Difference between revisions of "UseCases/Current/GenesysEngage-onpremises/SL09"
From Genesys Documentation
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|SMART_Benefits={{SMART Benefits | |SMART_Benefits={{SMART Benefits | ||
|UCBenefitID=Increased Revenue | |UCBenefitID=Increased Revenue | ||
| − | |UCBenefit=Increase conversions and revenue closure by engaging the right shoppers at the right time | + | |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 | }}{{SMART Benefits | ||
|UCBenefitID=Improved Employee Utilization | |UCBenefitID=Improved Employee Utilization | ||
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}}{{SMART Benefits | }}{{SMART Benefits | ||
|UCBenefitID=Improved Customer Experience | |UCBenefitID=Improved Customer Experience | ||
| − | |UCBenefit=Give sales reps visibility into the real-time customer journey and personas, allowing | + | |UCBenefit=Give sales reps visibility into the real-time customer journey and personas, 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 | }}{{SMART Benefits | ||
|UCBenefitID=Reduced Administration Costs | |UCBenefitID=Reduced Administration Costs | ||
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|UCIntro=This use case is the subject of an Early Adopter Program (EAP). Please contact Lindsay Frazier, Product Management for more information. | |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). This use case replaces SL03, SL04 and SL08 which were based on Web Engagement. | |
| − | + | |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. | |
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| − | |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 | ||
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| − | With Genesys Altocloud, you can proactively offer the right type of | + | With Genesys Altocloud, you can predict and prioritize high-value leads for your sales team to engage and proactively offer the right type of engagement - chat, or content offer – depending on what is right for this customer or prospect and right for you 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= | + | |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= | + | |Description=Predictive Engagement monitors individual customer journeys on a company website and applies machine learning, dynamic personas, and outcome probabilities to identify the right moments for proactive engagement via chat, callback, or content offer. It can instantly notify sales reps about hot leads and their behaviors. When the consumer interacts, the sales rep will have the customer journey information at their fingertips. |
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| − | + | Reporting, Callback, SFDC Third-Party Integration and Content Offer capabilities with Altocloud are planned in 2019, for further details please contact your respective Product Manager. | |
| − | |PainPoints=* | + | Customer Service applications of this use case are covered by a use case, CE37. |
| − | * | + | This use case replaces SL03, SL04 and SL08 which were based on Web Engagement. |
| − | * | + | |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 |
| − | |DesiredState=* | + | * 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 | |BuyerPersonas=Chief Digital Officer, Head of Sales, Head of Ecommerce | ||
| − | |CloudAssumptions= | + | |MaturityLevel=Differentiated |
| + | |CloudAssumptions= | ||
|PremiseAssumptions=Genesys Altocloud desktop gadgets are integrated into Workspace Desktop Edition. | |PremiseAssumptions=Genesys Altocloud desktop gadgets are integrated into Workspace Desktop Edition. | ||
| + | |PremiseAssumptionsAdditional_Sales=Callback, SFDC Third-Party Integration and Content Offer capabilities with Altocloud are planned in 2019, for further details please contact your respective Product Manager. | ||
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| + | Historical reporting is planned in 2019, for further details please contact your respective Product Manager. | ||
|BusinessImageFlow={{SMART BusinessImageFlow | |BusinessImageFlow={{SMART BusinessImageFlow | ||
|BusinessFlow='''Main Flow''' | |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/ | + | |BusinessImage=https://www.lucidchart.com/documents/edit/c0bff7b4-973d-451c-b61d-56682c06a23c/0 |
| − | |BusinessFlowDescription= | + | |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 | + | # Genesys continues to monitor website behavior and update segment attribution and outcome score as appropriate.<br /> |
| − | # Genesys continues to monitor website behavior and update | + | # The combination of segment and variations in outcome score can eventually trigger one of the following actions:<br /> |
| − | + | #* Offer chat to the visitor | |
| − | # The combination of | + | #* Display a content offer to the visitor<br /> |
| − | + | # In the case of chat, an algorithm determines the predicted availability of sales reps to handle the interactions. | |
| − | #* Offer chat to the | + | # If sales reps are predicted to be available to handle chat, a proactive invitation to chat is presented to the visitor |
| − | #* Display a content offer to the | + | # If the visitor accepts the invite, a popup registration window enables the visitor to enter their data. |
| − | # In the case of | ||
| − | # If sales reps are predicted to be available to handle chat | ||
| − | # If the | ||
}}{{SMART BusinessImageFlow | }}{{SMART BusinessImageFlow | ||
|BusinessFlow='''Routing''' | |BusinessFlow='''Routing''' | ||
| − | This diagram details the routing that takes place before and during the chat | + | This diagram details the routing that takes place before and during the chat. |
| − | |BusinessImage=https://www.lucidchart.com/documents/edit/ | + | |BusinessImage=https://www.lucidchart.com/documents/edit/3cb8e177-94fc-4921-9724-949462dc7f80/0 |
| − | |BusinessFlowDescription=# Genesys routes the interaction to a sales rep based on the skills, media, and language set in Genesys | + | |BusinessFlowDescription=# Genesys routes the interaction to a sales rep based on the skills, media, and language set in Genesys Predictive Engagement (target expression and virtual queue). |
| − | # Sales rep and | + | # 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. | # After the conversation ends, the sales rep agent sets a disposition code within their desktop to record the outcome of the conversation. | ||
}} | }} | ||
|BusinessLogic=====<span class="mw-headline" id="BL1_.E2.80.93_Customer_Identification">BL1 – Customer Identification</span>==== | |BusinessLogic=====<span class="mw-headline" id="BL1_.E2.80.93_Customer_Identification">BL1 – Customer Identification</span>==== | ||
| − | 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 | + | 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. |
| − | ====<span class="mw-headline" id="BL2_.E2.80.93_Persona_and_Outcome_Configuration">BL2 – | + | ====<span class="mw-headline" id="BL2_.E2.80.93_Persona_and_Outcome_Configuration">BL2 – Segment and Outcome Configuration</span>==== |
| − | + | 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. | * 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 | + | * 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 | * Proceed to checkout with a shopping cart | ||
* Submit payment | * Submit payment | ||
* Download a whitepaper | * 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 | + | * 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). |
====<span class="mw-headline" id="BL3_.E2.80.93_Action_Map_Configuration">BL3 – Action Map Configuration</span>==== | ====<span class="mw-headline" id="BL3_.E2.80.93_Action_Map_Configuration">BL3 – Action Map Configuration</span>==== | ||
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: | 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 | * User activity | ||
| − | * Outcome score - Typically, a drop in outcome score for a specific | + | * 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 | * Invite to Chat | ||
| − | + | * Display additional content to the customer (Content Offer) | |
| − | * Display additional content to the customer (Content Offer) | ||
====<span class="mw-headline" id="BL4_.E2.80.93_Pacing_Service">BL4 – Pacing Service</span>==== | ====<span class="mw-headline" id="BL4_.E2.80.93_Pacing_Service">BL4 – Pacing Service</span>==== | ||
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Genesys Widgets is used for: | Genesys Widgets is used for: | ||
| − | * Invite messages for chat | + | * Invite messages for chat |
* Collection of visitor's contact details | * Collection of visitor's contact details | ||
| − | * Engagement over chat session | + | * Engagement over chat session booking |
* Content offers | * Content offers | ||
|DistributionLogic====<span class="mw-headline" id="Distribution_Logic">Distribution Logic</span>=== | |DistributionLogic====<span class="mw-headline" id="Distribution_Logic">Distribution Logic</span>=== | ||
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Interaction-related reporting is based on Genesys Interactive Insights (GI2). Capabilities are similar to<span>Call Routing (CE01), Chat Routing (CE18) and Digital Callback (CE22).</span> | Interaction-related reporting is based on Genesys Interactive Insights (GI2). Capabilities are similar to<span>Call Routing (CE01), Chat Routing (CE18) and Digital Callback (CE22).</span> | ||
| − | |GeneralAssumptions=Genesys Widgets must be used. | + | |SMART_HybridAssumptions={{SMART HybridAssumptions |
| + | |Hybrid_Assumption=v 1.1.2 | ||
| + | }} | ||
| + | |GeneralAssumptions=Genesys Widgets must be used. Customer must deploy both Altocloud and Widgets code snippets on their website / web pages. | ||
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|SMART_PremiseAssumptions={{SMART PremiseAssumptions | |SMART_PremiseAssumptions={{SMART PremiseAssumptions | ||
|Premise_Assumption=Genesys Altocloud desktop gadgets are integrated into Workspace Desktop Edition. | |Premise_Assumption=Genesys Altocloud desktop gadgets are integrated into Workspace Desktop Edition. | ||
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}} | }} | ||
}} | }} | ||
Revision as of 09:24, February 25, 2019
No results
Important
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). This use case replaces SL03, SL04 and SL08 which were based on Web Engagement.No results
Contents
Use Case Overview
Story and Business Context
Info needed.
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: Info needed.
*You can sort all use cases according to their stated benefits here: Sort by benefits
Summary
Info needed.
Use Case Definition
Info needed.
Business and Distribution Logic
Business Logic
Info needed.
Customer-facing Considerations
Interdependencies
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 |
|---|---|---|---|
| None | None | None | None |
Document Version
Needs info.