> ## Documentation Index > Fetch the complete documentation index at: https://docs.vortexiq.ai/llms.txt > Use this file to discover all available pages before exploring further. # Conversion Drop During Incidents, New Relic > Conversion Drop During Incidents for New Relic stores. Tracked live in Vortex IQ Nerve Centre. How to read it, why it matters, and how to act on it. **Metrics type:** [Cross-Platform Metrics](/nerve-centre/overview#metrics-types-explained) • **Category:** [Monitoring](/nerve-centre/connectors#connectors-by-type) ## At a glance > Per-incident bar chart showing the conversion-rate drop observed during each NR P1/P2 incident over the rolling 90 days. Each bar is one incident; the height is the percentage-point drop in conversion rate during the incident window vs the baseline for the same time-of-day. Answers "do my incidents actually move customer conversion, or are they internal-only?" | | | | ----------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **What it counts** | For each closed CRITICAL/HIGH NR incident in the window, computes `drop_pp = baseline_conversion_rate - actual_conversion_rate_during_incident` in absolute percentage points. The chart shows one bar per incident, sorted by recency, with the drop\_pp as bar height. | | **NerdGraph endpoint** | NR side: `actor.account.aiIssues.issues(filter: {states: [CLOSED]}) { issues { issueId, priority, createdAt, closedAt } }` for incident windows. Commerce side: order-and-session events from the connected commerce sibling for conversion-rate evaluation; or GA4 conversion events. | | **Metric basis** | Per-incident actual-vs-baseline comparison. `actual_conversion_rate_during_incident = orders / sessions` over the incident window; `baseline_conversion_rate` = rolling 28-day same-day-of-week, same-time-of-day average covering the same minute span. | | **Aggregation window** | One bar per incident, computed at incident close. The 90-day window is the chart range; individual incident windows vary from 5 minutes to several hours. | | **Browser vs APM scope** | NR side: incident state across all NR product lines (APM, Browser, Infrastructure, Synthetic, Logs). Conversion-rate side: full-funnel (sessions to orders) measured customer-side via GA4 or commerce platform analytics. | | **Severity threshold** | CRITICAL and HIGH only. P3/MEDIUM excluded because they typically don't produce measurable conversion impact, and including them would make the chart noisy. | | **Filtered hosts / services** | NR scope: all entities by default; can be narrowed to revenue-critical services. Commerce scope: full-funnel events for the merchant's primary store. | | **Sample basis** | NR incident state is unsampled. Conversion data sample basis depends on source: GA4 is sampled on free-tier accounts (typically 100% on paid); commerce-platform events are unsampled. | | **Time zone** | UTC for incident windows; merchant local timezone for "same time-of-day" baseline alignment. Baseline is a 28-day window. | | **Time window** | `90D` (rolling 90-day chart) | | **Alert trigger** | `>10% drop` (any single incident with >10 percentage-point conversion drop). Calibrated to flag incidents whose customer impact justifies a post-incident review. | | **Roles** | owner, marketing | ## Calculation Calculated automatically from your New Relic data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading. ## Worked example A Shopify Plus store reviewed at 90-day quarter-end on 02 May 26. The chart shows 14 closed P1/P2 incidents over the period: | Incident ID | Severity | Date | Duration | Baseline conv | Actual conv | Drop (pp) | | ----------- | -------- | --------- | -------- | ------------- | ----------- | ---------------- | | iss-7a01ab | CRITICAL | 14 Mar 26 | 22 min | 3.2% | 1.1% | **2.1pp (-66%)** | | iss-7a04c2 | HIGH | 18 Mar 26 | 8 min | 3.4% | 3.1% | 0.3pp (-9%) | | iss-7a08e5 | CRITICAL | 22 Mar 26 | 47 min | 3.1% | 0.8% | **2.3pp (-74%)** | | iss-7a0c11 | HIGH | 02 Apr 26 | 12 min | 3.5% | 3.4% | 0.1pp (-3%) | | iss-7a1234 | CRITICAL | 08 Apr 26 | 31 min | 3.3% | 2.4% | 0.9pp (-27%) | | iss-7a1789 | HIGH | 15 Apr 26 | 18 min | 3.2% | 3.0% | 0.2pp (-6%) | | iss-7a1a55 | CRITICAL | 21 Apr 26 | 9 min | 3.3% | 2.9% | 0.4pp (-12%) | | iss-7a1c88 | HIGH | 26 Apr 26 | 14 min | 3.4% | 3.3% | 0.1pp (-3%) | | ... | ... | ... | ... | ... | ... | ... | **Pattern reading.** Three of fourteen incidents (\~21%) drove >2pp drops, the catastrophic ones. Eight of fourteen (\~57%) drove \<0.5pp drops, statistically barely visible to customers. The remaining three were in the 0.5, 1.5pp band. **Three takeaways for the post-incident review:** 1. **Severity-to-impact mapping is bimodal.** CRITICAL incidents on `checkout-api` (the three big drops) consistently cost 2pp+ of conversion; CRITICAL incidents on background services (search, recommendations, account management) barely moved conversion. Severity is a coarse signal; *which service* is broken matters more. 2. **Duration matters but not linearly.** The 47-minute checkout incident cost 2.3pp; the 22-minute checkout incident cost 2.1pp. Once a customer hits a broken checkout page they generally walk away within the first 2 minutes, so additional incident duration is mostly affecting *new* customers arriving rather than further punishing existing ones. 3. **HIGH incidents on non-checkout services are noise.** Of the seven HIGH incidents, six were on infrastructure (memory pressure, log volume, CDN cache hit ratio) and showed \<0.3pp impact. The team can deprioritise post-incident reviews on these. **Conversion impact translation.** Across all 14 incidents in the 90-day window, total cumulative conversion-drop was \~9.4 percentage-point-minutes. With \~\~140 sessions / minute on average and AOV £85, the cumulative GMV impact across the quarter was roughly **£17,000**, dominated by the three big checkout incidents (\~\~£12,500 of that total). The chart makes the case for prioritising checkout reliability investment over generalised infrastructure hardening. **Apdex calibration interaction.** The three big-drop incidents all had Apdex below 0.5 (frustrated zone) sustained for >5 minutes. The eight small-drop incidents kept Apdex above 0.7 throughout. Apdex \< 0.5 is the *predictor* of meaningful conversion drop; this card is the *outcome* measurement. Pair with Apdex history to validate the link. ## Sibling cards merchants should reference together | Card | Why pair it with Conversion Drop During Incidents | | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------ | | [Active Incidents](/nerve-centre/kpi-cards/newrelic/active-incidents) | The live counterpart. While that card answers "is something broken now?", this card answers "did past incidents matter?" | | [Mean Time To Resolve](/nerve-centre/kpi-cards/newrelic/mean-time-to-resolve) | Pair to see whether faster MTTR correlates with smaller conversion drops. | | [Revenue Lost / Min](/nerve-centre/kpi-cards/newrelic/revenue-lost-min) | Dollar-impact view of the same per-incident relationship. | | [Cart Abandonment During 5xx Spikes](/nerve-centre/kpi-cards/newrelic/cart-abandonment-during-5xx-spikes) | Funnel-step view rather than full-funnel. Same direction; finer detail. | | [Operational Health Score](/nerve-centre/kpi-cards/newrelic/operational-health-score) | Composite parent. Big-drop incidents typically saw the score drop below 60; small-drop incidents kept it above 75. | | [Datadog Conversion Drop During Incidents](/nerve-centre/kpi-cards/datadog/conversion-drop-during-incidents) | Cross-connector peer. | | [GA4 Conversion Rate](/nerve-centre/kpi-cards/google-analytics/conversion-rate) | The conversion-rate input source. Open for raw rate series. | | [Shopify Sales / Min](/nerve-centre/shopify/sales_per_minute) | Volume-weighted view of the same impact. | ## Reconciling against the vendor's own dashboard **Where to look in New Relic:** NR doesn't render this per-incident conversion-impact view natively. The closest equivalent screens for the input signals: * [Alerts & AI > Issues & Activity](https://one.newrelic.com/alerts-ai) for the historical incident list. * [Dashboards](https://one.newrelic.com/dashboards) > custom dashboards (if conversion data is piped into NR via Custom Events). For conversion-rate validation, compare against the merchant's commerce platform's analytics (Shopify Analytics > Acquisition > Conversion rate, BigCommerce Analytics > Marketing) for the same incident windows. **Why our number may legitimately differ from the vendor's:** | Reason | Direction of divergence | | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------- | | **Account timezone vs UTC**. Incident windows are UTC; conversion baseline uses merchant local timezone for "same time-of-day". For incidents that span timezone boundaries (e.g., a US-East incident that runs across midnight UTC) the baseline alignment can produce 0.1, 0.3pp drift. | Either direction at boundaries | | **NRQL retention windows**. 90-day window uses NR's incident-history store (separate from raw event retention). Incident metadata is retained 13 months; the conversion-rate inputs may be on rolled aggregates past day 8 on standard plans, producing slightly coarser per-incident attribution. | Drift on older incidents \<0.5pp | | **Incident grouping evolution**. Applied Intelligence may regroup incidents post-hoc as more correlation evidence arrives; the chart uses the final grouping at incident close. Re-grouping rare. | None typically | | **Conversion source choice**. GA4 vs Shopify-native conversion can disagree by 5, 10% on absolute rate (different session definitions). The drop-pp metric is robust because both sides use the same source for actual and baseline. | None for drop-pp | | **Baseline window staleness**. 28-day baseline. If the merchant ran an unusually low-traffic campaign during the baseline period, baseline reads low and the incident drop reads low. | Drop understated | **Cross-connector reconciliation:** NR Alerts and Datadog Monitors are independent alerting systems; both track the same conversion-rate input (same commerce sibling or GA4 connector). For incidents that fired on both platforms with peer alerts, the per-incident drop should match exactly. Discrepancies trace to incident-coverage differences: an NR incident with no DD peer is on the NR chart but not the DD chart, and vice versa. The chart count is therefore platform-specific; the drop-pp values per matched incident should agree. NR APM-detected incidents vs GA4-observed conversion drops: NR catches the *cause* (server-side error or latency); GA4 measures the *effect* (customer-side conversion drop). The two views are complementary. When this card shows a 2pp drop attributable to an NR incident but GA4's overall conversion rate barely moved during that window, traffic-weighting matters: the incident affected a small slice of traffic disproportionately.
Same-metric peers on other connectors | Connector | Card | Notes | | --------- | ------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------- | | Datadog | [`dd_xc_conv_drop_on_incident`](/nerve-centre/kpi-cards/datadog/conversion-drop-during-incidents) | Same composite using DD incident state. Per-matched-incident drop-pp should match. |
## Known limitations / merchant FAQs **NR vs Datadog: should the per-incident drop figures match across both platforms?** For incidents that fired on both platforms with peer alerts, yes, exactly. The conversion-rate input is the same (same commerce sibling or GA4). Differences trace to incident coverage: an incident only on NR doesn't appear on DD's chart and vice versa. The total chart counts will differ; matched-incident drops will not. **Apdex math: does a low Apdex during an incident predict the conversion drop?** Yes, fairly strongly. In our 90-day data across hundreds of incidents, Apdex \< 0.5 sustained for >5 minutes correlates with >1.5pp conversion drop \~80% of the time. Apdex 0.5, 0.7 produces sub-1pp drops typically. Apdex above 0.7 rarely produces measurable drops. The exception: a very brief but very severe incident (say, 60s of total checkout failure) can produce a 2pp drop on the affected window without much Apdex movement because the duration is too short for Apdex's 5-min rolling window to fully capture. **NRQL retention vs incident retention: can I see 12-month incident history?** Incidents are first-class entities and retained 13 months by default. The conversion-rate inputs may be on rolled aggregates past day 8 on standard plans (or day 395 on Data Plus), so per-incident attribution gets slightly coarser on older incidents but the chart still works. **NR and Datadog disagree on incident count by 4 over 90 days, why?** Almost always alert-condition coverage difference. NR may have 4 conditions DD doesn't, or vice versa. Audit the alert inventory; once aligned, the count converges. The matched-incident drops should be identical regardless. **Sampling: does sampling break this card?** Incident state is unsampled. Conversion-rate input is unsampled (commerce platform) or sample-corrected (GA4 paid). The drop-pp number is robust. **Multi-account: my US and EU stores have different baseline conversion rates, can the chart handle both?** Yes. Connect each pair (NR account + commerce sibling) as a separate integration. Each gets its own chart with its own per-incident drops. Combined cross-region analysis is shown side-by-side in the Nerve Centre stack panel. **Ingest cost vs visibility: can I sample down without losing this chart?** Yes. Conversion-rate is from commerce platform / GA4, not from NR transaction events; NR ingest sampling doesn't affect this card. Sample down NR transactions freely; this chart remains accurate. **Alert tuning: my >10% drop alert fires for tiny incidents, what should I tune?** The 10% drop threshold is calibrated for *significant* incidents; if it fires too often, two options: (a) raise the threshold to 15, 20% if your baseline conversion rate is high enough that 10% is in noise territory; (b) require a minimum incident duration ("alert only if drop >10% AND incident lasted >5 min") to filter out flutter incidents that briefly spike conversion drop. Option (b) is generally preferred because most short incidents don't justify a post-incident review anyway. **My biggest drop incident shows -1.8pp but the duty engineer remembers it as a "minor" event, why does it look big?** Two usual causes: (a) the incident was at peak time when the absolute number of affected sessions is highest, even a brief disruption to checkout costs disproportionate conversion at peak; (b) baseline conversion-rate at that hour was unusually high (a marketing campaign was running), so the *relative* drop is larger than the engineer's gut reading. The chart is honest about customer impact; engineer perception is often calibrated to the technical severity of the fix, not the customer cost. **Why isn't `daily impact` in the chart instead of `per incident`?** Per-incident attribution is the actionable view. Daily aggregation would smear individual incidents together and lose the "which incident did the damage" signal that drives post-incident review prioritisation. For daily aggregation, see [Revenue Lost / Min](/nerve-centre/kpi-cards/newrelic/revenue-lost-min) summed over the day. *** ### Tracked live in Vortex IQ Nerve Centre *Conversion Drop During Incidents* is one of hundreds of KPI pulses Vortex IQ tracks across New Relic and 70+ other ecommerce connectors. Nerve Centre runs the detection layer; Vortex Mind investigates the cause when something moves; Ask Viq lets you interrogate any number in plain English. [Start for free](https://app.vortexiq.ai/login) or [book a demo](https://www.vortexiq.ai/contact-us) to see this metric running on your own data.