Genesys Predictive Engagement (CE37) for Genesys Engage cloud

From Genesys Documentation
Jump to: navigation, search
This topic is part of the manual Genesys Engage Cloud Use Cases for version Current of Genesys Use Cases.
Important
Please be advised that this use case has been merged with Genesys Predictive Engagement (SL09). SL09 has now been decommissioned and all relevant content is displayed in this use case.
Use AI 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.

What's the challenge?

It’s challenging to identify the right individual, the best moments, and the optimal ways to offer assistance online. Companies want to shape their customers’ journeys and drive them towards desirable outcomes, but it’s hard to utilize all of the available data in a way that is meaningful and actionable. In addition, consumers expect fast answers, but it's expensive to always engage an agent.

What's the solution?

Proactively lead customers to successful journeys on your website. Apply machine learning, dynamic personas, and outcome probabilities to identify the right moments for proactive engagement via a web chat or help content screen-pop.

Use Case Overview

Story and Business Context

One of the biggest challenges for the modern business is learning to work with the data available in a way that is both meaningful and easy to act on. The data generated by a website often goes unexplored, and as a result, the intentions and reactions of individual customers and prospects can be overlooked. Focus is often placed on the broad strokes–key metrics such as the number of conversions per month–and the ability to identify the potential customers who need engagement is lost. As a result, customers who may be on the verge of signing up for a trial, completing a checkout, searching for information regarding service or support, or any other desirable outcome, fall through the cracks. The high volume of website traffic makes it a challenge to identify the right individuals, best moments, and optimal ways to engage in real time. Expectations for time-to-respond are increasing but extending your staff is costly.

Genesys Predictive Engagement uses machine learning to observe the progress of website visitors toward defined business outcomes –such as purchase completion or requesting a quote. The technology enables the business to use real-time observations and predictions rather than static rules, to trigger intervention only at the points when it is needed most.

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 use all the data generated by their website in a way that is both meaningful and easy to act on in real time. 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 Predictive Engagement prioritizes engagement with high value visitors and proactively offers to chat to better utilize your staff and reduce your costs.

Examples of how the customer experience can be optimized by using data, context, and website behavior for a predictive engagement:

  • Use of machine learning to detect the progress of website visitors toward defined outcomes–purchase completion, requesting a quote–and enable the business to trigger intervention only at the points when it is needed most.
  • A customer who is recognized to be having trouble submitting a loan application is prompted with a proactive web chat enabling an agent to help the customer walk through the steps.
  • 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).

Use Case Benefits*

The following benefits are based on benchmark information captured from Genesys customers and may vary based on industry, lines of business or Genesys product line:

Use Case Benefits Explanation
Improved Conversion Rates Follow individual customer journeys in real time on your website. Identify the moment of struggle or moment of opportunity and launch a chat or voice interaction with a sales agent at the right time to increase lead volume, improve lead qualification and reduce customer churn.
Improved Customer Experience Offer assistance only when needed to reduce customer annoyance.
Improved Employee Productivity Representatives are empowered with real-time customer journey data which allows them to personalize and prioritize engagements with prospective and existing customers.
Increased Revenue Retain customers by increasing customer satisfaction with faster and more personalized service. Improve the ability to up-sell and cross-sell existing customers with data based on their current interests, online journeys, and prior purchasing behavior.
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

Summary

Understanding and using knowledge of online activities and behaviors can provide context to better handle a follow-up digital or voice interaction to help customers who are shopping, buying, using the company's products across the full customer life cycle. This engagement intelligence can also be used for converting service requests to sales opportunities for cross-sell or up-sell. Genesys uses artificial intelligence to observe and analyze the progress of website visitors toward defined outcomes – service requests, pending transactions, application status. The technology allows the business to engage with customers using dynamic observations and predictions rather than simple static rules- creating happier customers, smarter employees, and better outcomes.

Companies have vast amounts of 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, eliminating the need for a voice call or contact without context. Genesys Predictive Engagement observes individual customer journeys on your company website and applies machine learning, dynamic (or audience) segmentation, and real-time outcome scoring to identify the right moments for proactive engagement with the right customer via chat, chatbot, or content offer.

Predictive Engagement's real-time engagement sophistication increases customer satisfaction, improves conversion rate, and optimizes the use of agent resources for the highest value customers leading to improvement of key performance indicators such as call deflection, average order value (AOV), first contact resolution, and conversion rates.


Use Case Definition

Business Flow

(1) 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 an agent or with a chatbot while the customer is browsing the website.
  4. An algorithm determines the predicted availability of agents to handle the interactions.
  5. If the customer accepts the invitation for chat, a registration window pops up where the customer can enter their data and the conversation with Genesys Blended AI Bots (CE31 Use Case) starts. In the registration form, customer can either manually enter their contact details (name, email) or contact details are pre-filled if already known to Genesys.
  6. In Genesys Routing logic, a decision can be made based using context (for example, customer segment, customer lifetime value) and current agent availability


Business Flow

(2) Routing


This diagram details the routing that takes place before and during the chat.

Business Flow Description

  1. Genesys routes the interaction to an agent based on the skills, media, language, and other ACD routing choices.
  2. An agent and customer are in conversation. The agent has access to full visitor context such as segment, journey information, and outcome score.
  3. After the conversation ends, the agent sets a disposition code within their desktop to record the outcome of the conversation.

Business and Distribution Logic

Business Logic

BL1 – Customer Identification

The system can use cookies to detect returning visitors and associate them with previous site visits. Identity information provided during the journey (such as email address or phone number) is captured after it is explicitly submitted from the webpage and can identify the visitor even across devices. After the customer is identified, all tracking data collected is associated to that specific customer. All customer information collected is 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 system provisioning. A segment can be made up of one or both of these components:

  • Attributes, such as browser type, device type, location, marketing campaign they are associated with, UTM parameters, and the referral website.
  • Journey pattern, such as web browsing behavior, searches performed on the website, items clicked, returning users, cart abandoner, and high-order value.

Outcomes or goals are specific tasks you want your visitors to perform on your website. As with segments, they 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
  • Application submission
  • Online purchase confirmation
  • Submit payment
  • Online quote
  • Book a demo or appointment

Genesys uses predictive analytics to evaluate in real time the probability for a specific outcome to be achieved, based on segment and visitor behavior on the website (the outcome score).

BL3 – Action Map Configuration

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

  • Segment
  • User activity
  • Outcome score (typically, a drop in outcome score for a specific segment can trigger a web chat)

BL4 – Customer Invite and Registration Window

Genesys Widgets are used for:

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

Distribution Logic

The distribution of the interaction is determined by the target expression and virtual queue configured in the Genesys Predictive Engagement rules.

User Interface & Reporting


Agent UI

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

Reporting

Real-time Reporting

An admin can see the Live Now view of current visitors and live tracking information on the site. The views allow admins to make real-time operational decisions. For example, when a marketing campaign has gone live and drill into individual customer journeys.

Historical Reporting

The visitor activity report provides trend analysis and a drill-down by device type.

Reporting on segments matched and outcomes achieved.

Action map performance of action types; web chat, content offers, and Architect flow. It allows a funnel drill-down performance of the key stages which can identify resourcing requirements, queue issues,

  • Qualification
  • Offer
  • Acceptance
  • Engagement

A CX Insights Dashboard that shows the following is available:

  • Combined total number of chats (native, Genesys Predictive Engagement and bots) for a workgroup for an interval.
  • Total number of native chats for a workgroup for an interval.
  • Total number of Genesys Predictive Engagement chats for a workgroup for an interval.
  • Total number of bot chats for a workgroup for an interval.
  • Combined total number of chats (native, Genesys Predictive Engagement and bots) for an agent for an interval.
  • Total number of native chats for an agent for an interval.
  • Total number of Genesys Predictive Engagement chats for an agent for an interval.
  • Total number of bot chats an agent for an interval.

Customer-facing Considerations

Interdependencies

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
None

    Digital

      Self-Service and Automation

        None None


        General Assumptions

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

        Customer Responsibilities

        • Customer must deploy both Genesys Predictive Engagement and Widgets code snippets on their website / web pages
        • Integration of Genesys Predictive Engagement desktop gadgets into Workspace Web Edition 9


        Related Documentation


        Document Version

        • Version 1.1.2 last updated June 11, 2021