Improving first-contact resolution for technical support
Accelerate AI Adoption
Resolve Challenge
First-contact resolution (FCR) rate in technical support is improvable at the point where escalation decisions happen: inside the IT Service Management (ITSM) platform, during the ticket itself. Most ITSM systems store the knowledge agents need but do not surface it at the moment they need it.
Every time a support agent opens a ticket, they diagnose the issue, search for the resolution procedure, and follow the steps. The knowledge exists: resolution procedures, troubleshooting guides, configuration articles, known-error records. It sits in the knowledge base, the wiki, and the runbook repository. The agent’s challenge is finding the right article before the customer’s patience runs out or the service level agreement (SLA) clock triggers an escalation.
The resolution gaps follow predictable patterns. A common issue has three knowledge base articles because each was written by a different team in a different year. The agent opens all three, picks the wrong one, and the resolution fails. A Tier 1 agent receives a ticket about a system they have never supported. The knowledge base search returns forty results. The agent escalates instead of resolving. These are not knowledge gaps. The knowledge base contains the answer. The agent cannot find it quickly enough, and no amount of classroom training can replicate the search under time pressure with a customer waiting.
Our Solution
Userlane is a software adoption platform that works inside browser-based applications. For technical support workflows, it improves first-contact resolution where escalation decisions happen: inside the ITSM platform, during the ticket itself.
Surfacing the right procedure at the right moment
The Assistant, Userlane’s searchable help widget, gives support agents instant access to resolution procedures, troubleshooting guides, and knowledge base articles without leaving the ITSM platform. Agents search from the ticket screen and get results filtered to the issue type, the product category, and the customer’s configuration. No separate tab, no wiki search, no guessing which article is current.
Walking agents through complex resolution workflows
In-app guidance walks support agents through multi-step resolution procedures inside the ITSM platform: escalation workflows, warranty verification sequences, configuration changes that span multiple screens. It replaces the printed cheat sheet and the message to a senior colleague asking for the steps.
Tracking resolution quality over time
The HEART Score, Userlane’s application health metric, tracks the share of tickets resolved correctly on the first contact. The support operations team sees the breakdown by issue category, agent tenure, and shift pattern: where first-contact resolution improved and where escalation rates remain high.
Measure. Act. Prove.
Every deployment follows the same cycle. Measurement comes first, intervention second, proof third. The cycle repeats with each new workflow.
Measure
Userlane records every ticket interaction before any intervention starts. The data shows where agents escalate unnecessarily, so the team acts on evidence, not assumptions.
The HEART Score captures the baseline. Every ticket handling action is recorded: which issue types are resolved on first contact, which are escalated, and where agents spend the most time searching for procedures. Nothing changes for the agents. The ITSM platform works exactly as before.
A pattern emerges. Tier 1 agents escalate a specific issue type at three times the rate of Tier 2. One product category has a first-contact resolution rate twenty percentage points below the average. New agents escalate any ticket involving a system they have not seen before, regardless of complexity. The HEART Score puts a single number on each support workflow’s health.
The team sees the real problem. Not “agents need more product training.” Two issue categories need better knowledge surfacing. One product category has outdated procedures that agents do not trust. The intervention writes itself.
Act
The Assistant and in-app guidance deploy only where the measurement found escalation gaps. Targeting the intervention is what makes the result provable.
The Assistant activates. Only where escalation rates are high. Agents handling ticket types with low first-contact resolution rates get contextual search results filtered to the issue and the customer’s product configuration. Issue categories with high resolution rates see no change.
In-app guidance meets agents in the workflow. Complex resolution procedures (multi-step troubleshooting, warranty verification, cross-system configuration changes) get contextual help inside the ITSM platform. No separate wiki, no asking a colleague. The help is where the work is.
The intervention stays proportional. High-escalation issue categories get support. Low-escalation categories are left alone. New agents get onboarding help that experienced agents never see.
Prove
The same measurement that found the problem now tracks whether the fix worked. A rising first-contact resolution rate, confirmed against the organization’s own ticket data, closes the loop.
The HEART Score moves. The same measurement that found the problem now tracks the fix. First-contact resolution rates by issue category, agent tenure, and shift pattern show whether the intervention worked.
Results track against the target. The goal the support operations team set before the intervention tracks automatically. The quarterly review gets a before-and-after built from live data, not a separate report.
The cycle starts over. The organization launches a new product. The ITSM vendor updates the ticket workflow. The HEART Score flags a dip. Measure, act, prove runs again. The infrastructure is already there.
Proven Impact
When The Assistant and in-app guidance replace knowledge base searches and colleague queries on technical support workflows, the results show in the organization's own data: higher first-contact resolution rates, lower escalation volume to Tier 2 and Tier 3, and shorter time to independent resolution for new agents.
Up to 97%
Task completion rate across enterprise deployments
Up to 75%
Reduction in training time for new staff
Up to 48%
Fewer support requests after deployment
Up to 60%
Lower training costs vs. classroom methods
Frequently Asked Questions
