SL09 - Titles and Canonical Info

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This information is shared by SL09 use cases across all offerings.

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

Titles and Taxonomy

Main Title Subtitle Taxonomy Product Category Draft Published Edit

Genesys Predictive Engagement

Use machine learning powered journey analytics to monitor website activity, predict visitor outcomes, and proactively engage with prospects and customers



No draft

Not published

Canonical Information

Platform Challenge and Solution

Platform Challenge: It’s challenging to identify the right individual, the best moments, and the optimal ways to engage 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 growing your inside or eCommerce sales staff is costly.

Platform Solution: Proactively lead customers to successful journeys on your website. Apply machine learning, dynamic personas, and outcome probabilitiesto identify the right moments for proactive engagement via a web chat.

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 Productivity Representatives are empowered with real time customer journey data from your website which allows them to personalize and prioritize engagements with prospective and existing customers.
Increased Revenue Accelerate sales and conversion rates by engaging online shoppers in real time at the right time as they browse your website. Grow customer lifetime value through more proactive and personalized service and cross-selling and upselling customers on products or services of greatest value to them.
Reduced Volume of Interactions 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.

High Level Flow

High Level Flow Steps

  1. Consumer is browsing on website
  2. Online journey tracked and likelihood to buy, to request a quote, to fill in a form or downloading a document assessed...
  3. System predicts the right moment to engage the consumer
  4. At the right moment the consumer is offered a chat session
  5. Sales Rep and consumer connect to resolve issues and complete the purchase

Data Sheet Image

SL09 - genesys predictive engagement for sales - header (2).png

Canonical Sales Content


  • Chief Marketing Officer
  • Head of Marketing Operations
  • Head of Sales Operations

Qualifying Questions

  1. What insights do you have about the behaviors that determine whether a website "browser" will become a customer?
  2. How do you know when to engage with online customers to increase your sales conversions?
  3. Which channels of engagement can you use today to proactively to engage visitors to your website?

Pain Points (Business Context)

  • Inability to see, understand and engage in real time with customers and prospects across channels.
  • Low conversion rate on website.
  • Poorly utilized inside sales staff.
  • Difficulty in creating and converting qualified online sales opportunities.
  • Low optimization of website user journeys of efficient engagement through self- and assisted- service.

Desired State - How to Fix It

  • Use journey analytics to detect where customers or prospects struggle on a website. Use this information to engage a sales rep at the critical point in the sales process.
  • Identify the visitor's segmentation, monitor their web behavior, and predict their outcome score related to the online purchasing process to define rules on when intervention is necessary.
  • Proactively engage with prospects via chat or content offer if this score drops below a defined threshold while on the website.
  • Identify hot leads on the website via customer browsing behavior and predict customer likelihood to perform a specific action.

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