Event detection vs. polling
Event detection identifies changes the moment they happen and pushes them to you; polling re-checks a source on a fixed schedule and reports whatever it finds at that time. The gap between the two is latency, and in GTM, latency is missed moments.
- Polling = scheduled re-checks (e.g., a nightly or weekly refresh). Event detection = continuous reading that surfaces changes as they occur.
- The practical difference is signal latency: how long after an event you can act on it.
- For fast-decaying trigger events (funding, leadership moves), the detection model determines whether the signal is still useful when it reaches you.
- This isn't an implementation detail; it's the difference between being first or forty-first into a buyer's inbox.
What's the difference?
Imagine a company closes a funding round on Tuesday at 9 a.m.
Under a polling model, a system re-checks its sources on a schedule, say, every night. The round won't surface until the next refresh runs, and then it sits in a queue before reaching a rep. By the time anyone acts, it's Wednesday afternoon and every competitor running the same dataset got the same alert at the same time.
Under an event-detection model, the system is reading sources continuously. It catches the announcement minutes after it breaks and pushes it straight to the owner. The rep reaches the buyer while the news is still fresh and the inbox isn't yet flooded.
Same event, same accuracy, with radically different outcomes, entirely because of when the signal arrived.
Why this matters for buying signals
Every buying signal has a decay curve, and the steepest curves belong to the highest-value triggers. A funding signal is worth most in the hours after it breaks; a leadership change signal is worth most in the new exec's first weeks.
Polling forces a structural delay onto every one of those signals. The faster the decay, the more damage the delay does. Event detection collapses that delay toward zero, which is the entire premise of signal-based GTM: you can't win on timing if your architecture is timing-blind.
This is also why "real-time" is worth interrogating. A vendor refreshing daily can still call itself real-time relative to a weekly competitor. The honest test is functional: does the signal arrive while it's still actionable? For high-decay events, that means sub-minute to sub-hour detection rather than a faster batch.
How polling adds latency (and where)
Total signal latency is the sum of several delays, and polling inflates the first one:
- Detection delay: time between the event and the system noticing it. Polling's weak point: bounded by the refresh interval.
- Processing delay: parsing, deduping, enriching.
- Queue delay: time the signal waits before reaching a rep, governed by lead routing.
Event detection attacks the first and largest source of delay. The metric that captures the whole chain is time-to-signal: ask any signal vendor for theirs on funding announcements; the answer reveals their architecture.
Polling vs. event detection
| Polling | Event detection | |
|---|---|---|
| Trigger | Schedule (hourly/daily/weekly) | The event itself |
| Detection latency | Bounded by refresh interval | Near-zero |
| Best for | Slow-changing attributes | Fast-decaying trigger events |
| Risk | Misses fast events; everyone gets it at once | Higher engineering complexity |
| GTM impact | Late, crowded outreach | Early, uncontested outreach |
Polling has its place: for slow-moving firmographics or technographics, a scheduled refresh is perfectly adequate. The mistake is applying a polling cadence to signals whose value lives and dies in hours.
How to evaluate a signal provider on this
- Ask for time-to-signal on funding. Minutes is good; "next refresh" is polling.
- Ask how they detect, not just what they cover. Coverage and accuracy are different axes from timing. See match rate.
- Check for source attribution. Continuous detection of primary sources should come with a verifiable link, not just a score.
- Test the decay case. Send them a recent, real event and ask how fast they'd have surfaced it.
Common mistakes
- Conflating "real-time" with event-driven. Marketing language often hides a polling cadence. Verify with time-to-signal.
- Optimizing coverage over timing. A 95% match rate on stale data loses to a smaller, faster feed on decaying signals.
- Fast detection, slow routing. Catching an event in minutes and then leaving it in a queue overnight wastes the advantage; fix lead routing too.
Frequently asked questions
Related terms
Sub-minute time-to-signal, with source attribution on every signal, so you reach the buyer first, not forty-first.