Vitally Web Customer Risk Badge

WHAT IT DOES

This userscript helps customer success managers monitor customer risk by displaying risk scores and alerts directly on Vitally dashboards. It scans Vitally customer success dashboard pages for customer risk scores, churn risk indicators, and account health metrics. It then displays a visual badge or panel showing the overall risk level, risk drivers (e.g., low engagement, high support tickets, declining usage, expiring soon, poor sentiment), and recent changes in risk status. The script highlights high-risk accounts with color-coded warnings, shows the risk score history, compares risk scores across different cohorts, and displays a mini trend chart of risk over time. It categorizes accounts by risk level (Low, Medium, High, Critical) and provides alerts for accounts that have recently become high-risk (e.g., "Account risk increased from Low to High in 7 days").

WHERE IT RUNS

The script runs on Vitally customer success dashboard pages. It works entirely within your browser, reading only the visible content of the current page. It does not track your browsing history, access other tabs, or send any data externally. All risk data is processed locally.

HOW TO USE

After installation, open the panel by clicking the tool's icon in the BotGentz sidebar. The script auto-scans Vitally dashboard pages on load (toggle this on/off). Choose your display mode—"Badge only" shows a floating badge in the corner, "Panel only" shows details in the panel, or "Both" shows both. Toggle "Show risk drivers" to display the factors driving each account's risk level. Click "Scan risk data" to manually analyze the page. The badge displays the number of high-risk accounts, medium-risk accounts, and the top risk driver. The panel shows a full breakdown: each account's name, risk level with color coding, risk score, previous risk status, last updated date, risk drivers with emoji tags, and a mini trend chart. Use "Clear data" to remove all overlays.

THE TECHNICAL MECHANISM

This script uses DOM traversal and pattern matching to extract risk data from Vitally dashboard pages. It scans for risk elements using selectors like .risk-score, .customer-risk, .account-risk, .health-score, [class*="risk"], and [class*="health"]. For each element, it extracts the account name from title elements, risk level from text content (Critical, High, Medium, Low), risk score using "risk score: X" patterns, previous risk level using "previous: X" patterns, and risk drivers from a list of keywords (low engagement, high support tickets, declining usage, expiring soon, at risk of churn, poor sentiment). It also extracts last updated dates. The script counts high-risk accounts and calculates summary statistics. Mini trend charts are generated using canvas with simulated trend data based on the account's risk level and score. All settings are persisted locally.

THE PANEL

The panel is draggable anywhere on the screen and remembers its position between sessions. It can be collapsed to a small floating bubble to minimize distraction, and expanded again with a single click. The panel's width can be adjusted, and pressing the Escape key closes it instantly. All settings—display mode, show drivers, and auto-scan—are persisted locally using GM_setValue, so your preferences remain intact across page reloads and browser restarts.

PLEASE NOTE

This userscript requires the free BotGentz extension to function. The script is designed for Vitally customer success dashboards; it may work on other customer health platforms but is not guaranteed. The extraction relies on the current HTML structure of Vitally—if Vitally updates its UI, the script may need to be updated. The mini trend charts are simulated based on the current risk score and level, not actual historical data. Risk drivers are detected from keywords and may not capture all possible drivers. The script does not access your Vitally account or API; it only reads what is visible on the page. No data from your browsing is stored or transmitted anywhere—all processing occurs entirely in your browser.