Settings: Creating and Updating Predictors

Predictors enable you to analyze various factors that might affect a specific metric. For example, you might check how the matching between customer and agent languages, ages, genders, and locations affect the NPS score.

To open the configuration menu, click the Settings gear icon, located on the right side of the top menu bar: .

Create a new predictor

To start creating a predictor:

1. Select Predictor from the left-hand navigation bar and then click New Predictor.
2. Name your predictor and select a dataset from the drop-down menu. When you select a dataset, the dataset date range appears.
3. Move the slider bars at either end of the date range to select the part of the dataset you want the predictor to evaluate.

Select a metric and the action type

To continue predictor configuration, perform the following steps:

1. Select the metric for this predictor. A predictor can be associated with only one metric.
2. Select the Action Type, which can be either Dataset generated or Agents. Actions refer to resources handling interactions—that is, agents.
   • Dataset generated: Action profile data is derived from the most up-to-date data captured in the dataset used to create the current predictor. Note that this dataset must be synchronized for the latest data to be available for the predictor.
   • Agents: Action profile data is taken from the designated Agent Profile schema. This is the typical production configuration for routing use cases.
3. Optional. Enter an expression to be used for computing the final score returned by the scoring engine. You can construct the expression using arithmetic operations, Python 2.7 built-in functions, and discovered fields.
   • To access the built-in functions, press the SHIFT+@ shortcut.

Select the action ID

To continue predictor configuration, perform the following step:

1. Select the Action ID from the drop-down menu. This is a unique employee identifier that is relevant for the type of metric you are evaluating.

   For example, if your metric involves routing, the Action ID might be the Agent ID. If your metric evaluates location as a factor in interaction success, you might specify the agent location as the Action ID.

Choose action features

Action Features are items that refer to the agent. All agent-related fields in your selected dataset appear in the drop-down list under Action Features.

1. Select an action feature from the drop-down list. The type associated with it in the dataset appears.
2. Continue until you have selected the action features you want to include in your predictor.
3. Optionally, you can create a new feature. A new feature must be based on existing features. When you create a new feature, you can add an expression, which enables you to perform some action on existing features and then use the result in your predictor.
   1. Click Add New Feature.
   2. Type a name for your new feature and then select a type (Boolean, list, string, and so on).
   3. Enter an expression. To construct your expression, you can use arithmetical operators, Python 2.7 built-in functions, and fields accessed by the @ hotkey.

Choose context features

Context Features are items that refer to the customer or that are available in interaction user data. They refer to aspects of the environment, broadly speaking, in which the interaction occurs. All customer- and userdata-related fields in your selected dataset appear in the drop-down list under Context Features.

1. Select a context feature from the drop-down list. The type associated with it in the dataset appears.
2. Continue until you have selected the context features you want to include in your predictor.
3. Optionally, you can create a new feature. A new feature must be based on existing features. When you create a new feature, you can add an expression, which enables you to perform some action on existing features and then use the result in your predictor.
   1. Click Add New Feature.
   2. Type a name for your new feature and then select a type (Boolean, list, string, and so on).
   3. Enter an expression. To construct your expression, you can use arithmetical operators, Python 2.7 built-in functions, and fields accessed by the @ hotkey.

Create and generate your new predictor

To finalize your predictor configuration, save and generate it:

1. Click Create to save your predictor settings. You should receive a success pop-up window indicating that the predictor has been created.
2. Before you can train and activate models, you must generate your predictor. Scroll up to the daterange display on your predictor configuration window, and then click Generate.

   Pop-up windows indicate the progress of the generate job.

Your new predictor now appears in the list of predictors, along with information about its status, such as the number of associated models, when it was last run, and its quality.

Update a predictor

You can edit your predictor unless you have created and activated one or more models based on it. In that case, Genesys recommends that you create a new predictor with the desired parameters.

You can change the predictor date range, purge generated data, and re-generate your predictor with a different date range at any time. However, already trained and activated models continue to use data from the old daterange.

1. Click Purge to change the date range in your dataset used to generate new models.

   Activated existing models continue to use the same date range.

2. Select the new date range, and then click Generate.

Pop-up windows indicate the progress of the purge and generate jobs.