UseCases/Current/PureConnect/SL09 - Revision history

Shlandy93: Redirected page to UseCases/Current/PureConnect/CE37

2020-09-24T14:42:46Z

Redirected page to UseCases/Current/PureConnect/CE37

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WikiSysop: Text replacement - "\|Offering?=([^\|]*)PureEngage" to "|Offering=$1GenesysEngage-onpremises"

2020-07-15T11:33:48Z

Text replacement - "\|Offering?=([^\|]*)PureEngage" to "|Offering=$1GenesysEngage-onpremises"

Shlandy93 at 11:03, April 20, 2020

2020-04-20T11:03:08Z

Shlandy93 at 16:49, March 10, 2020

2020-03-10T16:49:36Z

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

2020-01-20T20:53:31Z

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Shlandy93: Created page with "{{SMART UseCase |ID=SL09 |Title=Genesys Predictive Engagement |Offering=PureEngage |SMART_Benefits={{SMART Benefits |UCBenefitID=Improved Sales Rep / Agent Productivity |UCBen..."

2019-10-30T11:00:51Z

Created page with "{{SMART UseCase |ID=SL09 |Title=Genesys Predictive Engagement |Offering=PureEngage |SMART_Benefits={{SMART Benefits |UCBenefitID=Improved Sales Rep / Agent Productivity |UCBen..."

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{{SMART UseCase
|ID=SL09
|Title=Genesys Predictive Engagement
|Offering=PureEngage
|SMART_Benefits={{SMART Benefits
|UCBenefitID=Improved Sales Rep / Agent 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
|UCBenefitID=Increased Sales Conversions
|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=Improve CX and NPS scores and reduce Customer Effort by providing customers with a more timely and meaningful engagement online.
}}{{SMART Benefits
|UCBenefitID=Increased Quality of Lead Conversion
|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.
}}{{SMART Benefits
|UCBenefitID=Reduced Sales and Marketing Costs
|UCBenefit=Reduce customer acquisition cost (CAC). Predict which prospects are most likely to buy based on successful outcomes from previous customers. Use these insights to focus marketing efforts and ad spend on a target profiles or sales sales activities.
}}{{SMART Benefits
|UCBenefitID=Improved cross-sell and up-sell (Increase Customer Lifetime Value)
|UCBenefit=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.
}}
|UCIntro=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, Callback, Content Offer and third-party integration capabilities with Predictive Engagement are awaiting common architecture direction, for further details please contact your respective Product Manager. Customer Service applications of this use case are covered by a use case, CE37.
|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
* Call center agents are not able to serve customers with conversion, cross-sell, or up-sell opportunities
* 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 thresholdwhile 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 predict their 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
|PremiseAssumptionsAdditional_Sales=Altocloud for on-premises is sold as subscription only. On-premises perpetual customers may consume through subscription.
|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. 
# The combination of segment and variations in outcome score can eventually trigger a chat offer to the visitor 
# An algorithm determines the predicted availability of sales reps to handle the interactions.
# If sales reps are predicted to be 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 skills, media, language, and other ACD routing choices.
# 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 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====
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

The configuration for Altocloud is documented here:

https://help.genesys.com/cic/mergedProjects/wh_tr/desktop/pdfs/altocloud_integration_tr.pdf

====BL4 – Agent Availability====
Altocloud 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 Window====
Genesys Widgets is used for:
* Invite messages for chat
* 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 with standard capabilities to adapt to customer corporate identity
|AgentDeskRequirements=* Requires Interaction Connect
* Single sign-on is available as an option
|RealTimeReporting=No real-time dashboards are available for this use case.
|HistoricalReporting=A CX Insights Dashboard that shows the following will be available:
* Combined total number of chats (native, Altocloud and bots) for a workgroup for an interval.
* Total number of native chats for a workgroup for an interval.
* Total number of Altocloud chats for a workgroup for an interval.
* Total number of bot chats for a workgroup for an interval.
* Combined total number of chats (native, Altocloud and bots) for an agent for an interval.
* Total number of native chats for an agent for an interval.
* Total number of Altocloud chats for an agent for an interval.
* Total number of bot chats an agent for an interval.
|GeneralAssumptions=Genesys Widgets must be used. Customer must deploy both Altocloud 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.

Genesys Interaction Connect release 2019R1 is required.
|RequiresAll=CE18
|PremiseAssumptionsAdditional=* Genesys Altocloud for On Premises is sold as Subscription only (On Premises Perpetual customers may consume through subscription).
|SMART_HybridAssumptions={{SMART HybridAssumptions
|Hybrid_Assumption=v 1.0.0
}}
}}

@@ Line 2: / Line 2: @@
 |ID=SL09
 |Title=Genesys Predictive Engagement
-|Offering=PureEngage
+|Offering=GenesysEngage-onpremises
 |SMART_Benefits={{SMART Benefits
 |UCBenefitID=

@@ Line 4: / Line 4: @@
 |Offering=PureEngage
 |SMART_Benefits={{SMART Benefits
-|UCBenefitID=Improved Sales Rep / Agent Productivity
+|UCBenefitID=
 |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 46: / Line 46: @@
 * 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
 |PremiseAssumptionsAdditional_Sales=Altocloud for on-premises is sold as subscription only. On-premises perpetual customers may consume through subscription.
 |BusinessImageFlow={{SMART BusinessImageFlow
@@ Line 107: / Line 112: @@
 * Collection of visitor's contact details
 * Engagement over chat session booking<br />
 |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 with standard capabilities to adapt to customer corporate identity
@@ Line 126: / Line 132: @@
 Genesys Interaction Connect release 2019R1 is required.
 |RequiresAll=CE18
 |PremiseAssumptionsAdditional=* Genesys Altocloud for On Premises is sold as Subscription only (On Premises Perpetual customers may consume through subscription).
 |DocVersion=v 1.0.0
 }}

@@ Line 32: / Line 32: @@
 |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, Callback, Content Offer and third-party integration capabilities with Predictive Engagement are awaiting common architecture direction, for further details please contact your respective Product Manager. Customer Service applications of this use case are covered by a use case, CE37.
-|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.
-* <span>Call center agents are not able to serve customers with conversion, cross-sell, or up-sell opportunities</span>
+* Difficulty in creating and converting qualified online sales opportunities.
-* Hard to create and convert qualified online sales opportunities
+* Low optimization of website user journeys of efficient engagement through self- and assisted- service.
-* 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. Use this information to improve their purchasing journeys.
-|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.
-* 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 intervention is necessary.
-* 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.
-* Proactively engage with prospects via chat or content offer if this score drops below a defined thresholdwhile 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 experienceIdentify hot leads on the website via their browsing behavior and predict their 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