# Reading your analytics Source: https://help.didit.me/account-organization/analytics-dashboard Updated: 2026-10-12 Volume, conversion and funnel metrics per application: see where users drop off, which feature is driving your spend, and whether a change worked. **Analytics** shows volume, approval rate and the step-by-step funnel, per application. Open it with **Open Analytics** on Home, or from the sidebar where the redesigned page has reached you. Use the funnel to find drop-off and the per-feature volume to explain your bill - both are questions you otherwise end up guessing at. ## What's there [Video: Analytics, teams and roles - Didit Academy](https://www.youtube.com/watch?v=sRcOBxjRYag) Volume, conversion, and funnel metrics for KYC, KYB and transactions, with a dashboard you can arrange the way you like. The redesigned page described below, with its own **Analytics** entry in the sidebar and a tab per area (**Overview**, **Individuals**, **Businesses**, **Transactions**, **Funnel** and **World**, plus **Cases** where case management is available), is rolling out. Until it reaches you, **Open Analytics** shows the previous dashboard of widgets, with **Date**, **Workflow** and **Version** filters and **Export**. On the redesigned page: - Pick the date range, compare it with the previous period or the same period last year, and switch between **Live** and **Sandbox**. - **Customize** adds, removes and resizes widgets. Only you see your layout. - Each widget's menu can expand it, ask Ask Didit about it, **pin it to Home**, or open the list of records behind it. - **Export** downloads the dashboard, with its filters, as a spreadsheet. ![The Analytics page in the Didit console with key numbers and charts](https://help.didit.me/screenshots/analytics-light.webp) *Analytics: key numbers, trends and where your verifications come from, per application.* ## The three questions it answers well **"Where are users dropping off?"** The funnel shows completion per step, so you stop guessing. Drop-off is usually concentrated in one step, and the step tells you the cause - document selection means a coverage or subtype problem, camera means an environment problem, questionnaire means the form is too long. See [when a session never finishes](https://help.didit.me/verification-sessions/session-not-finished). **"Why is my bill what it is?"** Your bill is a function of completed features, so per-feature volume explains it arithmetically. A spend surprise is always a volume surprise on some specific feature. See [what counts as a billable check](https://help.didit.me/billing-credits/what-counts-as-billable). **"Did that change work?"** Compare before and after on the same metric. A threshold change, a step reorder, or a language fix all show up here if they mattered - and if they don't show up, they didn't matter. ## Metrics are per application Analytics is scoped to the application you have selected, like workflows and keys. If your traffic is split across applications, you're looking at part of the picture - which is worth remembering before concluding that volume fell. See [organizations, applications and environments](https://help.didit.me/account-organization/organizations-and-applications). Sandbox has its own metrics too, and they're meaningless as a measure of real behaviour - the outcomes were chosen, not measured. ## Reading the approval rate honestly A low approval rate has several very different causes, and the number alone doesn't distinguish them: - **Genuine fraud pressure** - the checks are working. - **Thresholds too tight** - you're declining real people. See [face match scores and thresholds](https://help.didit.me/liveness-biometrics/face-match-thresholds). - **Coverage gaps** - users can't use the document they hold, so they fail at selection. - **A review queue nobody works** - sessions sit In review and never become approved, which reads as a low approval rate. See [sessions stuck in review](https://help.didit.me/verification-sessions/session-in-review). Before acting on the rate, look at the mix underneath it. ## Freshness Dashboard figures can lag slightly behind individual sessions - a session you just completed may not be in the aggregate yet. If a number looks wrong immediately after a change, check the underlying session in **User verifications** rather than concluding the metric is broken. ## Getting the data out For your own reporting, export session data as CSV from the User verifications list or the **Reports** page, with the columns and filters you choose. See [reports and exports](https://help.didit.me/verification-sessions/reports-and-exports). For a per-session record, use the PDF. See [downloading a verification report](https://help.didit.me/verification-sessions/download-verification-report). If you need continuous data in your own warehouse rather than periodic exports, take it from [webhooks](https://help.didit.me/integration/webhooks-basics) and aggregate on your side - that's the pattern that scales, and it gives you metrics defined the way your business defines them rather than the way a dashboard does. ## Who can see it Analytics access follows role, like everything else in the console. If a teammate can't see the section, check their role first. See [inviting team members and setting roles](https://help.didit.me/account-organization/team-members-and-roles).