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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=Increased Revenue
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| − | |UCBenefit=Accelerate sales cycle and lead conversion rates (MQL to SQL to conversion)
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| − | }}{{SMART Benefits
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| − | |UCBenefitID=Increased Revenue
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| − | |UCBenefit=Increase conversions and revenue closure by engaging the right shoppers at the right time
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| − | }}{{SMART Benefits
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| − | |UCBenefitID=Improved Employee Utilization
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| − | |UCBenefit=Engage sales reps only when they can have the most impact on the sale
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| − | }}{{SMART Benefits
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| − | |UCBenefitID=Improved Customer Experience
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| − | |UCBenefit=Give sales reps visibility into the real-time customer journey and personas, allowing them to focus on the sale
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| − | }}{{SMART Benefits
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| − | |UCBenefitID=Reduced Administration Costs
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| − | |UCBenefit=Reduce customer acquisition cost (CAC)
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| − | }}{{SMART Benefits
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| − | |UCBenefitID=Improved Customer Experience
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| − | |UCBenefit=No longer disrupt the website visitor experience with unnecessary offers of chat or interaction
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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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| − | <span>Customer Service applications of this use case will be covered by a new use, CE37, which is currently being developed.</span>
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| − | <span>This use case replaces SL03, SL04 and SL08 which were based on Web Engagement.</span>
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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 digital customers 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 shape our individual customer’s journey and identify the customers who need engagement most. As a result, customers 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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| − | With Genesys Altocloud, you can proactively offer the right type of engagement–chat, callback, 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 Altocloud 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=<span>Genesys Altocloud monitors individual customer journeys on your company website and applies machine learning, dynamic personas, and outcome probabilities to identify the right moments for proactive engagement, via a chat, callback, or content offer. You can instantly notify your sales reps about hot leads and their behaviors. When the consumer interacts, the sales rep has the customer journey information at their fingertips.</span>
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| − | |Description=<span>Genesys</span><span>Predictive Engagement monitors individual customer journeys on a company website andapplies 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 repsabout hot leadsand their behaviors. When the consumer interacts, the sales repwill have the customer journey information at their fingertips.</span><span></span>
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| − | ''Customer Service applications of this use case will be covered by a new use case, CE37, currently being developed.''
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| − | ''This use case replaces SL03, SL04 and SL08 which were based on Web Engagement.''
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| − | |PainPoints=* <span>Inability to see, understand and engage in real time<span></span>prospect journeys across channels</span>
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| − | * <span>Low conversion rate on website</span>
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| − | * <span>Inside sales poorly utilized</span>
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| − | * <span>Hard to create and convert qualified online sales opportunities </span>
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| − | * <span>Website user journeys not optimized for efficient engagement<span> </span>through self and assisted service</span>
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| − | |DesiredState=* <span>Use journey analytics to detect where customers struggle on a website and use this information to improve their purchasing journeys</span>
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| − | * <span>Identify the user’s Persona, monitor their web behavior, and predict their outcome score related to the online purchasing process</span>
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| − | * <span>Use machine learning to profile behavior, predict outcomes and allow organizations to define rules on when to intervene</span>
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| − | * <span>Proactively engage with prospects via chat, callback or content offer if this score drops below a defined thresholdwhile on the website</span>
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| − | * <span>Engage a sales rep at the critical point in the sales process to increase likelihood of closing the sale, while improving customer experience</span>
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| − | * <span>Identify hot leads on the website via their browsing behavior and predict their likelihood to perform a specific action</span>
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| − | * <span>Provide sales rep with context from the customer journey on the website</span><span>Deflect from live agent contact by proactively displaying additional information or offering the most cost-effective channel for the specific customer segment</span><span></span>
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| − | |BuyerPersonas=Chief Digital Officer, Head of Sales, Head of Ecommerce
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| − | |CloudAssumptions=This use case is not currently available in PureEngage Cloud.
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| − | |PremiseAssumptions=Genesys Altocloud desktop gadgets are integrated into Workspace Desktop Edition.
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| − | |BusinessImageFlow={{SMART BusinessImageFlow
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| − | |BusinessFlow='''Main Flow'''
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| − | <span>The following diagram shows the main flow of the use case, from the point of view of the system.</span>
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| − | |BusinessImage=https://www.lucidchart.com/documents/edit/13e9d240-627d-4f64-ba18-c2d48f68c18e/1
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| − | |BusinessFlowDescription=<span></span>
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| − | # The customer starts browsing the company website.
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| − | # Genesys uses cookie information to determine returning customers and associate data from previous journeys to them.
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| − | # Genesys continues to monitor website behavior and update persona and outcome score as appropriate.
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| − | # If the identified persona is configured for a Slack message, a Slack message is sent out to the Account Team.
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| − | # The combination of persona and variations in outcome score can trigger one of the following actions:
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| − | #* Offer callback to the customer
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| − | #* Offer chat to the customer
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| − | #* Display a content offer to the customer
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| − | # In the case of callback or chat, a pacing 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 or callback, an invite is presented to the customer.
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| − | # If the customer accepts the invite, a popup registration window enables the customer to enter their data.
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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 or callback.
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| − | |BusinessImage=https://www.lucidchart.com/documents/edit/13e9d240-627d-4f64-ba18-c2d48f68c18e/2
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| − | |BusinessFlowDescription=# Genesys routes the interaction to a sales rep based on the skills, media, and language set in Genesys Altocloud (target expression and virtual queue).
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| − | # Sales rep and customer are in conversation. The sales rep has access to full customer context such as persona, 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 customer even across devices. If a customer 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 customer identity cannot be determined, the customer is handled as an anonymous user and all tracked data attached to them. Once the customer is identified, all tracking data collected and to be collected is associated to that specific customer.
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| − | ====<span class="mw-headline" id="BL2_.E2.80.93_Persona_and_Outcome_Configuration">BL2 – Persona and Outcome Configuration</span>====
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| − | Personas are a way to categorize visitors on the website into segments, based on common behavior and attributes. Personas are configured upfront during the configuration of the system.A persona is 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. Outcomes or goals are specific tasks you want your visitors to perform on your website. As personas, 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 - Genesys uses predictive analytics to evaluate in real time the probability for a specific outcome to be achieved, based on persona 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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| − | * Persona
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| − | * User activity
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| − | * Outcome score - Typically, a drop in outcome score for a specific persona can trigger an action.The following actions are part of this use case:
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| − | * Invite to Chat
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| − | * Invite to Callback
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| − | * Display additional content to the customer (Content Offer) as configured in Genesys Altocloud
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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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| − | <span style="font-size: 16.239999771118164px; font-weight: bold;">BL5 – Customer Invite and Registration Window</span>
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| − | Genesys Widgets is used for:
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| − | * Invite messages for chat and callback
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| − | * Collection of visitor's contact details
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| − | * Engagement over chat session or callback booking
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| − | * Content offers
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| − | |DistributionLogic====<span class="mw-headline" id="Distribution_Logic">Distribution Logic</span>===
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| − | <span>The distribution of the interaction is determined by the target expression and virtual queue configured in the Genesys Altocloud rules.</span>
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| − | |CustomerInterfaceRequirements=* Based on Genesys Widgets with standard capabilities to adapt to customer corporate identity
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| − | '''Third Party Integration Requirements'''
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| − | ====<span class="mw-headline" id="Marketo">Marketo</span>====
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| − | Existing integration with Marketo can be used as optional add-on for this use case.High-level functionality:
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| − | * Use cookie information to gather data on the Marketo campaign (such as Lead Score, Email Address, and Name)
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| − | * Display relevant data to sales rep
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| − | ====<span class="mw-headline" id="SFDC">SFDC</span>====
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| − | Existing Genesys Altocloud integration with SFDC is an optional add on for this use case. Website visitors can be associated to SFDC data via their email address. If they provide their email address (as when submitting a form), SFDC data is retrieved, used for context, and displayed to sales rep.
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| − | |AgentDeskRequirements=* Integration of Genesys Altocloud 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=Genesys Altocloud Analytical Dashboards report:
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| − | * Action Map Performance
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| − | ** Qualified
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| − | ** Offered
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| − | ** Accepted
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| − | ** Engaged
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| − | * Visits, Personas, and Outcomes
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| − | Interaction-related reporting is based on standard Pulse templates. Capabilities are similar to Call Routing (CE01), Chat Routing (CE18) and Digital Callback (CE22).
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| − | |HistoricalReporting=Genesys Altocloud Analytical Dashboards report:
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| − | * Action Map Performance
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| − | ** Qualified
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| − | ** Offered
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| − | ** Accepted
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| − | ** Engaged
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| − | * Visits, Personas, and Outcomes
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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>
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| − | |GeneralAssumptions=Genesys Widgets must be used.
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| − | General logic for routing of interactions uses part of these cases. If CE18 and/or CE22 are already deployed and customized, SL09 design and configuration must take it into account.
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| − | |RequiresOr=CE18, CE22
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| − | |SMART_PremiseAssumptions={{SMART PremiseAssumptions
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| − | |Premise_Assumption=Genesys Altocloud desktop gadgets are integrated into Workspace Desktop Edition.
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| − | |SMART_CloudAssumptions={{SMART CloudAssumptions
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| − | |Cloud_Assumption=This use case is not currently available in PureEngage Cloud.
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