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| − | {{SMART UseCase
| + | #REDIRECT [[UseCases/Current/GenesysEngage-onpremises/CE37]] |
| − | |ID=SL09
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| − | |Title=Genesys Predictive Engagement
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| − | |Offering=PureEngage
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| − | |SMART_Benefits={{SMART Benefits
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| − | |UCBenefitID=Reduced Sales and Marketing Costs
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| − | |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.
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| − | }}{{SMART Benefits
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| − | |UCBenefitID=Improved Net Promoter Score
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| − | |UCBenefit=Improve CX and NPS scores and reduce Customer Effort by providing customers with a more timely and meaningful engagement online.
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| − | }}{{SMART Benefits
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| − | |UCBenefitID=Increased Sales Conversions
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| − | |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.
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| − | }}{{SMART Benefits
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| − | |UCBenefitID=Increased Quality of Lead Conversion
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| − | |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.
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| − | }}{{SMART Benefits
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| − | |UCBenefitID=
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| − | |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.
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| − | }}{{SMART Benefits
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| − | |UCBenefitID=Improved cross-sell and up-sell (Increase Customer Lifetime Value)
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| − | |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.
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| − | }}
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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.
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| − | 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.
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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 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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| − | 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.
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| − | 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.
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| − | 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.
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| − | |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.
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| − | |Description=*Reporting, SFDC Third-Party Integration and Content Offer capabilities with Predictive Engagement are planned in 2019, for further details please contact your respective Product Manager.
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| − | *Customer Service applications of this use case are covered by a use case, CE37.
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| − | *This use case replaces SL03, SL04 and SL08 which were based on Web Engagement.
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| − | *AltoCloud is available to On Premises Perpetual customers through subscription.
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| − | |PainPoints=*Inability to see, understand and engage in real time with customers and prospects across channels.
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| − | *Low conversion rate on website.
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| − | *Poorly utilized inside sales staff.
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| − | *Difficulty in creating and converting qualified online sales opportunities.
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| − | *Low optimization of website user journeys of efficient engagement through self- and assisted- service.
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| − | |DesiredState=*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 to increase likelihood of closing the sale, while improving customer experience.
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| − | *Identify the visitor's segmentation, monitor their web behavior, and predict their outcome score related to the online purchasing process. By using machine learning to profile behavior, predict outcomes, and allow organizations to define rules on when intervention is necessary.
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| − | *Proactively engage with prospects via chat or content offer if this score drops below a defined threshold while on the website.
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| − | *Identify hot leads on the website via customer browsing behavior and predict customer likelihood to perform a specific action.
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| − | *Deflect from live agent contact by proactively displaying additional information or offering the most cost-effective channel for the specific customer segment.
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| − | |BuyerPersonas=Chief Digital Officer, Head of Sales, Head of Ecommerce
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| − | |QualifyingQuestions=
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| − | |MaturityLevel=Differentiated
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| − | |SellableItems=
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| − | |CloudAssumptions=
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| − | |CloudAssumptionsAdditional_Sales=
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| − | |PremiseAssumptions=
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| − | |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.
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| − | * Historical reporting is planned in 2019, for further details please contact your respective Product Manager.
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| − | |BusinessImageFlow={{SMART BusinessImageFlow
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| − | |BusinessFlow='''Main Flow'''
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| − | The following diagram shows the main flow of the use case, from the point of view of the system.
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| − | |BusinessImage=https://www.lucidchart.com/documents/edit/b31f9b5f-7084-4fc4-8129-0bc938776ddf/0
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| − | |BusinessFlowDescription=# A visitor starts browsing the company website.
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| − | # Genesys determines whether visitor is new or returning to website, and associates data from previous journeys.
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| − | # Genesys continues to monitor website behavior and update segment attribution and outcome score as appropriate.<br />
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| − | # The combination of segment and variations in outcome score can eventually trigger a chat to the visitor<br />
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| − | # An algorithm determines the predicted availability of sales reps to handle the interactions.
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| − | # If sales reps are predicted to be available to handle chat, a proactive invitation to chat is presented to the visitor
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| − | # If the visitor accepts the invite, a popup registration window enables the visitor to enter their data.<br />
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| − | }}{{SMART BusinessImageFlow
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| − | |BusinessFlow='''Routing'''
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| − | This diagram details the routing that takes place before and during the chat.
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| − | |BusinessImage=https://www.lucidchart.com/documents/edit/38673dbe-49ce-47b8-aba2-4a876d0d4fd7/0
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| − | |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).
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| − | # Sales rep and prospect are in conversation. The sales rep has access to full visitor context such as segment, journey information, and outcome score.
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| − | # After the conversation ends, the sales rep agent sets a disposition code within their desktop to record the outcome of the conversation.
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| − | }}
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| − | |BusinessLogic=====<span class="mw-headline" id="BL1_.E2.80.93_Customer_Identification">BL1 – Customer Identification</span>====
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| − | 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.
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| − | ====<span class="mw-headline" id="BL2_.E2.80.93_Persona_and_Outcome_Configuration">BL2 – Segment and Outcome Configuration</span>====
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| − | 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:
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| − | * Attributes, such as browser type, device type, location, marketing campaign they are associated with, UTM parameters, and the referral website.
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| − | * Journey pattern, such as web browsing behavior, searches performed on the website, items clicked, returning users, cart abandoner, and high order value.
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| − | 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:
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| − | * Proceed to checkout with a shopping cart
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| − | * Submit payment
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| − | * Download a whitepaper
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| − | * Book a demo or appointment
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| − | 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).
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| − | ====<span class="mw-headline" id="BL3_.E2.80.93_Action_Map_Configuration">BL3 – Action Map Configuration</span>====
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| − | 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:
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| − | * Segment
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| − | * User activity
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| − | * Outcome score - Typically, a drop in outcome score for a specific segment can trigger an action.
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| − | The following actions are part of this use case:
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| − | * Invite to Chat
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| − | | |
| − | ====<span class="mw-headline" id="BL4_.E2.80.93_Pacing_Service">BL4 – Pacing Service</span>====
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| − | Genesys' patented pacing algorithm automatically predicts sales rep availability, based on contact center statistics and tracking of ongoing engagement attempts. If no sales rep is predicted to be available for the requested media type and skill, no invite is sent.
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| − | | |
| − | ====BL5 – Customer Invite and Registration====
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| − | Genesys Widgets is used for:
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| − | * Invite messages for chat
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| − | * Collection of visitor's contact details
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| − | * Engagement over chat session booking
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| − | |DistributionImageFlow=
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| − | |DistributionLogic=The distribution of the interaction is determined by the target expression and virtual queue configured in the Genesys Predictive Engagement rules.
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| − | |CustomerInterfaceRequirements=* Based on Genesys Widgets 9 with standard capabilities to adapt to customer corporate identity
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| − | |AgentDeskRequirements=* Integration of Genesys desktop gadgets into Workspace Desktop Edition
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| − | * Single Sign-On (the sales rep logs into Workspace only once)
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| − | |RealTimeReporting=Interaction-related reporting is based on standard Pulse templates. Capabilities are similar to Chat Routing (CE18).
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| − | |HistoricalReporting=Interaction-related reporting is based on Genesys Interactive Insights (GI2). Capabilities are similar to Chat Routing (CE18).
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| − | |GeneralAssumptions=Genesys Widgets must be used. Customer must deploy both Predictive Engagement and Widgets code snippets on their website / web pages.
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| − | 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.
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| − | |CustomerAssumptions=
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| − | |RequiresAll=CE18
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| − | |RequiresOr=
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| − | |Optional=
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| − | |Exceptions=
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| − | |SMART_PremiseAssumptions={{SMART PremiseAssumptions
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| − | |Premise_Assumption=Genesys desktop gadgets are integrated into Workspace Desktop Edition.
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| − | }}
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| − | |PremiseAssumptionsAdditional=
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| − | |SMART_CloudAssumptions=
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| − | |CloudAssumptionsAdditional=
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| − | |SMART_HybridAssumptions=
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| − | |Requires=
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| − | |Conditions=
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| − | |DocVersion=v 1.1.4
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| − | }}
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