UseCases/Current/GenesysEngage-cloud/SL09 - Revision history

Shlandy93: Redirected page to UseCases/Current/GenesysEngage-cloud/CE37

2020-09-29T12:45:19Z

Redirected page to UseCases/Current/GenesysEngage-cloud/CE37

WikiSysop: WikiSysop moved page UseCases/Current/PureEngageCloud/SL09 to UseCases/Current/GenesysEngage-cloud/SL09: Text replacement - "PureEngageCloud" to "GenesysEngage-cloud"

2020-07-15T11:24:32Z

WikiSysop moved page UseCases/Current/PureEngageCloud/SL09 to UseCases/Current/GenesysEngage-cloud/SL09: Text replacement - "PureEngageCloud" to "GenesysEngage-cloud"

WikiSysop: Text replacement - "PureEngageCloud" to "GenesysEngage-cloud"

2020-07-15T11:24:31Z

Text replacement - "PureEngageCloud" to "GenesysEngage-cloud"

Shlandy93 at 12:08, April 17, 2020

2020-04-17T12:08:43Z

Shlandy93 at 12:42, March 31, 2020

2020-03-31T12:42:12Z

Shlandy93 at 16:48, March 10, 2020

2020-03-10T16:48:00Z

Bgrenon: Text replacement - "\|SMART_HybridAssumptions={{SMART HybridAssumptions \|Hybrid_Assumption=(.*) }}" to "|DocVersion=$1"

2020-01-20T21:07:19Z

Text replacement - "\|SMART_HybridAssumptions={{SMART HybridAssumptions \|Hybrid_Assumption=(.*) }}" to "|DocVersion=$1"

Shlandy93 at 13:19, August 14, 2019

2019-08-14T13:19:48Z

Shlandy93: Created page with "{{SMART UseCase |ID=SL09 |Title=Genesys Predictive Engagement |Offering=PureEngageCloud |SMART_Benefits={{SMART Benefits |UCBenefitID=Increased Revenue |UCBenefit=Increase con..."

2019-06-20T08:58:06Z

Created page with "{{SMART UseCase |ID=SL09 |Title=Genesys Predictive Engagement |Offering=PureEngageCloud |SMART_Benefits={{SMART Benefits |UCBenefitID=Increased Revenue |UCBenefit=Increase con..."

New page

{{SMART UseCase
|ID=SL09
|Title=Genesys Predictive Engagement
|Offering=PureEngageCloud
|SMART_Benefits={{SMART Benefits
|UCBenefitID=Increased Revenue
|UCBenefit=Increase conversions and revenue closure by engaging the right shoppers at the right time and Accelerate sales cycle and lead conversion rates (MQL to SQL to conversion)
}}{{SMART Benefits
|UCBenefitID=Improved Employee Utilization
|UCBenefit=Engage sales reps only when they can have the most impact on the sale
}}{{SMART Benefits
|UCBenefitID=Improved Customer Experience
|UCBenefit=Give sales reps visibility into the real-time customer journey and , allowing the right agent to prioritize their time with the right customer at the right time and no longer disrupt the website visitor experience with unnecessary offers of chat or interaction
}}{{SMART Benefits
|UCBenefitID=Reduced Administration Costs
|UCBenefit=Reduce customer acquisition cost (CAC)
}}
|UCIntro=This use case is the subject of an Early Adopter Program (EAP). Please contact Lindsay Frazier, Product Management for more information.

Customer Service applications of this use case is addressed by Genesys Predictive Chatbots (CE37).
|UCOverview=One of the biggest challenges for the modern business is learning to utilize all of the data available to them in a way that is both meaningful and actionable. The data generated by a website is often left 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 page views this month–and we lose the ability to identify the potential customers who need engagement most. As a result, prospects who may be on the verge of signing up for a trial, completing a checkout, or any other desirable outcome, fall through the cracks.

The high volume of website traffic makes it challenging 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 inside sales staff is costly. Marketing dollars are spent on a wide range of demand generation activities but it is difficult to connect the dots from lead to contact to opportunity to closed.
Genesys Predictive Engagement uses machine learning to track the progress of website visitors towards defined outcomes–purchase completion, requesting a quote–and enables the business to define rules to trigger intervention only at the points when it is needed most.

With Genesys Predictive Engagement, you can predict and prioritize high-value leads for your sales team to engage and proactively offer chat to better utilize your staff and reduce your costs. Genesys Predictive Engagement uses machine learning to track the progress of website visitors towards defined outcomes–purchase completion, requesting a quote–and enables the business to define rules to trigger intervention only at the points when it is needed most.
|UCSummary=Genesys Predictive Engagement monitors individual customer journeys on your company website and applies machine learning, dynamic segmentation, and real-time outcome scoring to identify the right moments for proactive engagement with the right customer via chat or content offer. When the visitor interacts, the sales rep has the customer journey information at their fingertips.
|Description=* Reporting, SFDC Third-Party Integration and Content Offer capabilities with Genesys Predictive Engagement (Altocloud) are planned in 2019, for further details please contact your respective Product Manager.
* Customer Service applications of this use case are covered by a use case, CE37.<br />
|PainPoints=* Inability to see, understand and engage in real time with customers and prospects across channels
* Low conversion rate on website
* Inside sales poorly utilized
* Hard to create and convert qualified online sales opportunities
* Website user journeys not optimized for efficient engagement through self and assisted service
|DesiredState=* Use journey analytics to detect where customers or prospects struggle on a website and use this information to improve their purchasing journeys
* Identify the visitor's segmentation, monitor their web behavior, and predict their outcome score related to the online purchasing process
* Use machine learning to profile behavior, predict outcomes and allow organizations to define rules on when to intervene
* Proactively engage with prospects via chat or content offer if this score drops below a defined threshold while on the website
* Engage a sales rep at the critical point in the sales process to increase likelihood of closing the sale, while improving customer experienceIdentify hot leads on the website via their browsing behavior and heir likelihood to perform a specific action
* Provide sales rep with context from the customer journey on the website
* Deflect from live agent contact by proactively displaying additional information or offering the most cost-effective channel for the specific customer segment
|BuyerPersonas=Chief Digital Officer, Head of Sales, Head of Ecommerce
|MaturityLevel=Differentiated
|PremiseAssumptions=Genesys Predictive Engagement desktop gadgets are integrated into Workspace Desktop Edition.
|PremiseAssumptionsAdditional_Sales=SFDC Third-Party Integration and Content Offer capabilities with Genesys Predictive Engagement are planned in 2019, for further details please contact your respective Product Manager.

Historical reporting is planned in 2019, for further details please contact your respective Product Manager.
|BusinessImageFlow={{SMART BusinessImageFlow
|BusinessFlow='''Main Flow'''

The following diagram shows the main flow of the use case, from the point of view of the system.
|BusinessImage=https://www.lucidchart.com/documents/edit/b31f9b5f-7084-4fc4-8129-0bc938776ddf/0
|BusinessFlowDescription=# A visitor starts browsing the company website.
# Genesys determines whether visitor is new or returning to website, and associates data from previous journeys.
# Genesys continues to monitor website behavior and update segment attribution and outcome score as appropriate.<br />
# The combination of segment and variations in outcome score as predicted by the machine learning engine can eventually trigger a chat to the visitor.
# An algorithm determines the availability of sales reps to handle the interactions.
# If sales reps are identified as available to handle chat, a proactive invitation to chat is presented to the visitor
# If the visitor accepts the invite, a popup registration window enables the visitor to enter their data.
}}{{SMART BusinessImageFlow
|BusinessFlow='''Routing'''

This diagram details the routing that takes place before and during the chat.
|BusinessImage=https://www.lucidchart.com/documents/edit/38673dbe-49ce-47b8-aba2-4a876d0d4fd7/0
|BusinessFlowDescription=# Genesys routes the interaction to a sales rep based on the target suggested by Genesys Predictive Engagement (target expression and virtual queue).
# Sales rep and prospect are in conversation. The sales rep has access to full visitor context such as segment, journey information, and outcome score.
# After the conversation ends, the sales rep agent sets a disposition code within their desktop to record the outcome of the conversation.
}}
|BusinessLogic=====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 when explicitly submitted from the web page and can identify the visitor even across devices. If a visitor uses a second device to visit the website the next day and provides a piece of this information, their visit can be associated to the previous journeys across devices. When visitor identity cannot be determined, the customer is handled as an anonymous user and all tracked data attached to them. Once the visitor is identified, all tracking data collected and to be collected is associated to that specific visitor.
====BL2 – Segment and Outcome Configuration====
Segments are a way to categorize visitors on the website into segments, based on common behavior and attributes. Segments are configured upfront during system provisioning. A segment is made up of two 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 segments, these are configured upfront. Typical outcomes include:
* Proceed to checkout with a shopping cart
* Submit payment
* Download a whitepaper
* 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 will result in an action to the customer. These triggers include:
* Segment
* User activity
* Outcome score - Typically, a drop in outcome score for a specific segment can trigger an action.

The following actions are part of this use case:
* Invite to Chat

====BL4 – Sales Rep Availability====
Genesys Predictive Engagement can optionally evaluate sales rep availability, based on contact center statistics like estimated wait time and engage only when sales reps are ready to serve the interaction. If no sales rep is available for the requested queue, then no invite is sent.

====BL5 – Customer Invite and Registration====
Genesys Widgets is used for:
* Invite messages for chat
* Collection of visitor's contact details
* Engagement over chat session booking
|DistributionLogic====Distribution Logic===
The distribution of the interaction is determined by the target expression and virtual queue configured in the Genesys Predictive Engagement rules.
|CustomerInterfaceRequirements=* Based on Genesys Widgets 9 with standard capabilities to adapt to customer corporate identity
|AgentDeskRequirements=N/A
|RealTimeReporting=Interaction-related reporting is based on standard Pulse templates. Capabilities are Routing (CE18).
|HistoricalReporting=Interaction-related reporting is based on Genesys Interactive Insights (GI2). Capabilities are similar to Routing (CE18).

There are two analytical dashboards are provided:
# Visitor Activity on the website: Provides the count of visits filtered by time range, segments matched, and outcomes achieved.
# Action Map Performance: Provides the count of actions that were offered, accepted, and rejected filtered by time range.
|SMART_HybridAssumptions={{SMART HybridAssumptions
|Hybrid_Assumption=v 1.0.0
}}
|GeneralAssumptions=Genesys Widgets must be used. Customer must deploy both and Widgets code snippets on their website / web pages.

General logic for routing of interactions uses part of these cases. If CE18 is already deployed and customized, SL09 design and configuration must take it into account.
|RequiresAll=CE18
|SMART_PremiseAssumptions={{SMART PremiseAssumptions
|Premise_Assumption=N/A
}}
}}

@@ Line 4: / Line 4: @@
 |Offering=PureEngageCloud
 |SMART_Benefits={{SMART Benefits
-|UCBenefitID=Improved Sales Rep / Agent Productivity
+|UCBenefitID=Improved Employee Productivity
 |UCBenefit=Sales reps are empowered with real time customer journey data from your website. This visibility allows them to personalize and prioritize engagements with prospective customers. Productivity is improved when sales reps interact when they have the most impact. Our software predicts which prospects are most likely to buy or abandon based on outcomes from previous customers taken guesswork out of the equations for your sales teams.
 }}{{SMART Benefits
@@ Line 10: / Line 10: @@
 |UCBenefit=Accelerate sales cycles and lead conversion rates (MQL to SQL to conversion) by engaging prospects or online shoppers in real time—at the right time—as they browse your website.
 }}{{SMART Benefits
-|UCBenefitID=Improved Customer Experience
+|UCBenefitID=Improved Net Promoter Score
 |UCBenefit=Improve CX and NPS scores and reduce Customer Effort by providing customers with a more timely and meaningful engagement online.
 }}{{SMART Benefits
@@ Line 49: / Line 49: @@
 * Deflect from live agent contact by proactively displaying additional information or offering the most cost-effective channel for the specific customer segment.
 |BuyerPersonas=Chief Digital Officer, Head of Sales, Head of Ecommerce
 |MaturityLevel=Differentiated
 |CloudAssumptionsAdditional_Sales=Existing PEC customers must upgrade to:
@@ Line 55: / Line 58: @@
 *Shared Designer v9
 *Advanced Chat (Nexus)
 |BusinessImageFlow={{SMART BusinessImageFlow
 |BusinessFlow='''Main Flow'''
@@ Line 111: / Line 116: @@
 * Collection of visitor's contact details
 * Engagement over chat session booking
 |DistributionLogic=The distribution of the interaction is determined by the target expression and virtual queue configured in the Genesys Predictive Engagement rules.
 |CustomerInterfaceRequirements=* Based on Genesys Widgets 9 with standard capabilities to adapt to customer corporate identity
@@ Line 124: / Line 130: @@
 General logic for routing of interactions uses part of these cases. If CE18 is already deployed and customized, SL09 design and configuration must take it into account.
 |RequiresAll=CE18
 |SMART_PremiseAssumptions={{SMART PremiseAssumptions
 |Premise_Assumption=N/A
 }}
-|PremiseAssumptions=Genesys Predictive Engagement desktop gadgets are integrated into Workspace Desktop Edition.
+|PremiseAssumptionsAdditional=
 |DocVersion=v 1.0.1
 }}

← Older revision	Revision as of 11:24, July 15, 2020
(No difference)

@@ Line 2: / Line 2: @@
 |ID=SL09
 |Title=Genesys Predictive Engagement
-|Offering=PureEngageCloud
+|Offering=GenesysEngage-cloud
 |SMART_Benefits={{SMART Benefits
 |UCBenefitID=Improved Employee Productivity

@@ Line 33: / Line 33: @@
 With Genesys Predictive Engagement, you can predict and prioritize high-value leads for your sales team to engage and proactively offer chat to better utilize your staff and reduce your costs. Genesys Predictive Engagement uses machine learning to track the progress of website visitors towards defined outcomes–purchase completion, requesting a quote–and enables the business to define rules to trigger intervention only at the points when it is needed most.
 |UCSummary=Genesys Predictive Engagement monitors individual customer journeys on your company website and applies machine learning, dynamic segmentation, and real-time outcome scoring to identify the right moments for proactive engagement with the right customer via chat or content offer. When the visitor interacts, the sales rep has the customer journey information at their fingertips.
-|Description=* Reporting, SFDC Third-Party Integration and Content Offer capabilities with Genesys Predictive Engagement (Altocloud) are planned in 2019, for further details please contact your respective Product Manager.
+|Description=*Reporting, SFDC Third-Party Integration and Content Offer capabilities with Genesys Predictive Engagement (Altocloud) are planned in 2019, for further details please contact your respective Product Manager.
-* Customer Service applications of this use case are covered by a use case, CE37.<br />
+*Customer Service applications of this use case are covered by a use case, CE37.<br />
-|PainPoints=* Inability to see, understand and engage in real time with customers and prospects across channels
+|PainPoints=* Inability to see, understand and engage in real time with customers and prospects across channels.
-* Low conversion rate on website
+* Low conversion rate on website.
-* Inside sales poorly utilized
+* Poorly utilized inside sales staff.
-* Hard to create and convert qualified online sales opportunities
+* Difficulty in creating and converting qualified online sales opportunities.
-* Website user journeys not optimized for efficient engagement through self and assisted service
+* Low optimization of website user journeys of efficient engagement through self- and assisted- service.
-|DesiredState=* Use journey analytics to detect where customers or prospects struggle on a website and use this information to improve their purchasing journeys
+|DesiredState=* Use journey analytics to detect where customers or prospects struggle on a website. Use this information to improve their purchasing journeys.
-* Identify the visitor's segmentation, monitor their web behavior, and predict their outcome score related to the online purchasing process
+* Identify the visitor's segmentation, monitor their web behavior, and predict their outcome score related to the online purchasing process.
-* Use machine learning to profile behavior, predict outcomes and allow organizations to define rules on when to intervene
+* Use machine learning to profile behavior, predict outcomes, and allow organizations 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
+* Proactively engage with prospects via chat or content offer if this score drops below a defined threshold while on the website.
-* Engage a sales rep at the critical point in the sales process to increase likelihood of closing the sale, while improving customer experience
+* Engage a sales rep at the critical point in the sales process to increase likelihood of closing the sale, while improving customer experience.
-* Identify hot leads on the website via their browsing behavior and heir likelihood to perform a specific action
+* Identify hot leads on the website via customer browsing behavior and predict customer likelihood to perform a specific action.
-* Provide sales rep with context from the customer journey on the website
+* Provide sales rep with context from the customer journey on the website.
-* Deflect from live agent contact by proactively displaying additional information or offering the most cost-effective channel for the specific customer segment
+* Deflect from live agent contact by proactively displaying additional information or offering the most cost-effective channel for the specific customer segment.
 |BuyerPersonas=Chief Digital Officer, Head of Sales, Head of Ecommerce
 |MaturityLevel=Differentiated
@@ Line 119: / Line 119: @@
 # Visitor Activity on the website: Provides the count of visits filtered by time range, segments matched, and outcomes achieved.
 # Action Map Performance: Provides the count of actions that were offered, accepted, and rejected filtered by time range.
 |GeneralAssumptions=Genesys Widgets must be used. Customer must deploy both Predictive Engagement and Widgets code snippets on their website / web pages.
@@ Line 128: / Line 127: @@
 |Premise_Assumption=N/A
 }}
 }}

@@ Line 4: / Line 4: @@
 |Offering=PureEngageCloud
 |SMART_Benefits={{SMART Benefits
-|UCBenefitID=Increased Revenue
+|UCBenefitID=Improved Sales Rep / Agent Productivity
-|UCBenefit=Increase conversions and revenue closure by engaging the right shoppers at the right time and Accelerate sales cycle and lead conversion rates (MQL to SQL to conversion)
+|UCBenefit=Sales reps are empowered with real time customer journey data from your website. This visibility allows them to personalize and prioritize engagements with prospective customers. Productivity is improved when sales reps interact when they have the most impact. Our software predicts which prospects are most likely to buy or abandon based on outcomes from previous customers taken guesswork out of the equations for your sales teams.
 }}{{SMART Benefits
-|UCBenefitID=Improved Employee Utilization
+|UCBenefitID=Increased Sales Conversions
-|UCBenefit=Engage sales reps only when they can have the most impact on the sale
+|UCBenefit=Accelerate sales cycles and lead conversion rates (MQL to SQL to conversion) by engaging prospects or online shoppers in real time—at the right time—as they browse your website.
 }}{{SMART Benefits
 |UCBenefitID=Improved Customer Experience
-|UCBenefit=Give sales reps visibility into the real-time customer journey and , allowing the right agent to prioritize their time with the right customer at the right time and no longer disrupt the website visitor experience with unnecessary offers of chat or interaction
+|UCBenefit=Improve CX and NPS scores and reduce Customer Effort by providing customers with a more timely and meaningful engagement online.
 }}{{SMART Benefits
-|UCBenefitID=Reduced Administration Costs
+|UCBenefitID=Increased Quality of Lead Conversion
-|UCBenefit=Reduce customer acquisition cost (CAC)
+|UCBenefit=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.
 }}
 |UCIntro=This use case is the subject of an Early Adopter Program (EAP). Please contact Lindsay Frazier, Product Management for more information.
@@ Line 38: / Line 44: @@
 * Use machine learning to profile behavior, predict outcomes and allow organizations to define rules on when to intervene
 * Proactively engage with prospects via chat or content offer if this score drops below a defined threshold while on the website
-* Engage a sales rep at the critical point in the sales process to increase likelihood of closing the sale, while improving customer experienceIdentify hot leads on the website via their browsing behavior and heir likelihood to perform a specific action
+* Engage a sales rep at the critical point in the sales process to increase likelihood of closing the sale, while improving customer experience
 * Provide sales rep with context from the customer journey on the website
 * Deflect from live agent contact by proactively displaying additional information or offering the most cost-effective channel for the specific customer segment
@@ Line 76: / Line 83: @@
 * 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 segments, these are configured upfront. Typical outcomes include:
+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:
 * Proceed to checkout with a shopping cart
 * Submit payment
 * Download a whitepaper
 * 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====
@@ Line 102: / Line 110: @@
 * Collection of visitor's contact details
 * Engagement over chat session booking
-|DistributionLogic====Distribution Logic===
+|DistributionLogic=The distribution of the interaction is determined by the target expression and virtual queue configured in the Genesys Predictive Engagement rules.
 |CustomerInterfaceRequirements=* Based on Genesys Widgets 9 with standard capabilities to adapt to customer corporate identity
 |AgentDeskRequirements=N/A
@@ Line 113: / Line 120: @@
 # Action Map Performance: Provides the count of actions that were offered, accepted, and rejected filtered by time range.
 |SMART_HybridAssumptions={{SMART HybridAssumptions
-|Hybrid_Assumption=v 1.0.0
+|Hybrid_Assumption=v 1.0.1
 }}
-|GeneralAssumptions=Genesys Widgets must be used. Customer must deploy both  and Widgets code snippets on their website / web pages.
+|GeneralAssumptions=Genesys Widgets must be used. Customer must deploy both Predictive Engagement and Widgets code snippets on their website / web pages.