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 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.
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).
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.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.
How Genesys Predictive Engagement gathers outcome probability data
- 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.
Start training your models
Each of your machine learning models must be trained before it can make predictions. In order to start the training, you must:
- Create an outcome.
- Have users actively using your tracked website. To verify user activity, use Live Now.
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.
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.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.
- When you add a new outcome, your model training automatically accounts for it.
- Automated model training and predictions are performed on the original customer journey events, which are not anonymized and will generally contain PII.
In general, the longer your models run and the more data they evaluate, the better their predictions will be.
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.
Other factors can affect the accuracy of your model's performance, including:
- The total number of customer journeys recorded
- The frequency that an outcome occurs in the data
- The richness of events produced by the customerTipYou can exclude IP addresses to prevent internally generated events from influencing your models.