Support Escalation Prediction
“Which tickets will escalate to engineering?”
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A real-world example
Which tickets will escalate to engineering?
Engineering escalations cost 8x more than L1 resolutions and take 5x longer. A SaaS company handling 5,000 tickets per month where 12% escalate spends $3.6M annually on engineering support time. Late escalations are worse: tickets that should have been escalated immediately but bounced through L1/L2 first have 3x longer MTTR and generate 2x more negative NPS responses. The escalation signal is in the intersection of ticket content, the reporting account's product usage anomalies, and recent deployment history.
How KumoRFM solves this
Graph-learned product intelligence across your entire account base
Kumo connects tickets, accounts, users, product events, and support agents into a graph. It learns that tickets from accounts that experienced a specific API error pattern in the last 24 hours, filed by users who previously had escalated tickets, about features deployed in the last sprint, escalate at 9x the base rate. The model captures agent-topic expertise (certain agents resolve specific issue types 3x faster) and account-level product stability signals that ticket text alone cannot convey.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
TICKETS
| ticket_id | account_id | user_id | category | priority | created_date |
|---|---|---|---|---|---|
| TK301 | ACC301 | U301 | API error | P1 | 2025-03-02 |
| TK302 | ACC302 | U302 | Feature request | P3 | 2025-03-02 |
| TK303 | ACC303 | U303 | Performance | P2 | 2025-03-01 |
ACCOUNTS
| account_id | plan | arr | health_score | csm |
|---|---|---|---|---|
| ACC301 | Enterprise | $240,000 | 45 | Lisa T. |
| ACC302 | Growth | $36,000 | 82 | Mike R. |
| ACC303 | Enterprise | $180,000 | 68 | Lisa T. |
USERS
| user_id | account_id | role | prior_escalations | tenure_days |
|---|---|---|---|---|
| U301 | ACC301 | Developer | 3 | 450 |
| U302 | ACC302 | Admin | 0 | 120 |
| U303 | ACC303 | Developer | 1 | 280 |
PRODUCT_EVENTS
| event_id | account_id | event_type | timestamp | details |
|---|---|---|---|---|
| PE01 | ACC301 | API 500 error | 2025-03-02 | endpoint: /v2/predict |
| PE02 | ACC301 | API 500 error | 2025-03-01 | endpoint: /v2/predict |
| PE03 | ACC303 | Slow query | 2025-03-01 | query_time: 12.5s |
AGENTS
| agent_id | name | tier | specialty | avg_resolution_hours |
|---|---|---|---|---|
| AG01 | Alex | L1 | General | 4.2 |
| AG02 | Priya | L2 | API issues | 8.5 |
| AG03 | James | L3/Eng | Backend | 24.0 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(TICKETS.ESCALATED_TO_ENG, 0, 48, hours) FOR EACH TICKETS.TICKET_ID WHERE TICKETS.PRIORITY <= 'P2'
Prediction output
Every entity gets a score, updated continuously
| TICKET_ID | ACCOUNT | CATEGORY | ESCALATION_PROB |
|---|---|---|---|
| TK301 | ACC301 | API error | 0.91 |
| TK302 | ACC302 | Feature request | 0.03 |
| TK303 | ACC303 | Performance | 0.42 |
Understand why
Every prediction includes feature attributions — no black boxes
Ticket TK301 -- ACC301, API error, P1
Predicted: 91% escalation probability
Top contributing features
Recurring API 500 errors (48h)
7 occurrences
32% attribution
User prior escalation history
3 past escalations
22% attribution
Account health score
45 (critical)
18% attribution
Feature deployment recency
/v2/predict updated 3d ago
16% attribution
Ticket priority vs account ARR
P1 on $240K account
12% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A SaaS company handling 5,000 tickets per month that routes likely-to-escalate tickets directly to L2/L3 reduces MTTR by 45% and saves $3.6M annually in engineering time. Kumo connects ticket context to product telemetry and account health, routing tickets to the right expertise level before escalation damage occurs.
Related use cases
Explore more B2B SaaS use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
