SL09/Canonical - Revision history

Shlandy93 at 15:31, June 17, 2020

2020-06-17T15:31:25Z

Shlandy93 at 14:02, March 31, 2020

2020-03-31T14:02:48Z

Bgrenon: 1 revision imported

2020-03-26T15:34:43Z

1 revision imported

smart_wiki>Shlandy93 at 16:16, March 24, 2020

2020-03-24T16:16:08Z

Shlandy93 at 16:51, March 10, 2020

2020-03-10T16:51:42Z

Shlandy93 at 09:45, June 20, 2019

2019-06-20T09:45:35Z

Bgrenon: 1 revision imported

2018-08-22T18:18:35Z

1 revision imported

Cboring at 13:53, August 8, 2018

2018-08-08T13:53:14Z

New page

{{SMART Meta
|SolutionCategory=SL
|Solution=Sales
|Title=Genesys Predictive Engagement
|Subtitle=Use machine learning powered journey analytics to monitor website activity, predict visitor outcomes, and proactively engage with prospects and customers
}}
{{SMART Canonical
|BuyerPersonas=Chief Digital Officer, Head of Ecommerce, Head of Sales
|QualifyingQuestions=# What insights do you have about the behaviors that determine whether a website "browser" will become a customer?
# How do you know when to engage with online customers to increase your sales conversions?
# Which channels of engagement can you use today to proactively to engage visitors to your website?
|PlatformChallenge=It’s challenging to identify the right individual, the best moments, and the optimal ways to engage online. Companies want to shape their customers’ journeys and drive them towards desirable outcomes, but it’s hard to utilize all of the available data in a way that is meaningful and actionable. In addition, consumers expect fast answers, but growing your inside or eCommerce sales staff is costly.
|PlatformSolution=Proactively lead customers to successful journeys on your website. Apply machine learning, dynamic personas, and outcome probabilities to identify the right moments for proactive engagement via a chat, callback, or content offer. Notify sales reps of hot leads and provide insights to their buying journey.
}}
{{SMART DataSheetFlow
|Flow=Consumer is browsing on website
}}
{{SMART DataSheetFlow
|Flow=Online journey tracked and likelihood to buy assessed
}}
{{SMART DataSheetFlow
|Flow=System predicts the right moment to engage the consumer
}}
{{SMART DataSheetFlow
|Flow=At the right moment the consumer is offer chat, callback or content offer
}}
{{SMART DataSheetFlow
|Flow=Sales Rep and consumer connect to resolve issues and complete the purchase
}}
{{SMART Benefits
|CanonicalBenefitID=Increased Revenue
|CanonicalBenefit=Engaging the right consumers at the right time increases sales conversions and revenue growth
}}
{{SMART Benefits
|CanonicalBenefitID=Improved Customer Experience
|CanonicalBenefit=Website visitor experience is not disrupted with unnecessary offers of chat or interaction
}}
{{SMART Benefits
|CanonicalBenefitID=Improved Employee Utilization
|CanonicalBenefit=Engaging sales reps only when assistance is needed and providing them with online journey insights
}}

@@ Line 6: / Line 6: @@
 }}
 {{SMART Canonical
 |PlatformChallenge=It’s challenging to identify the right individual, the best moments, and the optimal ways to engage online. Companies want to shape their customers’ journeys and drive them towards desirable outcomes, but it’s hard to utilize all of the available data in a way that is meaningful and actionable. In addition, consumers expect fast answers, but growing your inside or eCommerce sales staff is costly.
 |PlatformSolution=Proactively lead customers to successful journeys on your website. Apply machine learning, dynamic personas, and outcome probabilitiesto identify the right moments for proactive engagement via a web chat.
@@ Line 13: / Line 14: @@
 *Difficulty in creating and converting qualified online sales opportunities.
 *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. 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.
+|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.
-*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.
+*Identify the visitor's segmentation, monitor their web behavior, and predict their outcome score related to the online purchasing process 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.
 *Identify hot leads on the website via customer browsing behavior and predict customer likelihood to perform a specific action.
 |HighLevelFlowLucid=https://www.lucidchart.com/documents/edit/aa57095f-7276-4379-9221-38b3d5b3f633/0
 |BuyerPersonas=Chief Marketing Officer, Head of Marketing Operations, Head of Sales Operations

@@ Line 8: / Line 8: @@
 |PlatformChallenge=It’s challenging to identify the right individual, the best moments, and the optimal ways to engage online. Companies want to shape their customers’ journeys and drive them towards desirable outcomes, but it’s hard to utilize all of the available data in a way that is meaningful and actionable. In addition, consumers expect fast answers, but growing your inside or eCommerce sales staff is costly.
 |PlatformSolution=Proactively lead customers to successful journeys on your website. Apply machine learning, dynamic personas, and outcome probabilitiesto identify the right moments for proactive engagement via a web chat.
-|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.
-* Poorly utilized inside sales staff.
+*Poorly utilized inside sales staff.
-* Difficulty in creating and converting qualified online sales opportunities.
+*Difficulty in creating and converting qualified online sales opportunities.
-* Low optimization of website user journeys of 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. 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.
+|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.
-* 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.
+*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.
-* 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.
-* Identify hot leads on the website via customer browsing behavior and predict customer 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.
-* 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.
 |HighLevelFlowLucid=https://www.lucidchart.com/documents/edit/aa57095f-7276-4379-9221-38b3d5b3f633/0
 |BuyerPersonas=Chief Marketing Officer, Head of Marketing Operations, Head of Sales Operations
@@ Line 42: / Line 42: @@
 {{SMART Benefits
 |CanonicalBenefitID=Improved Employee Productivity
-|CanonicalBenefit=Representatives are empowered with real time customer journey data from your website which allows them to personalize and prioritize engagements with prospective and existing customers. Productivity is improved when reps interact when they have the most likely impact on the sale or customer service
+|CanonicalBenefit=Representatives are empowered with real time customer journey data from your website which allows them to personalize and prioritize engagements with prospective and existing customers.
 }}
 {{SMART Benefits

@@ Line 8: / Line 8: @@
 |PlatformChallenge=It’s challenging to identify the right individual, the best moments, and the optimal ways to engage online. Companies want to shape their customers’ journeys and drive them towards desirable outcomes, but it’s hard to utilize all of the available data in a way that is meaningful and actionable. In addition, consumers expect fast answers, but growing your inside or eCommerce sales staff is costly.
 |PlatformSolution=Proactively lead customers to successful journeys on your website. Apply machine learning, dynamic personas, and outcome probabilitiesto identify the right moments for proactive engagement via a web chat.
 |HighLevelFlowLucid=https://www.lucidchart.com/documents/edit/aa57095f-7276-4379-9221-38b3d5b3f633/0
 |BuyerPersonas=Chief Marketing Officer, Head of Marketing Operations, Head of Sales Operations
@@ Line 31: / Line 41: @@
 }}
 {{SMART Benefits
-|CanonicalBenefitID=Increased Sales Conversions
+|CanonicalBenefitID=Improved Employee Productivity
-|CanonicalBenefit=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.
+|CanonicalBenefit=Representatives are empowered with real time customer journey data from your website which allows them to personalize and prioritize engagements with prospective and existing customers. Productivity is improved when reps interact when they have the most likely impact on the sale or customer service
 }}
 {{SMART Benefits
-|CanonicalBenefitID=Improved cross-sell and up-sell (Increase Customer Lifetime Value)
+|CanonicalBenefitID=Increased Revenue
-|CanonicalBenefit=Grow customer lifetime value by retaining customers through faster, more proactive and personalized service and cross-selling and upselling customers on products or services of greatest value to them. Altocloud uses data based a customer’s current interests, online journeys and prior online purchasing behavior to power our machine learning engine. Utilizing this predictive engagement machine learning engine, complimentary products and services can be suggested to agents or directly offered to customers at the moment of purchase, on the right channel and by the right agent in real-time.
+|CanonicalBenefit=Accelerate sales and conversion rates by engaging online shoppers in real time at the right time as they browse your website. Grow customer lifetime value through more proactive and personalized service and cross-selling and upselling customers on products or services of greatest value to them.
 }}
 {{SMART Benefits
-|CanonicalBenefitID=Improved Sales Rep / Agent Productivity
+|CanonicalBenefitID=Reduced Volume of Interactions
-|CanonicalBenefit=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 likely impact on the sale. Altocloud uses machine learning to predicts which prospects are most likely to buy or abandon based on outcomes from previous customer journey’s and alerts sales agents so they can take action to drive conversions in real-time.
+|CanonicalBenefit=Improve self-service rates by providing customers with the right information at the right time or proactively offering a chatbot to automate the conversation and prevent contact with an agent.
 }}

← Older revision	Revision as of 15:34, March 26, 2020
(No difference)

@@ Line 6: / Line 6: @@
 }}
 {{SMART Canonical
-|BuyerPersonas=Chief Digital Officer, Head of Ecommerce, Head of Sales
+|PlatformChallenge=It’s challenging to identify the right individual, the best moments, and the optimal ways to engage online. Companies want to shape their customers’ journeys and drive them towards desirable outcomes, but it’s hard to utilize all of the available data in a way that is meaningful and actionable. In addition, consumers expect fast answers, but growing your inside or eCommerce sales staff is costly.
 |QualifyingQuestions=# <span>What insights do you have about the behaviors that determine whether a website "browser" will become a customer?</span>
 # <span>How do you know when to engage with online customers to increase your sales conversions?</span>
 # <span>Which channels of engagement can you use today to proactively to engage visitors to your website?</span>
-|PlatformChallenge=It’s challenging to identify the right individual, the best moments, and the optimal ways to engage online. Companies want to shape their customers’ journeys and drive them towards desirable outcomes, but it’s hard to utilize all of the available data in a way that is meaningful and actionable. In addition, consumers expect fast answers, but growing your inside or eCommerce sales staff is costly.
+|DataSheetImage=SL09 - genesys predictive engagement for sales - header (2).png
 }}
 {{SMART DataSheetFlow
@@ Line 17: / Line 19: @@
 }}
 {{SMART DataSheetFlow
-|Flow=Online journey tracked and likelihood to buy assessed
+|Flow=Online journey tracked and likelihood to buy,  to request a quote, to fill in a form or downloading a document assessed...
 }}
 {{SMART DataSheetFlow
@@ Line 23: / Line 25: @@
 }}
 {{SMART DataSheetFlow
-|Flow=At the right moment the consumer is offer chat, callback or content offer
+|Flow=At the right moment the consumer is offered a chat session
 }}
 {{SMART DataSheetFlow
@@ Line 34: / Line 36: @@
 {{SMART Benefits
 |CanonicalBenefitID=Improved Customer Experience
-|CanonicalBenefit=Website visitor experience is not disrupted with unnecessary offers of chat or interaction
+|CanonicalBenefit=Website visitor experience is not disrupted with unnecessary offers of web chats
 }}
 {{SMART Benefits

← Older revision	Revision as of 18:18, August 22, 2018
(No difference)