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Genesys Predictive Routing for Sales (SL06) for Genesys Engage on premises

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This topic is part of the manual Genesys Engage on-premisess Use Cases for version Public of Genesys Use Cases.
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Place revenue generation at the center of your routing decisions by using AI to match each customer opportunity with the best agent

What's the challenge?

Your existing routing strategy doesn’t use machine learning to adapt to the changing patterns of interactions and optimize for sales conversions. You want customers to speak with a rep who can fulfill their need quickly and is predicted best to increase revenue, based on customer journey. Don't let your CX scores suffer!

What's the solution?

Create a differentiated experience by connecting customers with your best-fit sales reps. Genesys Predictive Routing provides the finest grain matching between sales reps and customers and appropriately routes the interaction on the customer’s preferred 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 Sales KPIs.

A Sales KPI is a metric measuring the sales outcome of an interaction, in contrast to Service KPIs, which measure a Customer Experience or efficiency outcome. Sales KPIs can be a sales conversion rate, a sales revenue amount, a retention rate, a collection promise to pay. This use case focuses on improving revenue for inbound voice calls, but can also be extended to other sales-related KPIs, and other channel types with custom PS effort. The impacts of choosing another KPI or another channel type are documented in this use case wherever applicable.

Predictive Routing also applies to optimize Services KPIs. See Genesys Predictive Routing for Customer Service (BO06) for Genesys Engage on-premises.

Traditional routing is designed to match customers to agents through skills-based or group-based logic rather than improving KPI. Unlike traditional routing, Predictive Routing uses machine learning to find signals in historical data to build a predictive model. 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 Satisfaction Increased sales success leads directly to improved satisfaction for sales reps.
Improved Net Promoter Score Routing prospects to the sales reps best able to handle their sales request improves the customer experience.
Increased Revenue Machine learning-based matching of sales reps to prospects based on sales value directly increases revenue.


Consider a retail bank that wants to upsell credit cards to its existing customers. Depending on the customer attributes (such as age or income), the bank wants to maximize both the conversion rate and the credit limit that the customer will accept, resulting in a higher overall revenue. This use case is based on a measure of sales revenue driven from a Sales reporting application (such as CRM).

In this use case, we assume that a customer interaction is associated to a credit card offer, either from the explicit customer intention (from IVR, web, or mobile) or from a business rule (such as next best action, which is out of scope of this use case).

The Contact Center Manager or Business owner wants to increase overall revenue generated per agent. The Predictive Routing solution can assist in achieving this objective.

Predictive Routing:

  • 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 maximize the expected revenue per interaction.
  • Optionally customers can attach PDNA strand data to Agent profiles to improve their match with customers and intents based on their performance in trainings.
  • Provides the finest grain match of customer contact with agent to help maximize revenue per agent.Provides an uplift on revenue using continuous learning to rank the expected revenue for agents servicing customers.

The direct result is that the average revenue per interaction increases. Predictive Routing usually also influences adjacent service KPIs like first contact sale, CSAT or NPS, handle time, and transfers. It is a common best practice to monitor all Sales KPIs and adjacent Service Levels to evaluate all impacts (out of scope of this use case).

Use Case Definition

Business Flow

Model Creation

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

  1. The team Lead / Supervisor and the Analyst agree on the outcome metric to be used. This use case uses revenue optimization as the reference metric.
  2. The Analyst gathers customer profile, interaction profile, and agent profile data from Info Mart, and revenue data from sales records (such as CRM) or Info Mart, if captured in the data model.
  3. The Analyst analyses the data to determine correlating factors/predictors and verify if the data is suitable for a predictive model.
  4. The Analyst creates a predictive model based on the available data set.
  5. 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 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 more details.

Business Flow

Predictive Routing for Sales

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 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.
  2. 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.
  3. Genesys queues the interaction until at least one agent with the required skill(s) is available.
  4. Predictive Routing ranks the agents against expected revenue for that specific interaction and returns a ranked list with values.
  5. Genesys checks if the rank for at least one agent is above the threshold.
  6. 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.
  7. If yes, Genesys distributes to the best available agent based on the predictive model (the agent with the highest rank) and the routing rules.
  8. The agent disconnects the interaction.
  9. The outcome is mapped to an InfoMart 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 CRM sales transaction completion and value, 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.