Genesys KPI Insights (BO07) for PureConnect

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This topic is part of the manual PureConnect Use Cases for version Public of Genesys Use Cases.
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Monitor and analyze interaction data to detect addressable service level anomalies

What's the challenge?

You need quick and easy access to data insights that will help you improve results. When data is missing or is inconsistent across channels, and when business users find it difficult to get to information they need to make good decisions, customer and agent experiences suffer.

What's the solution?

Improve the customer and employee experience by giving business users a full view into real-time agent and workgroup activity and tools to take timely action. Genesys KPI Insights monitors performance against operational goals and provides simple filtering, drill-down and reporting to address service issues in a snap.

Story and Business Context

Business users must be able to report, monitor and make decisions regarding their contact center/customer experience to ensure ongoing improvement and the best business outcomes. Knowing when changes need to be made, the impact of the change, and when not to make changes requires the ability to rapidly identify anomalies and understand the root cause behind the anomalies. Maintaining alignment between routing, reporting, and resources is essential in streamlining the business and driving optimization. Companies set their business plans regularly along with the key performance indicator (KPI) objectives that they use to measure customer experience success. To manage the company's contact center objectives and meet end customers' business needs, there is a set of required operational KPIs.

A good business practice is to analyze contact center performance through the review of service level targets and agent performance. The goal is to assess areas of focus to improve the customer service quality and identify any remediation actions.

For example, a contributing factor for service level targets is the percentage of interactions answered within a time frame (target). A contributing factor for agent performance is the average agent negative/positive score. For example, an organization might set an objective to have the service level KPI and the average agent negative/positive score be within the reasonable limit that is set by supervisors according to business needs.

Use Case Benefits

Use Case Benefits Explanation
Improved First Contact Resolution Provide visibility into call repetition pattern in reports
Increased Revenue Isolate and track anomalies to facilitate root-cause analysis to remedy issues
Reduced Administration Costs Increase visibility into training needs and skills-based routing through better reporting data. Provide readily available reports through KPI-based reporting
Reduced Interaction Transfers Reduce transfers because of additional visibility attained through KPIs that help identify areas of training and skills-based routing optimization

Summary

Improve efficiency through real-time reporting to improve agent utilization, reduce churn, and enhance customer satisfaction scores. Companies need the ability to monitor and analyze detailed interaction data to discover anomalies inservice levels and agent performance. Mapping this data against business outcomes across all channels, and where appropriate, companies can make informed strategic and operational decisions that minimize future anomalies.


Use Case Definition

Business Flow

(1) Service Level Analysis


The flow below describes how a team lead or supervisor would perform a service level analysis. The reports needed for this analysis are defined in the Business Flow Description.

Business Flow Description

  1. The actor (team lead, supervisor, or business analyst) runs a dashboard. Reference - BL1
  2. The supervisor reviews the dashboard and checks it against business level KPIs for service level, interactions answered, and customer segmentation. Reference - BL2
  3. If the supervisor finds anomalies in the service level target, they analyze further reporting data to identify anomalies with factors that contribute to service level. Reference - BL3
  4. For further analysis, the supervisor looks at the service level target against the other variables and notices that the number of interactions answered is trending lower. Reference - BL4
  5. The supervisor analyzes the information for anomaly details and correlations and finds out that there were a few agents with higher than normal average talk times.(For example, workgroup, agent statistics KPI). Reference - BL5
  6. This information helps the supervisor identify the root cause for the service level anomaly. As an example, the supervisor looks into an agents’ interactions and discovers a very long interaction with multiple holds. After talking to the agent or listening to the call, the supervisor determines that the call was complex for agents to handle and it required multiple holds to get assistance. Subsequently, the supervisor identifies that the root cause is the training of agents who service a particular customer segment. Reference - BL6
  7. The team lead or supervisor takes appropriate action.

Business Flow

(2) Agent Performance Analysis


The flow below describes on how a team lead / supervisor would perform an analysis of agent performance. The reports needed for this analysis are defined in the Business Flow Description.

Business Flow Description

  1. The actor (team lead, supervisor or business analyst) runs a dashboard. Reference - BL1
  2. The supervisor reviews the dashboard against business level KPIs for agent performance and customer segmentation. Reference - BL2
  3. If the supervisor finds anomalies in the average agent score, they analyze further reporting data to identify anomalies with factors that contribute to agent score. For example, the supervisor might be able to determine which workgroup and/or agent show high average agent negative/positive scores. Reference - BL3
  4. The supervisor looks further into the details (for example, by filtering and sorting against workgroup and agent). Reference - BL4
  5. The supervisor analyzes the information for anomaly details and correlations (for example, workgroup, agent statistics KPI). Reference - BL5
  6. This information helps the supervisor identify the root cause for the average agent score anomaly. As an example, they may identify that high average agent negative/positive score is driven by certain agent statistics, Workgroup, etc. Subsequently, the supervisor identifies that the root cause is a particular agent servicing a particular customer segment. Reference - BL6
  7. The team lead or supervisor takes appropriate action.



For more details

For additional details, contact your Genesys Sales Representative by filing out the form or for immediate assistance call us: 1-888-Genesys.


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