> ## 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.

# High-Frustration Sessions (review queue), FullStory

> High-Frustration Sessions (review queue) for FullStory stores. Tracked live in Vortex IQ Nerve Centre. How to read it, why it matters, and how to act on it.

**Card class:** [Hero](/nerve-centre/overview#card-classes-explained)  •  **Category:** [Sessions](/nerve-centre/connectors#connectors-by-type)

## At a glance

> **High-Frustration Sessions (review queue)** is the action card that turns frustration data into a worklist of replays to watch. It lists the sessions that scored above the frustration threshold, ordered so the team can open the worst ones first. Instead of guessing which of thousands of recordings to review, you get a prioritised queue of the sessions where visitors hit the most friction. It is where the gauges become specific, watchable evidence.

|                       |                                                                                                                                                                                   |
| --------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **What it counts**    | The sessions in the period that exceeded the frustration threshold, presented as a prioritised replay-review queue.                                                               |
| **Sample type**       | Backend API data from FullStory, refreshed on the standard data refresh.                                                                                                          |
| **Why it matters**    | It converts aggregate frustration into specific replays. A team can watch a handful of the worst sessions and see the exact moment things broke, far faster than reading numbers. |
| **Reading the value** | Treat any non-empty queue as work waiting. Watch the top sessions first; they carry the heaviest frustration and the clearest evidence.                                           |
| **Currency**          | count                                                                                                                                                                             |
| **Time window**       | `7D`                                                                                                                                                                              |
| **Alert trigger**     | `>0`                                                                                                                                                                              |
| **Sentiment key**     | `fs_high_frustration_sessions`                                                                                                                                                    |
| **Roles**             | owner, marketing                                                                                                                                                                  |

## Calculation

Calculated automatically from your FullStory data. Vortex IQ filters captured sessions to those whose frustration score exceeds the configured threshold and ranks them for the period so the most frustrated sessions surface first. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.

## Worked example

*A representative reading of **High-Frustration Sessions (review queue)** for a typical merchant on FullStory.* Suppose the queue holds 23 sessions this week. The top three all show the same story: a visitor reaches the payment step, the card field rejects valid input, and they rage-click before leaving. That is a reproducible defect found in minutes by watching three replays, rather than days of guessing from conversion charts. The team files one fix that clears most of the queue. For deeper investigation, use Vortex Mind to cluster the queue by root cause; for natural-language exploration, ask Ask Viq what the worst sessions have in common.

## Sibling cards merchants should reference together

| Card                                                                                                 | Why merchants reach for it                             |
| ---------------------------------------------------------------------------------------------------- | ------------------------------------------------------ |
| [`fs_frustration_score`](/nerve-centre/kpi-cards/fullstory/avg-frustration-score)                    | The score that decides which sessions enter the queue. |
| [`fs_checkout_path_frustration`](/nerve-centre/kpi-cards/fullstory/checkout-path-frustration-events) | Checkout sessions are common queue entries.            |
| [`fs_error_click_rate`](/nerve-centre/kpi-cards/fullstory/error-click-rate)                          | Error-click sessions often top the queue.              |
| [`fs_worst_frustration_pages`](/nerve-centre/kpi-cards/fullstory/worst-frustration-pages)            | The page view of the same frustration.                 |
| [`fs_rage_click_rate`](/nerve-centre/kpi-cards/fullstory/rage-click-rate)                            | A common driver of high-frustration sessions.          |

## Reconciling against the vendor's own dashboard

**Where to look in FullStory's own dashboard:**

In FullStory, build a session-search segment filtered to high frustration and sort by the frustration measure. Confirm the threshold and period match the Vortex IQ profile to reconcile cleanly.

**Why the Vortex IQ value may legitimately differ:**

| Reason                                                                                                        | Direction | What to do                         |
| ------------------------------------------------------------------------------------------------------------- | --------- | ---------------------------------- |
| **Threshold setting.** Where the "high frustration" line sits determines how many sessions qualify.           | Variable  | Align the threshold.               |
| **Period boundary.** Vortex IQ uses a 7-day window by default; FullStory dashboards may use calendar periods. | Variable  | Match the period range.            |
| **Segment scope.** Bot or internal traffic exclusion may differ.                                              | Variable  | Match segment and filter settings. |

**Cross-connector reconciliation:** complement with the frustration-score and worst-pages cards. For divergence investigations, use Vortex Mind.

## Known limitations / merchant FAQs

**Q: How often does High-Frustration Sessions (review queue) update?**
The card refreshes on the standard data refresh (typically every 30-60 minutes for live integrations). For real-time signals, force a manual refresh from the dashboard.

**Q: A big queue looks alarming, is it?**
Queue size scales with traffic and the threshold, so a large queue on a high-traffic site is not automatically a crisis. What matters is whether the top sessions share a common, fixable cause.

**Q: Why does my FullStory dashboard show a different number?**
The most common reasons are the threshold setting, period boundaries, and segment scope. Match these before assuming a real divergence.

**Q: Can I customise the alert threshold?**
Yes, the frustration threshold that feeds this queue is configurable per profile in the Sensitivity tab. Tune it so the queue stays a workable size.

***

### Tracked live in Vortex IQ Nerve Centre

*High-Frustration Sessions (review queue)* is one of hundreds of KPI pulses Vortex IQ tracks across FullStory 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.
