SLA Management & Auto-Escalation
Define sophisticated service level agreements with multi-tiered response and resolution targets based on ticket priority, customer tier, issue type, and business impact. Unfound continuously monitors every ticket against its applicable SLAs, tracking time-to-first-response, time-to-resolution, and custom milestone deadlines you define. The system automatically escalates tickets approaching SLA breach thresholds, sending notifications to supervisors, reassigning to senior agents if needed, and adjusting priority levels to ensure critical deadlines are met. SLA rules can incorporate business hours calendars, holiday schedules, and timezone considerations to accurately track compliance across global operations. The platform dynamically adjusts SLA targets based on ticket complexity scores—recognizing that a simple password reset should resolve in minutes while a complex integration issue may require days—preventing the gaming of metrics that occurs with one-size-fits-all SLA approaches. Comprehensive SLA reporting shows compliance rates by team, agent, ticket type, and time period, identifying performance gaps and enabling data-driven improvement initiatives. Automated SLA management eliminates the manual tracking and spreadsheet juggling that consumes management time, ensuring no ticket falls through the cracks while providing the documentation needed for enterprise support agreements and compliance audits.
Predictive Workforce Planning
Optimize staffing levels and prevent both understaffing and overstaffing with Unfound's predictive analytics that forecast ticket volume with remarkable accuracy. The system analyzes historical patterns including seasonal trends, day-of-week variations, time-of-day fluctuations, and correlations with external events like product launches, marketing campaigns, feature releases, and known system incidents. Machine learning models identify complex patterns that simple trend analysis would miss—such as the cascading effect of a product release where initial high-excitement tickets give way to bug reports over subsequent weeks, or the correlation between blog post publications and specific question types. The forecasting engine generates staffing recommendations showing expected ticket volume by hour, required agent capacity to maintain SLA compliance, and suggested shift schedules that align resources with demand. Managers can model "what-if" scenarios to understand the impact of upcoming launches, plan for seasonal peaks like holidays or tax season, and optimize the balance between full-time staff, part-time agents, and outsourced overflow capacity. Accurate forecasting prevents the costly mistake of understaffing (which damages customer satisfaction and creates agent burnout) and overstaffing (which wastes budget on idle capacity)—many Unfound customers report 20-30% improvements in workforce efficiency simply from matching staffing levels to actual demand patterns revealed by predictive analytics.
Smart Assignment Learning
Unfound's assignment engine doesn't just follow static rules—it continuously learns from outcomes to improve routing decisions over time. The system tracks which agent-ticket pairings lead to successful outcomes across multiple dimensions: fastest time-to-resolution, highest customer satisfaction scores, best first-contact resolution rates, most knowledge base article creation, and highest quality assurance ratings. This outcome data feeds back into the routing algorithm, teaching it which agents excel at which types of problems, which pairings lead to frustrated customers despite technical resolution, which agents are developing expertise in emerging areas, and which assignment patterns optimize for team learning and skill development. The learning system discovers non-obvious patterns—perhaps Agent Sarah resolves billing inquiries 40% faster than team average but only when the customer is from the enterprise segment, or Agent Michael has exceptional satisfaction scores on complex technical issues but should be routed away from simple how-to questions where his detail-oriented explanations frustrate customers seeking quick answers. This nuanced understanding of agent capabilities goes far beyond the simple skill tags in traditional help desk systems, creating a dynamic matching engine that improves continuously and adapts automatically as agents develop new skills, team composition changes, and product evolution creates new support challenges.
Canned Response Library with AI Enhancement
Maintain consistency and speed with an intelligent library of pre-written responses for common scenarios, enhanced with personalization tokens and AI-powered suggestions for when to use each template. Unlike static canned responses that feel robotic and impersonal, Unfound's enhanced templates include dynamic personalization fields that automatically insert customer names, account details, product versions, specific error details from the ticket, and contextual information based on customer history. The system suggests relevant templates as agents work on tickets, learning from which templates are used for which ticket types and adapting suggestions to match each agent's writing style and preferences. Templates can include conditional content that automatically adapts based on customer segment, subscription tier, or issue details—showing enterprise customers different SLA information than free-tier users, including refund options only for customers within the refund window, or adapting troubleshooting steps based on the customer's product version. The library includes version control and approval workflows for regulated industries requiring compliance review of customer communications, usage analytics showing which templates are most effective at resolving issues without further follow-up, and A/B testing capabilities to optimize template wording for clarity and customer satisfaction. Agents can create personal templates for their individual workflows while team leaders maintain organization-wide templates ensuring brand voice consistency and regulatory compliance. The combination of speed (templates dramatically reduce typing time), consistency (ensure all agents provide accurate information), and personalization (dynamic fields create individually tailored responses) enables teams to scale support volume without sacrificing the personal touch that drives customer loyalty.
Customer Satisfaction Prediction
Don't wait for post-resolution surveys to discover unhappy customers—Unfound's predictive CSAT model analyzes ongoing conversations to identify at-risk interactions before tickets close, enabling proactive intervention to save customer relationships. The system evaluates multiple signals including customer sentiment in messages (analyzing language, tone, emoji usage, punctuation patterns), conversation length and reply frequency (excessive back-and-forth suggests confusion or frustration), time-to-response patterns (long gaps in agent responses correlate with dissatisfaction), issue escalation and reassignment (customers dislike being transferred multiple times), solution effectiveness indicators (did the suggested solution actually work), and historical customer satisfaction patterns (some customers are consistently harder to satisfy). When the model predicts a low satisfaction outcome, it triggers alerts for supervisors to review the interaction and consider interventions like personal outreach from senior agents, offering goodwill gestures such as service credits or discounts, escalating to product teams for deeper investigation, or providing additional training to agents struggling with similar cases. The predictive model enables support teams to focus quality assurance efforts on the interactions most likely to result in dissatisfaction rather than random sampling, ensuring limited QA resources have maximum impact. Analytics show prediction accuracy over time, calibrating confidence thresholds to balance between catching most at-risk interactions (high recall) and avoiding alert fatigue from false positives (high precision). Customers using this feature report 25-40% reductions in low satisfaction scores as teams shift from reactive damage control to proactive relationship management.
Knowledge Base Integration
Connect Unfound to your knowledge base, help center, documentation, and internal wikis to automatically suggest relevant articles to agents and customers throughout the support journey. When a customer submits a ticket, the system searches your knowledge base for articles matching the described issue and presents them in the customer-facing portal with messaging like "While our team reviews your request, these articles might help"—many customers find immediate answers and close their own tickets, reducing support volume. For agents, relevant articles appear in the ticket sidebar based on ticket content, classification, and extracted entities, providing instant access to troubleshooting procedures, known issues, product documentation, and resolution playbooks without requiring agents to manually search documentation. The system tracks which articles successfully resolve which types of tickets, surfaces this data to content teams to identify documentation gaps (frequent questions without good articles) and optimize existing content (articles that are viewed but don't lead to resolution), and feeds article effectiveness data back into the suggestion algorithm to prioritize the most helpful resources. Agents can send articles directly to customers with a single click, add personal context to explain how the article applies to their specific situation, and create new knowledge base entries from resolved tickets to capture solutions for future reference. The integration supports multiple knowledge base platforms including built-in systems, popular help center tools, wikis, and custom documentation sites via API connections. Advanced features include automatic article recommendations based on ticket classification before an agent even opens the ticket, self-service deflection tracking showing how many tickets were prevented by customers finding articles, and content optimization insights revealing which articles need updating based on follow-up questions and negative feedback patterns.
