what each trigger proves and how long it lasts

Each company trigger event has a decay window (how long it stays actionable) and a required response speed (how fast a touch must land to catch the window). These are practitioner and vendor estimates, not published constants, so treat the day-counts as calibrated defaults rather than physical laws.

Read the two columns as different clocks. The decay window is how long the underlying business condition stays true. The response speed is how quickly you must move relative to competitors chasing the same event. Funding has a short response speed because everyone sees the same TechCrunch post at once; an executive change has a longer response speed because the reply advantage is not a race, it is a budget clock that runs for about 100 days.

What each trigger proves, stated plainly:

• Funding round. Proves timing: new money exists and is not yet committed. It never proves fit. According to Fundraise Insider data in the dossier, 71% of companies that close a round finalize vendor selections within 90 days, and funded companies are 3 to 5x more likely to buy new software within 12 months.
• Executive change. Proves a new mandate, if the role is net-new. 70% of a new executive's budget gets allocated in the first 100 days, so the window is a spend clock, not a courtesy call.
• M&A. Proves forced re-evaluation. The 100-day integration plan drives IT migration, accounting rebaseline, and supplier renegotiation, with vendor consolidation as an explicit KPI at days 30 to 90.
• Product launch. Proves the company is in motion and briefly attention-rich. The window is short, 1 to 2 weeks.
• Earnings. Proves budget conversations are underway. Best timing is 2 to 8 weeks after the announcement, when tools are still being evaluated.

How to read signal strength: correlation, not category

Not all triggers are worth the same. A 1-million-purchase analysis cited in the dossier ranks the strongest company signals by their correlation with an actual purchase, and the spread is wide enough to change your whole watch list.

The number that should change your behavior is the last row. Job postings alone correlate at +7%, near noise, while sustained headcount growth of 10%+ over 90 days correlates at +38%, roughly five times stronger. The mechanism is the difference: a single posting proves only that someone approved spend on one seat, which could be a backfill or a ghost job. Sustained headcount growth proves the function has outgrown its tooling and is a live buying condition.

Treat the correlation column as your first filter and the decay table as your second. AI tool adoption is the strongest single signal on the list, but if you cannot detect it inside its window it is useless. A weaker signal you can catch fast beats a strong signal you catch cold.

Stacking: the single biggest conversion lever

Two triggers on one account inside a short window beat one, and three or more is a priority call. Signal stacking converts because it removes ambiguity, not because it adds volume. A single funding round could be fit-blind; funding plus a net-new VP Sales hire at the same account tells you both that budget exists and that a new owner exists to spend it.

The documented lift is real. Signal-triggered outreach delivers a 37% win rate versus 19% for cold outreach, per Champify data in the dossier. The highest-converting pair on record is funding combined with a new VP hire, which is exactly the convergence stacking is meant to catch.

The practical rule for tiering: a single weak signal such as a hiring velocity spike is Tier 3, monitor-only. The same spike alongside a new VP Sales hire at the same account moves it to Tier 1 and earns a fast human touch. Do not route single weak signals to reps; route stacks.

Stacking converts because it removes doubt about whether, not because it adds names to the list.

Response speed: what the data supports and what it does not

Respond inside the trigger's window, but do not import inbound-lead SLAs as if they governed outbound triggers. The most-cited speed studies measured inbound leads, and the most-quoted figure has no traceable source.

Here is the honest state of the evidence:

• The Oldroyd and InsideSales study of 15,000+ leads found that contacting a lead within 5 minutes made you 21x more likely to qualify it than waiting 30 minutes. That is an inbound finding.
• The Blazeo 2026 benchmark found teams with a defined SLA respond within 15 minutes at 54.9% versus 29.5% without one, and that the first responder wins roughly 50% of competitive deals.
• The Harvard Business Review 2011 study of 2,241 US companies found a 42-hour average response time, the gap most of these figures are measured against.
• The widely cited claim that 78% of customers go with the first responder has no traceable original study. Treat it as directional.

For trigger events, the defensible reading is the response-speed column in the core table. Funding and product launches demand a touch inside 24 to 48 hours because they are races. An executive change gives you 30 to 60 days because it is a budget clock. M&A gives you 1 to 2 weeks before the news gets shared in a Slack channel and forgotten. Match your speed to the mechanism, not to a borrowed 5-minute rule.

The procedure: from trigger set to measured pipeline

Build the operating loop in eight steps, from choosing what to watch through measuring which triggers actually earn their place. This is the sequence that keeps the reference above from becoming a listicle you never act on.

One order disagreement worth flagging: some sources put seed-stage funding as monitor-first rather than immediate outreach. The reasoning is that a seed company has not yet built the team that buys, so you watch for the first wave of hiring posts, which usually appear two to four weeks after the announcement, and act then. For later rounds where the buying function already exists, move inside the 24 to 48-hour window.

Detection sources carry their own lag and blind spots, which is why step two is its own line item:

• Funding. SEC Form D filings make a round public regardless of a press release, so a reporter can surface the filing before the company announces. Do not assume the press release is the first public trace.
• Hiring. Job boards are noisy and latency varies; postings can stay live for months after the role is filled.
• Tech-stack. Web crawlers see only the frontend fingerprint in HTML, JavaScript, HTTP headers, and DNS, roughly 20 to 30% of a company's actual stack.
• Layoffs. The US WARN Act requires 60-day advance notice before covered mass layoffs at employers with 100 or more employees, so this signal is knowable ahead of the event.

How this goes wrong: the false positives that make triggers lie

Every trigger has a documented way of misleading you. This section carries the most weight in the reference, because knowing the false positive is what separates a signal from a coin flip. Check the "how to catch it" column before you act.

Each row expanded:

Backfill masquerading as a mandate. A single new-exec appointment or job posting reads like a fresh priority but may be a like-for-like replacement. A backfill is weaker signal than a net-new role, because a new role means a new priority. Acting aggressively on one generic posting is the most common mistake in signal-based outbound.

Fit-blind funding. The raise proves timing, never fit. A funded company that is not a fit for what you sell is just noise with good production values. Skip the fit filter and you have built a faster way to email the wrong companies.

Mislabeled funding stage. Press labels are loose. A round gets called "Series A" whether it is a first institutional raise, a follow-on extension, or a bridge round meant to stretch runway. A bridge extension often signals runway stress, not spend. Treating both the same wastes outreach.

Tech-removal that is a detection gap. A tool vanishing from crawl data may be a crawl miss, not a churn. Removals can be cleaner signals than additions, because they confirm the incumbent relationship ended, but only after you rule out the detection error. A job-posting shift, such as a company dropping Salesforce requirements and adding HubSpot, is a more reliable confirmation of an actual switch.

Ghost job postings. Roles stay live for months after being filled, faking a live buying window. The value sits in newly posted roles, not the standing backlog that half your competitors have already worked. Weight first-seen date, not the fact that a posting exists.

M&A slack-channel drift. This is a process failure, not a data one. Teams pick up M&A news when it is widely covered, share it in a channel, and tell themselves they will follow up. By the time that becomes structured outreach, the early window has closed. The fix is a structured trigger, not a manual watch.

When your trigger set has passed the fit filter and you need to actually find the named people at the accounts that stacked two signals, plain-English sourcing removes the manual list-building between spotting the event and touching the buyer.

I built Refolk so you can ask for exactly the stacked condition you care about and get the companies and the people at once, instead of maintaining separate funding, hiring, and tech-stack watch lists that drift out of sync.

Geography changes the math: pool size and decay

The same trigger yields a very different queue depending on where you sell, and that changes how much a wasted fit-blind touch costs you. In Refolk's index of professional profiles, the addressable buyer pool for a "new CRO" trigger is far thinner in some markets than others.

Two derived ratios matter. The US VP Sales and CRO pool is about 7.6x the size of the UK pool (5,179 divided by 681). And the US Head of RevOps buyer pool is about 20% the size of the US executive pool (1,032 divided by 5,179).

The implication for decay windows: in a thin market, every fit account you burn on a fit-blind trigger is proportionally more expensive, because there are fewer replacements. If you sell into the UK, where the identical "new CRO" trigger yields roughly one-eighth the queue, decay windows and the fit filter matter more, not less. You cannot afford to spend a fit account on a mislabeled bridge round or a backfill.

Keeping the reference current

Re-check the day-counts and correlations against your own results rather than trusting the defaults forever. The dossier's windows are practitioner and vendor estimates, and exact median detection lag per source is not publicly established, so your own per-signal dashboard is the only authority that stays true for your business.

Run this before you call the program healthy.

The discipline that holds all of this together is the same one that separates a trigger program from a listicle: you act on the operational implication of the event, name the problem it created for the buyer, and move inside the window the mechanism actually allows. The vendor post tells you the event happened. This reference tells you what it proves, how it lies, and how many days you have before it stops being true.