Genesys Predictive Engagement (CE37) for Genesys Engage cloud

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This topic is part of the manual Genesys Engage cloud Use Cases for version Current of Genesys Use Cases.
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Use machine learning powered journey analytics to observe website activity, predict visitor outcomes, and proactively engage with prospects and customers via agent-assisted chat, content offer or chatbot.

Use Case Overview

Story and Business Context

For customers seeking service or support, a company’s website is often the first point of contact, even if it is only to find a phone number to call. But companies are challenged with making sense of and learning to utilize all of the data generated by their website in a way that is both meaningful and actionable in real-time. As a result, the intentions and needs of individual consumers are overlooked, and we lose the ability to shape the journey in the moment and identify the customers who need help the most. As a result, customers either end up calling into the contact center (an expensive support channel) or get frustrated with your business because they can’t find the help they need. Genesys monitors website behavior, applies machine learning to determine audience segments and predicted outcomes in real time, and then uses that information to guide customers to a successful outcome – starting with an effective self-service offer of a chatbot to those customers who need the most help. Companies have lots of rich data within their CRM, marketing automation, contact centers and websites, and Genesys enables companies to unlock that data in real-time to engage customers proactively, thereby eliminating the need for a voice call or contact without context.

Examples of how the customer experience can be optimized by using context include:

  • A customer who is recognized to be having trouble submitting a loan application is prompted with a chatbot to automate a conversation about the loan application.
  • A customer needs to activate their new mobile phone, goes to the website, and searches for "device activation". A proactive chatbot is offered to help the customer walk through the steps.
  • A customer is planning a trip abroad and needs to notify their credit card company. They go to the company's website and based on a search related to "travel alert", a chatbot is offered to assist to prevent the need to call the contact center.
  • A customer is proactively offered self-help options to assist with a transaction, for example providing a link to a video to help with a Return Merchandise Authorization (RMA).

Understanding and leveraging knowledge of online activities and behaviors can provide context to better handle a follow-up digital or voice interaction. This engagement intelligence can also be utilized for converting service requests to sales opportunities for cross-sell or up-sell. Genesys uses artificial intelligence to track the progress of website visitors towards defined outcomes – service requests, pending transactions, application status - and allows the business to define rules to trigger intervention only at the points when it is needed most.

Use Case Benefits*

Use Case Benefits Explanation
Improved Customer Experience Offer assistance only when needed to reduce customer annoyance.
Reduced Administration Costs Improve self-service rates by providing customers with the right information at the right time or proactively offering a chatbot to automate the conversation and prevent contact with an agent.
Reduced Handle Time When the engagement requires escalation from self-service to assisted service, the agent is provided context of the journey.
*You can sort all use cases according to their stated benefits here: Sort by benefits


Genesys monitors each and every individual customer journey on your company website and applies machine learning, audience segments and outcome probabilities to identify the right moments for proactive engagement via a chatbot. If the consumer needs to interact with an agent, the agent has the customer journey information at their fingertips.

Use Case Definition

Business Flow

Main Flow

Business Flow Description

  1. The customer starts browsing the company website.
  2. Genesys determines whether the customer is new or returning to the website, and associates data from previous journeys.
  3. The combination of segment and variations in outcome score can trigger an offer to chat with a chatbot while the customer is browsing the website.
  4. If the customer accepts the invitation for chat, a registration window pops up where the customer can enter his data and the conversation with Genesys Blended AI Bots (CE31 Use Case) will start. In the registration form, customer can either manually enter his contact details (name, email) or contact details will be pre-filled if already known to Genesys.

Business and Distribution Logic

Business Logic

BL1 – Customer Identification

Returning visitors can be detected using cookies and previous site visits can be associated with them. Identity information provided during the journey (e.g. email address or phone number) will be captured once explicitly submitted from the web page and can be used to identify the customer even across devices. If a customer uses a second device to visit the website in the future and provides a piece of this information, this new visit can be associated to the previous journeys across devices. When customer identity cannot be determined, the customer will be handled as an anonymous user and all data tracked will be attached to this anonymous user. Once the customer is identified, all tracking data collected will be associated to that specific customer. All customer information collected will be done in a GDPR compliant fashion.

BL2 – Segment and Outcome Configuration

Segments are a way to categorize visitors on the website based on common behavior and attributes. Segments are configured upfront during the configuration of the system. A segment can be made up of one or both of these components:

  • Attributes: e.g. browser type, device type, location, UTM parameters, the referral website.
  • Journey Pattern: e.g. web browsing behavior, searches performed on the website, items clicked on, returning users, cart abandonment, high order value, etc.

Outcomes or goals are specific tasks you want your visitors to perform on your website. As with segments, these are configured upfront. Typical outcomes include:

  • Check order status or return status
  • Open or check status of a trouble ticket
  • Locate warranty or return policy

BL3 – Action Map Configuration

Action Maps determine the way to engage with the website visitor. Within Action Maps, you define the triggers that will result in an action to the customer. These triggers can be based off any combination of:

  • Segment
  • User Activity
  • Outcome Score (typically, a drop in Outcome Score for a specific Segment can trigger a Chatbot offer)

BL4 – Customer Invite and Registration Window

Genesys Widgets will be used for:

  • Invite messages for chatbot
  • Collection of visitor's contact details
  • Engagement over chat session

Distribution Logic

Use Case Requirements

Customer Interface Requirements

Based on Genesys Widgets 9 with standard capabilities to adapt to customer corporate identity.

Agent Desktop Requirements

  • Integration of Altocloud desktop gadgets into Workspace Web Edition v9 (in case chatbot conversation requires escalation to an agent)


Real-time Reporting

Altocloud Analytical Dashboards will be used in this use case for:

  • Action Maps Performance with engagement funnel (Qualified, Offered, Accepted)
  • Visits, Segments, and Outcomes

Historical Reporting

Web Journey reporting from Altocloud will not be accessible via Infomart / CX Insights.


General Assumptions

  • Genesys Widgets 9 must be used
  • General logic for routing of interactions will be using the logic defined within the mandatory use cases
  • Design and configuration of this use case should take it into account previous deployment of mandatory use cases

Customer Assumptions

  • Customer must deploy both Altocloud and Widgets code snippets on their website / web pages


All required, alternate, and optional use cases are listed here, as well as any exceptions.

All of the following required: At least one of the following required: Optional Exceptions


      Self-Service and Automation

        None None None

        Premise Assumptions


        Cloud Assumptions

        • Integration of Altocloud desktop gadgets into Workspace Web Edition 9

        Related Documentation

        Document Version

        • 1.0.2