Helping To Reduce MTTR - Smart Ticketing
Advanced machine learning (ML) assigns tickets to the right team the first time.
Eliminate Ticket Ping-Pong Without Extra Work
Advanced ML delivers tickets to the right person the first time around.
Classifying, assigning, and prioritizing tickets accurately is one of the most difficult challenges help desk agents face. Employees often don't provide enough information — or relevant information — when they submit tickets, so agents are required to contact the employee to collect additional information and then spend time manually fixing them. By the time tickets are assigned to the right support teams, costly resolution time is spent that results in escalations and frustrated employees, as well as negatively affecting the perception of help desk services.
Barista Smart Ticketing solves these issues by intelligently categorizing tickets using a dynamic predictive model, automatically routing tickets to the right service desk. It even takes into account global enterprises and different business units.
Gain Efficiency. Reduce MTTR.
Smart Ticketing is a unique capability of Espressive Barista that eliminates the need for help desk agents to classify, assign, and prioritize tickets. By using advanced ML to build a predictive model from customer historical tickets, newly created tickets, and agent actions, your team can deploy fast without the requirement to build and maintain a large index of static rules. Tickets are correctly assigned to the right team from the start, drastically reducing mean time to resolution (MTTR) and providing a better employee experience.
HISTORICAL DATA
Historical data is exported from the ticketing tool
MACHINE LEARNING
Machine learning is used to build a predictive model
PREDICT
Employee phrases are used to predict ticket data
MODEL CREATED
A new and better machine learning model is created
Unique Machine Learning Model Corrects for Data Biases
Barista's predictive model learns from agent corrections and newly created tickets instead of relying solely on historical data, reducing agent workload by minimizing inaccuracies and inconsistencies in new tickets.
Agents can still correct the predicted data in the ticketing tool, or flag tickets for immediate follow-up.
Admins can review corrections or flagged tickets and approve them, and a better ML model is automatically created from the corrections.
Behavior can be further fine-tuned (i.e., defining override behaviors) for immediate control over the AI prediction.
