Genesys KPI Insights (BO07) for Genesys Engage on premises

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.

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Story and Business Context

Business users must be able to report, monitor and make decisions regarding their contact center/customer experience to both improve business outcomes and to move the needle. 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 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 annually or more regularly and the key KPI objectives that they will measure customer experience success against.

To manage the company's Contact Center objectives and meet end customer's business needs, there is a set of required operational key performance indicators (KPI) that are required. A good business practice is to analyse contact center performance through Service Level review to assess areas of focus in order to identify any remediation actions.

An example of a contributing factor to Service Level is Average Handle time. As such this business has set an objective for the Average Handle Time (AHT) KPI to be under 6 minutes for voice, email and chat interactions. While in some customer environments the AHT may be longer for Chat than voice this has been simplified as this is a simplified "essential use case".

The ability to generate the report ad-hoc or on a pre-scheduled basis is supported.

Historical reporting available on 15 minute increments if needed.

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 historical reporting to provide improved agent utilization, reduced churn and enhanced customer satisfaction scores. Based on service levels set, customers need to be able to both monitor and analyse interaction data plus discover anomalies through reviewing detailed interaction data. Mapping this against business outcomes, across all channels and where appropriate, customers need to be able to make informed strategic and operational decisions that minimize these anomalies in the future.

Use Case Definition

Business Flow

The flow below describes on how a team lead / supervisor would perform an analysis on low Service Level performance. The reports needed for this analysis are defined in the paragraphs below.

Business Flow Description

1. The actor (team lead, supervisor or business analyst) runs a report or a report is pushed to them.

Reference - BL1

1. He reviews the report against business level KPIs for service level, handle time, customer segmentation and media type (Interaction Volume Customer Segment Report).

Reference - BL2

1. If he finds anomalies in the service level, he analyses further reporting data to identify anomalies with contributing factors to Service Level. For example, he might find that Average Handling time was high as well.

Reference - BL3

1. He starts to drill down into further details (e.g. by filtering against queue or media type).

Reference - BL4

1. He analyses the information for anomaly details and correlations (e.g. Queue, ACW or Other KPI).

Reference - BL5

1. This information helps him to identify the root cause for the Service Level anomaly. As an example, he may identify that high ACW is driven by a certain Agent ID, Agent Group, etc.…Subsequently he identifies that the root cause is the skill levels of agents servicing a particular customer segment.

Reference - BL6

1. 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.