Genesys Predictive Engagement (CE37) for Genesys Engage on premises
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.
- 1 What's the challenge?
- 2 What's the solution?
- 3 Use Case Overview
- 4 Use Case Definition
- 5 User Interface & Reporting
- 6 Customer-facing Considerations
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 growing 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 actionable 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 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 in need of reducing 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.|
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. Predictive Engagement leads 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 Description
- The customer starts browsing the company website.
- Genesys determines whether the customer is new or returning to the website, and associates data from previous journeys.
- 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.
- An algorithm determines the predicted availability of agents to handle the interactions.
- 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.
- In Genesys Routing logic, a decision can be made based using context (for example, customer segment, customer lifetime value) and current agent availability
This diagram details the routing that takes place before and during the chat.
Business Flow Description
- Genesys routes the interaction to an agent based on the skills, media, language, and other ACD routing choices.
- An agent and customer are in conversation. The agent has access to full visitor context such as segment, journey information, and outcome score.
- After the conversation ends, the agent sets a disposition code within their desktop to record the outcome of the conversation.
Business and Distribution Logic
BL1 – Customer Identification
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, 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
- 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:
- User activity
- Outcome score (typically, a drop in outcome score for a specific segment can trigger a webchat)
BL4 – Customer Invite and Registration Window
Genesys Widgets will be used for:
- Invite messages for webchat
- Collection of visitor's contact details
- Engagement over chat session
The distribution of the interaction is determined by the target expression and virtual queue configured in the Genesys Predictive Engagement rules.
User Interface & Reporting
- Integration of Genesys Predictive Engagement desktop gadgets into Workspace Desktop Edition 8.5 (in case chatbot conversation requires escalation to an agent)
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.
The visitor activity report provides trend analysis and a drill-down by device type.
Reporting on segments matched and outcomes achieved (available through Predictive Engagement).
Action map performance of action types; webchat, content offers and architect flow. It allows a funnel drill-down performance of the key stages which can identify resourcing requirements, queue issues,
The Bot Dashboard provides a dashboard-style summary that you can use to evaluate the impact of Chat Bot, including visualizations of session and message volumes, and breaks down sessions based on whether bots, agents, or both, were involved. The dashboard report organizes data on the following tabs:
- The Bot Sessions tab provides an overall view of bot sessions, including information about:
- Session durations
- How many sessions were initiated, started, interrupted, or failed
- Information about the number of messages sent and received by bots.
- The Media Sessions tab focuses on media sessions, contrasting the number of media sessions with the number (and percentage) of sessions with bots, and with the number of sessions (and percentage) with bots only.
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
- 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 should account for previous deployment of mandatory use cases
- Customer must deploy both Genesys Predictive Engagement and Widgets code snippets on their website / web pages
- Version v. 1.2.1 last updated February 16, 2021