ATC/Current/AdminGuide/Outcomes Overview - Revision history

Barbara.martin@genesys.com: Published

2021-05-18T18:35:30Z

Published

Barbara.martin@genesys.com: Published

2021-05-11T17:17:26Z

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Barbara.martin@genesys.com: Published

2021-04-14T15:17:13Z

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Barbara.martin@genesys.com: Published

2020-12-17T18:53:22Z

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Barbara.martin@genesys.com: Published

2020-12-17T15:29:17Z

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Barbara.martin@genesys.com: Published

2020-11-20T18:24:09Z

Published

Ed.jamer@genesys.com: Text replacement - "Altocloud's" to "Predictive Engagement's"

2020-11-17T14:56:38Z

Text replacement - "Altocloud's" to "Predictive Engagement's"

Harleyvibes: Published

2020-10-12T16:05:15Z

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Harleyvibes: Published

2020-10-12T16:01:41Z

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WikiSysop: Text replacement - "\|Platform=([^\|])GenesysEngage-onpremises([\|])" to "|Platform=$1GenesysEngage-cloud$2"

2020-07-25T02:30:44Z

Text replacement - "\|Platform=([^\|]*)GenesysEngage-onpremises([\|]*)" to "|Platform=$1GenesysEngage-cloud$2"

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 <br />
 |ComingSoon=No
 |Platform=PureConnect, GenesysCloud, GenesysEngage-cloud

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 <br />
 |ComingSoon=No
 |Platform=PureConnect, GenesysCloud, GenesysEngage-cloud

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 |TocName=Outcomes Overview
 |Context={{MINTYDOCSPRODUCT}}'s AI-powered outcome scoring service learns to predict your business outcomes using machine learning models that are unique to your business.
 |ComingSoon=No
 |Platform=PureConnect, GenesysCloud, GenesysEngage-cloud
@@ Line 19: / Line 22: @@
 Our team investigates and integrates new algorithms into the scoring service based on customer use cases.|}}
 |Status=No
 }}{{Section
 |sectionHeading=How {{MINTYDOCSPRODUCT}} gathers outcome probability data

@@ Line 23: / Line 23: @@
 |anchor=OutcomeModelDataGathering
 |alignment=Vertical
-|structuredtext={{MintyDocsProduct}} monitors all the ways visitors arrive on and interact with your webpages. For example, if you are an e-commerce site, {{MintyDocsProduct}} tracks how visitors navigate your website and place items in their shopping cart as they proceed to the checkout page.
+|structuredtext={{MintyDocsProduct}} monitors all the ways that visitors arrive on and interact with your webpages. For example, if you are an e-commerce site, {{MintyDocsProduct}} tracks how visitors navigate your website and place items in their shopping cart as they proceed to the checkout page.
 <br />{{NoteFormat|
 * How a visitor achieves a certain probability score is unique to your business and website.

@@ Line 10: / Line 10: @@
 |anchor=OutcomeModelOverview
 |alignment=Vertical
-|structuredtext={{MINTYDOCSPRODUCT}} uses AI to predict whether a customer will achieve a particular business objective. These business objectives are called '''outcomes'''. For each outcome you define, there is a machine learning model that evaluates a user's behavior against the behavior of other users on your website to determine the user's outcome score. Your organization's models are unique to you.
+|structuredtext={{MINTYDOCSPRODUCT}} uses AI to predict whether a visitor will achieve a particular business objective. These business objectives are called '''outcomes'''. For each outcome you define, there is a machine learning model that evaluates a visitor's behavior against the behavior of other visitors on your website to determine the visitor's outcome score. Your organization's models are unique to you.
-Each outcome score represents the likelihood that the user will achieve the particular business outcome, based on the actions the customer has taken so far during the session or on other activities related to the customer that are included in the appropriate events (for example, geolocation).
+Each outcome score represents the likelihood that the visitor will achieve the particular business outcome, based on the actions the visitor has taken so far during the session or on other activities related to the visitor that are included in the appropriate events (for example, geolocation).
-The model updates a user's score for each outcome in real-time, and a user's scores can change as they navigate your website.
+The model updates a visitor's score for each outcome in real-time, and a visitor's scores can change as they navigate your website.
-If a customer ends up talking with an agent, the agent can see the customer's outcome scores while viewing the customer's complete set of journey context data. In addition, the outcome scores can be used to trigger action maps that enhance a customer's engagement on your site. {{NoteFormat|Because our internal service handles the training and deployment of models, it is not possible to deploy customer-specific models with {{MINTYDOCSPRODUCT}}.
+If a visitor communicates with an agent, the agent can see the visitor's outcome scores while viewing the visitor's complete set of journey context data. Also, the outcome scores can trigger action maps that enhance a visitor's engagement on your site. {{NoteFormat|Because our internal service handles the training and deployment of models, it is not possible to deploy customer-specific models with {{MINTYDOCSPRODUCT}}.
 Our team investigates and integrates new algorithms into the scoring service based on customer use cases.|}}
 |Status=No
@@ Line 23: / Line 23: @@
 |anchor=OutcomeModelDataGathering
 |alignment=Vertical
-|structuredtext={{MINTYDOCSPRODUCT}} monitors all the ways your customers arrive at and interact with your website pages. For example, if you are an e-commerce site, {{MINTYDOCSPRODUCT}} tracks how customers navigate your site and place items in their shopping cart as they proceed to the checkout page.{{NoteFormat|
+|structuredtext={{MintyDocsProduct}} monitors all the ways visitors arrive on and interact with your webpages. For example, if you are an e-commerce site, {{MintyDocsProduct}} tracks how visitors navigate your website and place items in their shopping cart as they proceed to the checkout page.
 * How a visitor achieves a certain probability score is unique to your business and website.
 * Outcome scores and the associated data science are scored in a GDPR-compliant manner. Predictive Engagement's data scientists work exclusively on anonymized GDPR-compliant data.|1}}For more information, see {{Link-AnywhereElse|product=ATC|version=Current|manual=AdminGuide|topic=About_data_tracking}}.
@@ Line 31: / Line 32: @@
 |anchor=OutcomeModelStartTraining
 |alignment=Vertical
-|structuredtext=Each of your machine learning models must be trained before it can make predictions. In order to start the training, you must:
+|structuredtext=Each of your machine learning models require training before they can make predictions.
 #{{Link-SomewhereInThisVersion|manual=AdminGuide|topic=Manage_outcomes|display text=Create an outcome}}.
-#Have users actively using your tracked website. To verify user activity, use {{Link-SomewhereInThisVersion|manual=AdminGuide|topic=Live_Now}}.
+#Have visitors use your tracked website. To verify user activity, use {{Link-SomewhereInThisVersion|manual=AdminGuide|topic=Live_Now}}.
-After you complete these steps, the model training automatically begins within 24 hours. The training process is completed and the model is ready to work before the start of the next business day.{{NoteFormat|Initially, you will see only a green bar with a check next to it in the '''Outcome Scores''' section. This indicates that the outcome condition has been met.
+After you complete these steps, the model training begins automatically within 24 hours. The training process completes and the model is ready to work before the start of the next business day.{{NoteFormat|Initially, you see only a green bar with a check next to it in the '''Outcome Scores''' section. This bar indicates that the outcome condition was met.
-You will see outcome scores after your model has been trained. |}}<br />
+You see outcome scores after your model is trained. |}}<br />
 |Status=No
 }}{{Section
@@ Line 44: / Line 47: @@
 |anchor=OutcomeModelOngoingTraining
 |alignment=Vertical
-|structuredtext=The model training process is fully automated: you do not need a data scientist to start, monitor, or maintain the training. Your model is automatically retrained nightly using the last 30 days of your user data. In addition, your model is periodically evaluated and retrained on fresh data.
+|structuredtext=The model training process is fully automated. You do not need a data scientist to start, monitor, or maintain the training. Your model is retrained nightly using the last 30 days of your user data. Also, your model is evaluated periodically and retrained on fresh data.
-While the model is undergoing retraining, outcome scores are predicted using the previously trained version of the model. During the training process, users will be able to continue working as normal.
+While the model is undergoing retraining, outcome scores are predicted using the previously trained version of the model. During the training process, agents can continue working as normal.
-Your newly trained model is tested to ensure it performs better than the previous version in terms of its precision and recall. If the new model does not work at least as well as the previous model, the previous model is reinstated until more data is gathered. Historical models are not retained.{{NoteFormat|
+Your newly trained model is tested to ensure that it performs better than the previous version in terms of its precision and recall. If the new model does not work at least as well as the previous model, the previous model is reinstated until more data is gathered. Historical models are not retained.{{NoteFormat|&nbsp;
-* When you add a new outcome, your model training automatically accounts for it.
+* When you add a new outcome, your model training accounts for it automatically.
-* Automated model training and predictions are performed on the original customer journey events, which are not anonymized and will generally contain PII.|}}<br />
+* Automated model training and predictions are performed on the original customer journey events, which are not anonymized and generally contain PII.|}}<br />
 |Status=No
 }}{{Section
@@ Line 57: / Line 60: @@
 |anchor=OutcomeModelImprovePredictions
 |alignment=Vertical
-|structuredtext=In general, the longer your models run and the more data they evaluate, the better their predictions will be.
+|structuredtext=In general, the longer your models run and the more data they evaluate, the better their predictions are.
-The best way to improve a model's predictions is to increase the number of achieved outcomes. In general, your dataset should contain several hundred positive examples in order for your model to be adequately trained to make reliable predictions.
+The best way to improve a model's predictions is to increase the number of achieved outcomes. In general, your dataset should contain several hundred positive examples for your model to be adequately trained to make reliable predictions.
 Other factors can affect the accuracy of your model's performance, including:
-*The total number of customer journeys recorded
+*Total number of visitor journeys recorded
-*The frequency that an outcome occurs in the data
+*Frequency that an outcome occurs in the data
-*The richness of events produced by the customer{{NoteFormat|You can {{Link-SomewhereInThisVersion|manual=AdminGuide|topic=Web_tracking|anchor=ExcludeIPAddressesses|display text=exclude IP addresses}} to prevent internally generated events from influencing your models.|2}}
+*Richness of events that the visitor produces {{NoteFormat|You can {{Link-SomewhereInThisVersion|manual=AdminGuide|topic=Web_tracking|anchor=ExcludeIPAddressesses|display text=exclude IP addresses}} to prevent events generated internally from influencing your models.|2}}
 |Status=No
 }}{{Section
 |sectionHeading=Predict based on custom events
 |alignment=Vertical
-|structuredtext=Customers can provide predictions generated by other predictive systems as "custom events." Custom events can be consumed by the Outcome Score Service and used to train models and generate outcome predictions based on the features produced by an external system. This is another example of how a generalized AI system enables hyper-personalization.
+|structuredtext=You can provide predictions that other predictive systems generate as "custom events." The Outcome Score Service can consume the custom events and use them to train models and generate outcome predictions based on the features that an external system produces. This example shows how a generalized AI system enables hyper-personalization.
 <nowiki>**</nowiki>We need to put this in plain English, but I want clarification before I begin...

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 {{Article
 |Standalone=No
-|DisplayName=Outcomes Overview
+|DisplayName=Outcomes overview
 |TocName=Outcomes Overview
 |Context={{MINTYDOCSPRODUCT}}'s AI-powered outcome scoring service learns to predict your business outcomes using machine learning models that are unique to your business.

@@ Line 5: / Line 5: @@
 |Context={{MINTYDOCSPRODUCT}}'s AI-powered outcome scoring service learns to predict your business outcomes using machine learning models that are unique to your business.
 |ComingSoon=No
-|Platform=PureConnect, GenesysEngage-onpremises, GenesysCloud
+|Platform=PureConnect, GenesysEngage-cloud, GenesysCloud
 |Section={{Section
 |sectionHeading=Predicting outcome scores

ATC/Current/AdminGuide/Outcomes Overview - Revision history

Barbara.martin@genesys.com: Published

Barbara.martin@genesys.com: Published

Barbara.martin@genesys.com: Published

Barbara.martin@genesys.com: Published

Barbara.martin@genesys.com: Published

Barbara.martin@genesys.com: Published

Ed.jamer@genesys.com: Text replacement - "Altocloud's" to "Predictive Engagement's"

Harleyvibes: Published

Harleyvibes: Published

WikiSysop: Text replacement - "\|Platform=([^\|]*)GenesysEngage-onpremises([\|]*)" to "|Platform=$1GenesysEngage-cloud$2"

WikiSysop: Text replacement - "\|Platform=([^\|])GenesysEngage-onpremises([\|])" to "|Platform=$1GenesysEngage-cloud$2"