Genesys Predictive Routing for Customer Service (BO06) for Genesys Engage on premises

Place CX and agent efficiency at the center of your routing decisions using AI to match each customer interaction with the best agent

What's the challenge?

Today’s contact centers generate large volumes of data and have outgrown legacy skill and queue-based routing for matching customers and agents. It is almost impossible to optimize for metrics such as First Call Resolution (FCR) or Average Handling Time (AHT) because thousands of if-then rules have to be built and managed.

What's the solution?

Genesys Predictive Routing works in real-time, using AI to analyze 100s of data points to discover patterns to match customers to the best agents. With Genesys Predictive Routing, contact centers can improve customer experiences, grow revenue, improve efficiency, and optimize for important KPIs.

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Story and Business Context

Companies want to improve their business Key Performance Indicators (KPI), capitalize on innovation in Artificial Intelligence and drive business decisions with the abundance of data and context available. Predictive Routing uses machine learning to support optimization of Customer Service KPIs.

Customer Service KPIs are metrics measuring a customer experience or efficiency outcome of an interaction, as opposed to Sales KPIs that measure the sales outcome of an interaction. Service KPIs can be of two types:

• Customer experience outcome such as Net Promotor Score (NPS), Customer Satisfaction (CSAT), First Contact Resolution (FCR), and Customer Effort Score (CES)
• Operational efficiency metrics such as Handle Time (AHT), transfers, hold count, hold time, cases open/closed, and back-office tasks opened

This use case illustrates an improvement in First Contact Resolution (FCR), captured from Genesys Info Mart or from 3rd-party surveys (for inbound voice interactions, for example). The use case also illustrates service-related KPIs, where the data for the KPIs is available in Info Mart or another available data source.

Predictive Routing also applies to Sales & Marketing KPIs. See Genesys Predictive Routing for Sales (SL06) for Genesys Engage on-premises

Traditional routing matches customers to agents through skills-based or queue-based logic. The goal is to maintain a service level, rather than to improve a KPI. Predictive Routing differs from traditional routing in that it uses machine learning to detect patterns in historical data from Genesys Info Mart and other third-party data sources. The predictive algorithm then uses these sources to build a model that predicts the business outcome of a customer's interaction when handled by an employee.

The predictive model works to improve KPIs by ranking agents according to their predicted impact on the business outcome. It then assigns the interaction to the highest ranked available agent. A/B testing measures the real-world impact of Predictive Routing on the target KPI by comparing Predictive Routing performance against the existing routing strategy.

Use Case Benefits

Use Case Benefits	Explanation
Improved Customer Experience	Reduce misroutes or repeated transactions to improve customer satisfaction by targeting the best agents to resolve different types of calls.
Improved First Contact Resolution	Improve First Contact Resolution by routing the interaction to the most proficient Agent available to handle it.
Reduced Employee Attrition	Improve Employee Experience by routing work to the Agent that they are more efficient or “good” at more often.
Reduced Handle Time	Reduce transfers and conferences by routing interactions to the best qualified agent and reduce average handle time by having more efficient workers take each interaction.

Summary

Organizations seeking to improve the level of customer service offered to their customers realize significant benefits from Predictive Routing. Machine learning models configured to optimize metrics such as First Call Resolution are at the core of the solution.

A customer calls the contact center, and Predictive Routing uses the data captured about the customer, their journey, and the current interaction to rank all available agents according to their predicted probability of resolving the call. Configuration options manage and balance the Service Level (speed to answer) with connecting to the most suitable agent. The result is a reduction in repeat contacts and improved FCR.

The outcome data feeds back into the machine learning model to inform future predictions. Impacts on KPIs and the performance of the machine learning models are available via real-time reports.

Use Case Definition

Business Flow

This business flow shows the use case from the perspective of the customer and agent.

Business Flow Description

1. The customer contacts the company using the inbound voice channel. This inbound interaction can be the result of a proactive rule on a web or mobile application.
2. One of the Inbound use cases for the corresponding media type handles the interaction and captures interaction context data. The exact data captured depends on the interaction and engagement type.
3. Based on the interaction context, Genesys selects an initial group of agents with the required skill(s) as possible routing targets to handle the interaction.
4. Predictive Routing calculates the scores of the agents in the target group using a machine learning model that takes into account the agents' historic performance on similar interactions.
5. When there are multiple agents available, Genesys attempts to route the interaction to the available agent with a highest score.
6. If there is an interaction surplus and an agent becomes ready, Genesys selects an interaction from the queue taking into account the priority of each waiting interaction, the score the agent has for each interaction, and the time the interactions were queued.
7. If no agents are available within the configured timeout, the routing strategy expands the potential target pool of agents by reducing the skill requirements and then repeats the target agent selection using Predictive Routing.
8. After dealing with the customer call, the agent disconnects the interaction.
9. The outcome is mapped to Genesys Info Mart attribute (for example, a disposition code or custom key-value pair).
10. Optional: The customer is offered a survey. The answer to the survey is stored in a third-party system.
11. Optional: Outcome data, such as case management closure, is produced and stored by a third-party application.

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.