At a glance
The cart-abandonment rate observed during 5xx-spike windows, compared to the 30-day baseline outside those windows. For a merchant, this is “when the server returned errors, how many shoppers gave up?” Each percentage point above baseline is a quantifiable cost: shoppers who reached the cart but did not complete checkout because the site was broken. The card overlays Datadog 5xx spikes with the commerce-platform abandonment rate.
| API endpoints touched | Datadog Metrics API for 5xx-rate spikes (above 1% sustained 5 minutes); commerce-sibling KPI for abandonment rate (carts created vs orders completed). |
| Metric basis | Time-aligned 1-minute buckets in UTC: (1) 5xx rate from Datadog APM, (2) abandonment rate from the commerce sibling. Plotted on a single chart with abandonment-rate spikes annotated by their concurrent 5xx-rate. |
| Aggregation window | 1-minute rollup at source; the card displays a 30-day window with the abandonment baseline as a horizontal reference line. |
| Severity threshold | The card flags any 5xx spike where concurrent abandonment rate exceeded baseline by more than 5 percentage points. P1 for sustained 5xx above 5% with abandonment above 80%; P2 for 5xx above 1% with abandonment 5pp above baseline. |
| Alert pre-filtering | Synthetic test traffic excluded; health-check 5xxs excluded; non-storefront-channel orders excluded from abandonment denominator (B2B portal, draft orders, POS). |
| Log Management gating | Not used. The card consumes APM (5xx rate) and commerce-sibling KPI (abandonment); both are independent of Logs. |
| Commerce-sibling required | Required. Abandonment rate is a commerce-platform metric; without one connected, the card cannot function. |
| What “abandonment” means here | (carts_created − orders_completed) / carts_created × 100. A shopper who added items to cart but did not place an order in the same session is “abandoned”. Healthy baseline: 65-80% (most shoppers abandon; this is normal). A spike above this baseline during a 5xx window equals “the errors caused the abandonment”. |
| Time zone | UTC for cross-connector arithmetic; baseline computed over 30 days excluding 5xx-spike windows. |
| Why this card exists | To answer “when our server returned errors, did shoppers give up or did they retry?” Some incidents produce mass abandonment (impulse-purchase shoppers); others produce mass retry (high-intent shoppers). The card tells the merchant which kind of shoppers they have. |
| Time window | 30D (rolling 30 days) |
| Alert trigger | > 5% above baseline, abandonment 5pp above baseline during a 5xx spike pages on-call. |
| Roles | owner, marketing |
Calculation
Calculated automatically from your Datadog 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 US homewares brand on BigCommerce. 30-day baseline abandonment rate: 71.4%. The card surfaces three 5xx spikes from the past 30 days.| Date | 5xx peak | Duration | Abandonment during | Baseline | Excess abandonment | Estimated lost orders |
|---|---|---|---|---|---|---|
| 21 Apr 26 | 6.0% | 90 min | 88.5% | 71.4% | +17.1pp | 124 |
| 09 Apr 26 | 3.2% | 78 min | 78.2% | 71.4% | +6.8pp | 41 |
| 28 Mar 26 | 1.5% | 26 min | 73.9% | 71.4% | +2.5pp | 4 (within noise) |
- The 21 Apr incident produced a 17.1pp abandonment excess. This was the Stripe upstream PSP outage. With ~720 carts created during the 90-minute window, the excess abandonment translates to ~124 carts that would otherwise have completed at baseline. At AOV 4,600 in revenue.
- The relationship is roughly linear above 1.5% 5xx rate. At 1.5% the abandonment excess is barely measurable; at 3.2% it is +6.8pp; at 6.0% it is +17.1pp. The threshold below which 5xx rate produces no detectable abandonment excess is around 1% (which is why the alert threshold is calibrated there).
- Mobile shoppers abandon faster than desktop. The breakdown view (not shown in the table above) reveals: during the 21 Apr incident, mobile abandonment hit 94% while desktop abandonment hit 81%. Same 5xx rate, different shopper response. Action insight: mobile-heavy traffic mix means 5xx incidents cost disproportionately more.
- The card connects two metrics that live in different tools. Datadog has the 5xx rate; the commerce platform has the abandonment rate. Each tool can show its half, but neither can show the relationship. This card is one of the most valuable cross-channel surfaces in the manifest because the relationship is the actionable insight.
- Above 1% sustained 5xx, abandonment becomes measurably elevated. Below, no signal. The 1% threshold is empirically calibrated against many merchants. Use this when prioritising engineering fixes: a chronic 0.8% 5xx rate is not worth fixing for revenue impact alone; a chronic 1.5% rate is.
- Mobile shoppers are less patient than desktop. Brands with mobile-heavy traffic see disproportionately higher abandonment for the same 5xx rate. Action insight: if mobile is >60% of your traffic, your tolerance for 5xx is lower than the default. Tune the alert threshold downward (e.g. alert at 0.5% 5xx instead of 1%).
Sibling cards merchants should reference together
| Card | Why pair it with Cart Abandonment During 5xx Spikes | What the combination tells you |
|---|---|---|
| 5xx Response Rate | The driver: 5xx spikes feed this card’s correlation. | When 5xx is up but abandonment is flat, shoppers are tolerating the errors (high intent); when both are up, the errors are causing abandonment. |
| Conversion Drop During Incidents | The complementary measured-loss view. | This card focuses on cart-abandonment specifically; that card focuses on overall conversion. Both quantify customer impact during incidents. |
| Revenue Lost / Min | The live ticker during the incident. | This card is the post-incident view of the same financial reality. |
| Checkout Service Health × Sales | The latency-vs-orders dual axis. | Latency-driven abandonment vs error-driven abandonment are different patterns; pair to determine cause. |
| Top Errors | The triage view: which errors were dominant during the abandonment spike? | Helps engineering prioritise which 5xx pattern to fix first. |
| Cart Abandonment Rate / BC equivalent | The all-window baseline for abandonment. | This card uses that baseline as the comparison reference. |
| Stripe Decline Rate | Payment-side cause: when 5xx is downstream of payment-PSP issues, declines also rise. | Distinguishes “payment-PSP cascade” from “in-house code regression”. |
| GA4 Sessions | Independent traffic measurement during the window. | If GA4 sessions also dropped during the 5xx window, traffic dropped before reaching cart, making the abandonment denominator smaller. |
Reconciling against the vendor’s own dashboard
Where to look in Datadog:APM → Service List filtered by HTTP status code 5xx for the 5xx-rate driver. APM → Traces for the trace-level detail of 5xx requests. Dashboards → APM Overview for the time-series view.The abandonment side of this card comes from the connected commerce platform’s KPI; open that platform’s analytics for the same windows. Why our values may legitimately differ from a hand-aligned chart:
| Reason | Direction | Why |
|---|---|---|
| Time zone alignment | Either | Card aligns both axes to UTC for arithmetic; manual comparison using different timezones produces apparent shifts. |
| API rate limits | Brief gaps | Both APIs are rate-limited; cached values may be 1-2 minutes stale during burst minutes. |
| Log indexing latency | Not applicable | Neither axis uses Logs. |
| Cart-source filtering | Vortex IQ stricter | This card excludes B2B portal carts, draft orders, and POS carts from the abandonment denominator; the commerce platform’s all-channel abandonment may be different. |
| Order-completion attribution window | Either | Some commerce platforms count an order as “completed” only when payment confirmed; others when the order is created (paid or pending). The choice affects abandonment calculation. |
| Card | Expected relationship | What causes the divergence |
|---|---|---|
shopify.total_revenue / bigcommerce.total_revenue / adobe_commerce.total_revenue | Excess abandonment translates to lost orders; lost orders translate to lost revenue. | The card’s “estimated lost orders” should be consistent with revenue dips on the platform-side cards during the same window. |
google_analytics.ga_sessions | Sessions during the 5xx window may also be reduced if the spike caused some traffic to bounce before reaching cart. | If GA4 sessions are flat but cart-creation dropped, the issue is at the cart-add step (PDP errors); if both dropped, the issue is upstream (homepage / category errors). |
stripe.stripe_payment_health_score | Payment-side cause peer. | If Stripe health drops simultaneously, the 5xx spike is downstream of payment-PSP issues; if Stripe health stable, the cause is in your code or another upstream. |