Zoho Ticket Sentiment × Dental Intel — Drillable

Click any chart segment, badge, pill, or table row to filter. Real SGA practice TTM production joined with synthetic ticket sentiment for pipeline proof.

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Tickets
Negative
High escalation
Top root cause
Avg sentiment
Tickets / $100K TTM

Sentiment click a slice to filter

Escalation risk click a slice to filter

Root cause click to filter

Requester state click to filter

Top emotions click a bar to filter

Tickets vs TTM production real Dental Intel TTM through · X = $TTM, Y = tickets · bubble size = tickets, color = avg sentiment

Each bubble is a practice. Bubbles above the trendline generate more tickets than their production predicts (IT friction tax). Below the line = either healthy or silently suffering (low volume from giving up asking).

Practice leaderboard real SGA practices · click a row to filter

PracticeTTM $TicketsT/$100KNegAvg% neg

Category heatmap click a row to filter

CategoryTicketsNeg% neg

Tickets

TicketPracticeCategoryPri ScoreEmotionsEscCause StateConfModel
Methodology + column legend

Score -1 to +1 (sentiment); Conf model self-reported (0-1). Emotions multi-label. Esc model-judged escalation risk with reason (hover the row).

Routing: short/low-priority → Haiku 4.5; long/urgent/keyword-flagged → Sonnet 4.6.

Data: 50 English rows from Tobi-Bueck/customer-support-tickets, reshaped into Zoho schema with synthetic SGA practice metadata. For dental-shaped signal, feed a real Zoho Desk export.