Free interactive tool
Alert Capacity Planner: Can Reps Review Alerts in Time?
A visitor alert is only useful if a rep reviews it while the visit is still recent. Whether that happens is arithmetic: how many identified accounts pass your alert filters each day, how long one review takes, how much time the reps really set aside, and how often they look at the queue. This planner computes whether every alert is reviewed before your deadline and, if not, which single change would fix it: a tighter filter, more reps, more review time, or more frequent checks. It turns “alerts are noisy” into a number you can set a threshold from.
The formula
alerts per day A = identified accounts per day × pass-through review load L = A × minutes per alert capacity K = reps × review minutes per rep per day utilization U = L ÷ K (over 100%: the backlog grows every day) alerts per check n = A × check interval ÷ business hours time to clear t = n × minutes per alert ÷ reps longest wait W = check interval + t meets the deadline when U ≤ 100% and W ≤ deadline
This is a batch model of a shared queue: alerts arrive evenly through the business day, and at each check the reps split the waiting alerts between them. The longest wait belongs to an alert that arrives just after a check and is reviewed last in the next batch. Each suggested fix changes one input and holds the others; for example the highest pass-through is the smaller of K ÷ (accounts × minutes per alert) and the rate at which W equals the deadline. No fix to the filter or the headcount helps when the check interval is already as long as the deadline; only checking more often does.
Run your numbers
Before any alert filter. Average of the last four weeks.
Alerts sent ÷ identified accounts, same weeks.
Open, check fit and history, decide. Time a sample of ten.
Time actually set aside, not the whole day.
- Alerts per day
- 18
- Review minutes needed per day
- 108
- Review minutes available per day
- 90
- Utilization
- 120%
- Alerts waiting at each check
- 4
- Longest wait
- 2 h 8 min
What would meet the deadline (each on its own)
- Tighten the alert filters to at most 12.5% pass-through (15 alerts a day).
- Or share the queue between 4 reps at the current filters.
- Or give each of the 3 reps 36 review minutes a day.
Alert review capacity — visitorops.com/tools/alert-capacity-planner Inputs: 120 identified/day · 15% pass · 6 min/alert · 3 reps × 30 min · 9 h day · check every 2 h · deadline 4 h Alerts/day 18 · load 108 min · capacity 90 min · utilization 120% Batch per check 4 · clear in 8 min · longest wait 2 h 8 min · misses the deadline Tighten the alert filters to at most 12.5% pass-through (15 alerts a day). Or share the queue between 4 reps at the current filters. Or give each of the 3 reps 36 review minutes a day.
The pre-filled values are a hypothetical example, not a benchmark. Nothing you enter leaves your browser.
Worked example (hypothetical numbers)
A team’s tool identifies about 120 accounts per business day, and 15% pass the alert rule: 18 alerts a day (hypothetical numbers, the tool’s default input). A review takes about 6 minutes. Three SDRs share the queue, each with 30 minutes a day set aside, and they check it every 2 hours of a 9-hour day. The deadline is 4 business hours.
The queue needs 108 review minutes a day and the team has 90: 120% utilization. The timing itself is fine (4 alerts wait at each check, cleared in 8 minutes, so the longest wait is 2 h 8 min), but the extra 18 minutes a day pile up, and by the end of the week alerts are days old. Any one of three changes fixes it: tighten the rule to 12.5% pass-through (15 alerts a day), add a fourth rep, or set aside 36 minutes a day per rep. The team tightens the rule first: it adds the alerts guide’s suppression for anonymous-only visits to low-intent pages, which removes the alerts reps were least likely to act on.
Where do the inputs come from?
- Identified accounts and pass-through. From the last four weeks: identified accounts per business day in the vendor’s export, and alerts actually sent in the same days. If you are designing a new rule, count how many of last month’s identified accounts would have met it.
- Minutes per alert. Time ten real reviews, from opening the alert to the decision (act, hold or suppress), including the CRM lookup. Use the average, not the fastest.
- Review minutes per rep. The time reps really spend on the queue, not their working day. If nobody set time aside, ask the reps what they spend now; that is the capacity.
- Deadline. Your own rule, in business hours. The follow-up SLA matrix sets windows by tier (same business day for the strongest tier); a review deadline has to be shorter than the follow-up window it feeds.
Which fix should you pick?
- Tighten the filter first when the alerts that would drop out are the weak ones: a single blog visit, an account with no owner, or an unverified match. The Slack alert rule template lists the conditions to add. Raising the filter bar that way also raises the share of alerts worth acting on, which a headcount change does not.
- Add time or people when the alerts that would drop out are ones you want: target accounts on pricing pages, open opportunities, closed-lost accounts returning.
- Check more often when utilization is fine and only the wait is too long. Two fixed checks a day cannot meet a 2-hour deadline, whatever the headcount.
Review the utilization at the weekly intent review. A queue above about 85% has no room for a busy day: our rule of thumb, not a measured figure, and the planner flags it.
When is the model too simple?
- Busy days. The model uses an average day. Run it again with your busiest day of the last four weeks; if that day fails, alerts go stale on the days that matter, such as after a campaign launch.
- Night and weekend arrivals. Alerts from outside business hours wait for the first check of the morning. Check that batch on its own: overnight or weekend alerts × minutes per alert ÷ reps must fit inside the deadline counted from the start of the day.
- Owned accounts. If each alert goes to the account owner instead of a shared queue, run the planner for the busiest owner with that owner’s accounts and one rep.
- Reviews that vary a lot. If some reviews take two minutes and others twenty, the average hides the long ones. The longest wait is then longer than shown; keep more slack.