Genesys Predictive Routing for Customer Service (BO06) for GenesysEngage-onpremises
What's the challenge?Your existing routing strategy doesn’t use machine learning to adapt to the changing patterns of interactions and optimize for business KPIs. You want customers to speak with someone who can fulfill their need quickly and is predicted as the best agent to optimize KPIs. Don’t let your CX score, productivity and outcomes suffer.
What's the solution?Create a differentiated experience by connecting customers with your best-fit agents. Genesys Predictive Routing provides the finest grain matching between agent and customer to improve business KPIs and appropriately route the interaction on any channel.
Story and Business Context
Business leaders want to improve their business Key Performance Indicators (KPI), leverage the innovation in Artificial Intelligence and drive business decisions with the abundance of data and context available in their business. Predictive Routing uses machine learning to support optimization of Service KPIs.
Service KPIs gather any types of 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 or Service 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, case open/closed, back office tasks opened, and field technical sent
This use case focuses on improving First Contact Resolution (FCR), captured from Genesys Info Mart or from 3rd-party surveys (for inbound voice interactions, for example) but may also be extended to other service-related KPIs, and other channel types with custom PS effort. Wherever applicable, this use case documents the impacts of choosing another channel type or another KPI, captured from Genesys data or from 3rd-party sources.
Predictive Routing also applies to Sales & Marketing KPIs. See Engage/SL06 Genesys Predictive Routing for Sales (SL06) for Genesys Engage
Traditional routing is designed to match customers to agents through skills-based or group-based logic to maintain a service level rather than improving KPI. Unlike traditional routing, Predictive Routing uses machine learning to find signals in historical data to build a model that predicts the business outcome of a customer's interaction when handled by an employee. This model improves KPIs by ranking agents before making the match with customers. This model also addresses the operational challenges that occur in understaffing and overstaffing scenarios while balancing the service level with improving KPI.
Predictive Routing has built-in A/B Testing to demonstrate the uplift of the KPI provided through use of machine learning. Predictive Routing leverages a variety of Genesys or third party data sources in order to build high quality predictors. In particular, Predictive Routing supports a native integration with Performance DNA in order to obtain a granular representation of the agent's profile.
Use Case Benefits
|Use Case Benefits||Explanation|
|Improved Employee Attrition Rate||Improve Employee Experience by routing to the Agent the work that they are more efficient or “good” at more often.|
|Improved First Contact Resolution||Improve First Contact Resolution by routing the interaction to the most proficient Agent available to handle it.|
|Improved Net Promoter Score||Target the best agents to resolve the customer call type by reducing misroutes or repeated transactions to improve customer satisfaction measures (such as Net Promoter Score and Customer Effort Score|
|Reduced Handle Time||Reduce transfers and conferences by routing interactions to the best qualified agent and reduce handle time as more efficient workers often have a lower Average Handle Time|
The contact center manager wants to increase the percentage of the interactions achieving First Contact Resolution (FCR). The Predictive Routing solution can assist in achieving this objective. FCR can be obtained either from:
- Genesys Historical Reporting data, enabling you to define FCR. For example, FCR 7 is defined as the number of customers who didn't make a subsequent contact for the same service type in 7 days.
- A 3rd-party outcome data source (such as a survey).
- Optionally customers can also attach PDNA strand data to Agent profiles to improve their match with customers and intents based on their performance in trainings.
- Uses machine learning, a subset of Artificial Intelligence, to compare feedback of the actual outcome with the predicted outcome, helping to improve future agent-to-customer matches.
- Ranks agents predicted to resolve the inquiry on first contact.
- Provides the finest-grain match of customer contact with agent to improve FCR.
- Provides an FCR uplift using continuous learning to rank the expected FCR for agents servicing customers.
- In many customer environments, anchoring the optimization on a customer experience metric like FCR, CSAT, or NPS also drives benefits in adjacent KPIs such as a reduction in handle time, transfers, or multi-agent events (MAE) with manageable impact to service levels.
The result is that repeat contacts are reduced and FCR improves.
Use Case Definition
The following flow shows how a model is created. The main actor of this flow is typically a Business Analyst / Data Analyst in charge of the model creation. The Analyst is a trained professional from Genesys, a partner, or a customer organization.
Business Flow Description
- The team Lead / Supervisor and the Analyst agree on the outcome metric to be used. This use case uses FCR as the reference metric.
- The Analyst gathers customer profile, interaction profile, agent profile, and FCR data from Info Mart, and optionally from 3rd-party data sources.
- The Analyst analyzes the data to determine correlating factors/predictors and verify if the data is suitable for a predictive model.
- The Analyst creates a predictive model based on the available Info Mart data set.
- The Analyst reviews the quality of the predictive model and potential for uplift. If the quality is satisfactory, the model can be provisioned.
The modeling process described above may be extended to incorporate the following changes:
- Integration of additional 3rd-party data sources for customer profile (such as CRM), agent profile (such as WFO), content analysis data (speech or text), or outcome data (such as NPS surveys or CRM or case management)
- Selection of other KPI(s) to optimize based on Info Mart data (such as AHT) or 3rd-party data (such as NPS)
The selection, analysis, and integration of this data into the predictive model requires a project-based implementation that is supported by the Predictive Routing product, but not described in this use case. Contact Genesys Professional Services for details.
Predictive Routing First Contact Resolution
This business flow shows the use case from the perspective of the customer and agent.
Business Flow Description
- The customer contacts the company using one of the available inbound channels (such as voice, e-mail, chat, mobile, work item, or Apple Business Chat).* This inbound interaction may be the result of a proactive rule on a web or mobile application.
- One of the Inbound use cases for the corresponding media type handles the interaction. The context data is captured depending on the interaction and engagement type.
- Genesys queues the interaction until at least one agent with the required skill(s) is available.
- Predictive Routing ranks the agents against the FCR probability for that specific interaction and returns a ranked list with values.
- Genesys checks if the rank for at least one agent is above the threshold.
- If no agents are available within the configured timeout, the routing expands the potential target pool of agents, such as by reducing the required skill level.
- If yes, Genesys distributes to the best available agent based on the predictive model (the agent with the highest rank) and the routing rules.
- The agent disconnects the interaction.
- The outcome is mapped to an InfoMart attribute (for example, a disposition code or custom key value pair).
- Optional: The customer is offered a survey. The answer to the survey is stored in a third-party system.
- 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.