CE13 - Titles and Canonical Info

Administration Dashboard

Go back to admin dashboard to create and manage platform-specific use cases in the system:

Administration Dashboard

Titles and Taxonomy

Main Title	Subtitle	Taxonomy	Product Category	Draft	Published	Edit
Genesys Predictive Engagement	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.	Customer Engagement	Digital	No draft	GenesysEngage-onpremises

Canonical Information

Platform Challenge and Solution

Platform 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.

Platform 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.

Platform Benefits

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

Canonical Benefit	Explanation
Improved Employee Utilization	An omnichannel outbound engine improves the number of productive contacts per agent (occupancy) and reduces cost expenditure from under-utilized outbound resources.
Increased Revenue	Close rates, cross-sells and up-sell rates will improve by generating outbound contact through voice, SMS or email and empowering agents with single searchable desktop application that shows customer context across all channels
Reduced Volume of Interactions	Contacting customers proactively via SMS, Email or voice message will reduce the volume of inbound interactions handled by agents

High Level Flow

High Level Flow Steps

Campaign strategy defined for new automated outbound campaign...
Including channels, pacing, escalation, time between contacts, and max contact attempts
Contact list provided by organization...
Channel preferences of individual consumers and Do-Not-Contact suppression lists are applied.
Company sends first communication to customer.
Calls-to-action dependent on the channel used
If no response, customer record included in next pass through contact list – Repeat multiple times

Data Sheet Image

Canonical Sales Content

Personas

Chief Digital Officer
Head of Contact Center(s)
Head of Customer Experience
Head of Customer Service

Qualifying Questions

What insights do you have about the behaviors that determine whether someone on your website is lost or unable to complete a service task?
How do you know when to engage with online customers to provide support and assistance?
Which channels of engagement can you use today to proactively to engage customers on your website?

Pain Points (Business Context)

Low self-service rate on website leading to high number of contacts into contact center
Inability to see, understand and engage with customers during service journeys across channels in real time
Website user journeys not optimized for efficient engagement through self-service

Desired State - How to Fix It

Use journey analytics to detect where customers struggle on a website and use this information to improve their service journeys
Identify the user’s persona, monitor their web behavior, and predict their outcome score related to the customer service process
Use machine learning to profile behavior, predict outcomes and allow organizations to define rules on when to intervene
Proactively engage with customers via a chatbot if this score drops below a defined threshold while on the website
Deflect from live agent contact by proactively displaying additional information or offering the most cost-effective channel