How does Strkr detect that a deal has gone stale?
Strkr uses a combination of record-driven signals: time in current stage against the trailing twelve-month median (configurable multiple, 2x by default per stage and per segment), last meaningful activity date (meetings, two-way email threads, inbound signals rather than one-way outbound touches, with configurable thresholds of 14, 30, or 60 days per stage), close date position (deals with multiple close-date slips in a quarter), missing champion engagement (champion flagged on the deal who has not engaged in a configurable window), missing procurement signal (late-stage deals without a procurement contact or budget-authority confirmation), competitor mentions in the activity log, prospect email reply latency rising week over week, and meeting cancellation pattern (two or more cancellations without a rebook). Strkr AI combines the individual signals into a composite risk score per deal with the drivers surfaced so the AE and manager read the diagnosis rather than a black-box number.
What does the pipeline aging report actually show?
The aging report renders the pipeline with stale deals highlighted inline on the main pipeline view (color coding, icon, aging badge) rather than hidden in a separate tab. A rep-level roll-up shows stale count and dollar amount per rep so managers run one-to-ones with specific lists rather than general cleanup asks. A stage-level breakdown shows which stages accumulate the most stale deals (the qualification stage usually hides the most because the entry barrier is low). A segment slice separates enterprise, mid-market, and SMB posture with segment-specific thresholds. Age buckets separate recoverable (15 to 30 days), stretching (30 to 60), fading (60 to 90), and likely dead (90-plus). Every stale deal drills through to the detail with the drivers surfaced. A historical trend shows stale percentage over trailing quarters. The report schedules as a weekly snapshot to the RVP or sales ops lead.
What automated workflows ship to clean the pipeline?
The nine most-used unstick workflows are nudge the AE (deal stale 15 days DMs the AE with specific drivers and recommended action), escalate to manager (deal stale 30 days without AE action escalates in Slack with acknowledgment history), auto-move to nurture (low-signal stale deals cycle to marketing nurture segment at 60 days), auto-close-lost by policy (deals stale 90 days with specific dead signals auto-close-lost on configurable reason with AE reopen path), request close date confirmation (close dates in the past or outside the policy band prompt AE to confirm or update), champion refresh request (champion-stale deals prompt AE to confirm or replace), procurement signal check (late-stage deals without procurement contact prompt AE to add the contact or flag the gap), win-loss touch (closed-lost deals fire a learning touch asking the loss reason), and scheduled cleanup sweep (weekly flow runs the full detection logic, generates the hygiene report, DMs each AE with their specific stale list, and posts the aggregated roll-up to the sales ops channel).
Do stale-deal thresholds account for different sales motion lengths?
Yes. Every stale-deal threshold is tenant-configurable per stage and per segment. Enterprise motions with 12-month cycles use different thresholds than SMB motions with 30-day cycles. The time-in-stage threshold runs as a configurable multiple of the stage median rather than an absolute number of days, so the posture scales to the motion. The last-activity threshold ships with different defaults per stage (14 days for early discovery, 30 days for middle stages, 60 days for late-stage negotiations). The champion-engagement threshold differs by deal size and segment. The admin tunes the thresholds in the admin console and the automation respects the configuration on every evaluation. Strkr AI learns from the motion by watching which stale flags led to recovery versus close-lost and tunes the composite risk score accordingly.
How does cleaning stale deals actually improve the forecast?
Forecast accuracy correlates strongly with pipeline hygiene. A forecast model trained on historical win rate assumes the current pipeline matches the historical pattern. When 30 percent of the current pipeline is stale (deals that will not close in the forecast window), the model generates a confident number that is systematically wrong. Cleaning the stale deals through hygiene workflows (auto-close-lost on policy, auto-move to nurture, nudge-and-escalate) brings the pipeline back into alignment with the historical pattern, which tightens the forecast. Teams that run the Strkr hygiene workflows typically see forecast accuracy improve by 10 to 20 percentage points within two quarters. The CFO walks into the board with a credible bottom line rather than a bloated pipeline math problem. The correlation between hygiene and accuracy renders in the hygiene report so the leadership team sees the connection in real time.
Does auto-close-lost risk closing deals that would have recovered?
The policy is tunable and defaults conservative. Auto-close-lost only fires on deals stale 90 days with specific dead signals (champion departed, competitor won on evidence, prospect confirmed no decision in the activity log). The AE gets a notification before the close-lost fires and can reopen the deal if the signal is wrong. The auto-close effectiveness report tracks how often an auto-closed deal reopens (false positive) versus how often it stays closed (correct call) so the policy tunes toward the right balance. Teams that run the auto-close flow typically see a false-positive rate under 5 percent after the first quarter of tuning. The recovery rate report also shows the percentage of flagged stale deals that returned to active engagement through nudge or escalation rather than auto-close, so the hygiene policy balances cleanup against recovery.
