Outcomes overview

Genesys Predictive Engagement’s AI-powered outcome scoring service learns to predict your business outcomes using machine learning models that are unique to your business.

Predicting outcome scores

Genesys Predictive Engagement 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 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 visitor’s score for each outcome in real-time, and a visitor’s scores can change as they navigate your website.

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.
Note: Because our internal service handles the training and deployment of models, it is not possible to deploy customer-specific models with Genesys Predictive Engagement. Our team investigates and integrates new algorithms into the scoring service based on customer use cases.

How Genesys Predictive Engagement gathers outcome probability data

Genesys Predictive Engagement monitors all the ways that visitors arrive on and interact with your webpages. For example, if you are an e-commerce site, Genesys Predictive Engagement tracks how visitors navigate your website and place items in their shopping cart as they proceed to the checkout page.


Note:
  • 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.
For more information, see About the data we track.

Start training your models

Each of your machine learning models require training before they can make predictions.

To start the training:

  1. Create an outcome.
  2. Have visitors use your tracked website. To verify user activity, use Live Now.
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.
Note: 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 see outcome scores after your model is trained.

Ongoing training

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, agents can continue working as normal.

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.
Note:
  • 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 generally contain PII.

Improve predictions

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 for your model to be adequately trained to make reliable predictions.

Other factors can affect the accuracy of your model’s performance, including:

  • Total number of visitor journeys recorded
  • Frequency that an outcome occurs in the data
  • Richness of events that the visitor produces
    Tip
    You can exclude IP addresses to prevent events generated internally from influencing your models.