Project Name
SLA Breaches Cut 80% With Random Forest ML Ticket Routing for Salesforce
A large-scale IT services organisation running a high-volume customer support operation entirely within Salesforce employed a significant pool of support engineers across multiple specialisation tiers, handling hundreds of tickets per day. Engineers cherry-picked easy tickets to inflate efficiency metrics, fabricated rejections to bounce complex cases, and the historical ticket data needed to fix the system was itself corrupted by years of this behaviour. SLA breaches were chronic and top engineers were burning out. Applying its AI-First approach, Ksolves replaced the manual dispatch process with a Machine Learning-powered routing engine that matches every ticket to the right engineer, first time, every time, with no human intervention in the assignment decision.
- System Gamification (Cherry-Picking): Engineers selectively accepted easy tickets to artificially inflate personal efficiency metrics, leaving complex, time-consuming cases unassigned or delayed - a behaviour structurally incentivised by the existing measurement framework and impossible to fix through policy alone.
- False Ticket Rejections: Engineers frequently rejected legitimately assigned tickets by citing inaccurate or fabricated reasons, causing tickets to bounce between queues, accumulate wait time, and arrive at resolution significantly delayed.
- Chronic SLA Breaches: Complex tickets that were deprioritised or repeatedly bounced ultimately reached engineers who lacked the expertise to resolve them efficiently, causing resolution times to spike and SLA commitments to breach at an operationally damaging rate.
- Engineer Burnout and Customer Frustration: Top-performing engineers disproportionately absorbed the most complex cases that junior engineers avoided, creating unsustainable workloads while customers experienced delayed and inconsistent resolutions.
- Severely Degraded Historical Data Quality: The historical ticket dataset, the foundation required for any ML model, was heavily mislabelled and corrupted by years of behavioural gaming - substantial remediation was required before the data could support reliable model training.
- No Workload Balancing Mechanism: The existing process had no mechanism to consider an engineer's current active ticket load when assigning new work. Overloaded engineers received new tickets at the same rate as available ones, accelerating burnout and reducing throughput.
Ksolves designed a multi-stage intelligent routing engine that removes human discretion from the assignment decision entirely. The solution combines rigorous data remediation, predictive ML modelling, real-time workload scoring, and seamless Salesforce integration - governed by a principle of fairness: every engineer receives work commensurate with their demonstrated capability and current capacity, and no ticket is ever assigned to the wrong person again.
- Django and REST API Foundation: A secure API backbone hosting all ML inference, scoring, and routing decision logic - the backbone connecting the routing engine to Salesforce in real time.
- Data Preprocessing Pipeline: Custom ETL cleansing and re-labelling years of mislabelled historical ticket data corrupted by behavioural gaming, and engineering the features required for reliable model training.
- Eligibility Filtering (Hard Constraints): A filtering layer removing unavailable engineers from consideration based on shift, availability, and PTO status before any ML scoring is applied.
- Predictive ML Engine (Base TTR Score): Random Forest Regression predicting a baseline Time-to-Resolution per engineer based on historical ticket category and complexity patterns.
- Dynamic Workload Penalty Matrix: A scoring layer adjusting raw ML TTR scores with real-time workload penalties - preventing engineer overload and starvation by factoring current active ticket load into every assignment decision.
- Automated Execution and Fallback Failsafe: The routing decision is written back to Salesforce via REST API automatically, with a Manager Queue Fallback activating when no eligible engineer is available.
Technology Stack
| Category | Technology |
|---|---|
| Machine Learning | Random Forest Regression |
| Backend Framework | Python / Django |
| Integration Layer | REST APIs / Salesforce |
| Data Engineering | Custom ETL Pipeline |
| Scoring Engine | Dynamic Penalty Matrix |
- SLA Breach Rate Reduced by Approximately 80% (Target): ML-matched assignments align ticket complexity with engineer capability on first touch, eliminating the routing gaps that drove breaches - target breach reduction of 80% based on engagement projections.
- First-Touch Assignment Accuracy Transformed: Algorithm-driven assignment maps every ticket to the optimal engineer on first assignment, with backend validation eliminating false rejection pathways that previously bounced tickets between queues.
- Engineer Workload Distribution Equalised: The Dynamic Workload Penalty Matrix distributes assignments across the full eligible pool in proportion to capacity - reducing overload on senior engineers and developing junior engineer capability through exposure.
- Ticket Processing Volume Scaled Significantly: The automated routing engine handles materially higher daily ticket volumes than the legacy process supported, with no incremental coordination overhead as volume grows.
An IT services organisation whose manual ticket dispatch process was actively gamed by engineers – cherry-picking, fabricated rejections, chronic SLA breaches, and corrupted historical data – was transformed into a fully automated ML-driven routing operation through Ksolves AI/ML consulting services. Random Forest Regression now assigns every ticket to the optimal engineer based on demonstrated expertise, predicted resolution time, and real-time workload capacity, with no human intervention and a Manager Queue failsafe for edge cases. SLA breaches are targeted for an 80% reduction, workload is equalised across the team, and the routing infrastructure can extend to additional queues, product lines, or geographies without rebuilding the core engine.
Is Your Support Team Still Assigning Tickets by Hand and Watching SLAs Slip Because of It?
