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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, locations, customer's reason for making contact, and agent skills affect the NPS score. In addition to simple Predictors, you can also combine them to create composite Predictors that analyze multiple metrics.

Viewing Data on This Window

  • To open the configuration menu, click the Settings gear icon, located on the right side of the top menu bar: GPMsettingsGear.png.
  • A right-hand toggle navigation menu opens a tree view of all Datasets associated with your Account, with the Predictors and Models configured for each. To open or close this navigation menu, click the GPRDatPredModNavIcon.png icon. Note that composite predictors do not appear in this tree view.
  • You must reload the page to view updates made using the Predictive Routing API, such as appending data to a Dataset, creating, updating, or deleting a Predictor, or creating, updating, or deleting a Model.
Important
  • The Tooltips, which appear when you hover over any ? icon, contain helpful explanatory information about the associated fields.

Creating a Predictor

The following series of procedures takes you through the steps required to create and configure a new Predictor.

Procedure: Begin to create a new Predictor

Purpose: To create a Predictor, which specifies a metric you plan to optimize and the agent and customer features you have found to have the strongest effect on that metric.

Prerequisites

  • You might want to run a Feature Analysis report before creating a Predictor. The Feature Analysis report can analyze which features in your dataset have the strongest impact on the value of a specific metric.

Steps

To start creating a Predictor:

  1. Select Predictor from the left-hand navigation bar and then click Add Predictor.
  2. Name your Predictor and select a Dataset from the drop-down menu. When you select a Dataset, the Dataset date range appears.
    Note: Predictor names can consist only of alphanumeric characters, and must start with a letter or underscore.
  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.

PrMPredictDaterange.png

Procedure: Select a metric and the Agent and Customer Identifiers


Steps

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 Agent identifier, which can be either Dataset generated or Agents.
    • Dataset generated: Agent 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: Agent profile data is taken from the Agent Profile schema. This is the typical production configuration.
  3. Select the Customer identifier, which can be either Customers or None.
    • None: Customer and interaction data is gathered from attached data.
    • Customers: Customer data is taken from both the Customer Profile schema and attached data. If both sources include data for a specific field, the value in the attached data is used. This is the typical production configuration.
  4. KPI Type: This field is reserved for future use. All users should accept the default type, which is Service.
  5. 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 3 built-in functions, and discovered fields. To access the built-in functions, press the SHIFT+@ shortcut.
    Examples of ways to use this field:
    • If URS has different scales for scoring, you can use this field to scale the returned score appropriately.
    • You might need to translate the result returned from URS to correct the sort order. For instance, if customer_talk_duration is a target metric, agents with lower scores are actually better. So you might enter the score expression 1 / p_score, which produces an outcome such that higher predicted values are lower actual scores.
    • For an in-depth discussion of how GPR handles metrics, see Understanding Score Expressions.

PrMPredictorMetric.png

Procedure: Select the Agent ID and Actions Cutoff


Steps

To continue Predictor configuration, perform the following steps:

  1. Select the Agent ID from the drop-down menu. This is a unique employee identifier that is relevant for the type of metric you are evaluating.
    If Agents (the Agent Profile schema) has been selected as Agent Identifier, the Agent ID you select must be the same field as the ID_FIELD in the Agent Profile.
  2. Select the maximum number of best scores that will be returned when you make a scoring request to the API.
    Important
    The maximum number of best scores is only relevant to the API, not to scoring requests sent using the Predictive Routing application.

PrMPredictorAction.png

Procedure: Choose Agent Features


Steps

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

  1. Select an Agent Feature from the drop-down list. The type associated with it in the Dataset appears.
  2. Continue until you have selected the Agent 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 the type of value this feature returns: Boolean (the returned value is an either/or value, such as true/false), list (a list of the possible returned values), string, and so on.
    3. If you need to use a value from a different source than that initially added to the schema, toggle the Override control to on (toggle turns from gray to blue) for those features that should be updated at the time of scoring and then add an expression that tells GPR what value to use. For example, if you configured your Agent Profile schema with the CSAT captured in the AVG_CSAT column, but at runtime you want the value to be computed from other columns in the schema, turn on the Override control and enter the desired expression in the Expression field.
      To construct your expression, you can use arithmetical operators, Python 3 built-in functions, and fields accessed by the following shortcuts:
      • SHIFT+@ - for Dataset fields
      • SHIFT+# - for Profile fields

PrMPredictorActFeatures.png

Procedure: Choose Customer Features


Steps

Customer 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 Customer Features.

  1. Select a Customer Feature from the drop-down list. The type associated with it in the Dataset appears.
  2. Continue until you have selected the customer 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 the type of value this feature returns: Boolean (the returned value is an either/or value, such as true/false), list (a list of the possible returned values), string, and so on.
    3. If you need to use a value from a different source than that initially added to the schema, toggle the Override control to on (toggle turns from gray to blue) for those features that should be updated at the time of scoring and then add an expression that tells GPR what value to use. For example, if you configured your Customer Profile schema with the customer value status captured in the CUST_VALUE column, but at runtime you want the value to be computed from other columns in the schema, turn on the Override control and enter the desired expression in the Expression field.
      To construct your expression, you can use arithmetical operators, Python 3 built-in functions, and fields accessed by the following shortcuts:
      • SHIFT+@ - for Dataset fields
      • SHIFT+# - for Profile fields

PrMPredictorContext.png

Procedure: Create and generate your new Predictor


Steps

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.

PredictorCreateGenerate.png

Viewing and Updating Predictors

After you have created Predictors, use the following procedures to view and maintain them. It is important to ensure that your Predictors and Models stay up-to-date, so that they continue to address your most compelling business needs.

Procedure: View your Predictors


Steps

When you navigate to Settings > Predictors, the window shows a table listing all your existing Predictors. For each, the table shows what the following information:

  • Name - The name given to the Predictor when it was created.
  • Type - Whether the metric requires classification (binary) or regression analysis.
  • Models - The number of Models created for the Predictor.
  • Rows - The number of rows in the Dataset used to create the Predictor.
  • Quality - The quality value displayed for the Predictor (AUC for classification metrics and RMSE for regression metrics) is the average of the results for each trained Model associated with that Predictor. Both active and inactive Models are included in the average, as long as they are trained.
  • Metric - The metric that the Predictor is built to optimize.
  • Last Run - The last time the Predictor was trained.

From this list you can do the following:

  • Edit your Predictor, if you have not yet created and activated any Models created for it. See Update a Predictor (below) for details.
  • Create or edit Models for the predictor. Click the name of a Predictor to open it, and then click Models to access the Model functionality.
  • Delete a Predictor. Select the check box next to a Predictor name, and then click the trash can icon.

PredictorListActions.png

Procedure: View the Predictor Information Pane


Steps

When you view the configuration data for a Predictor, the right-hand toggle Information pane icon becomes active. Click this button to view the following information:

  • The Predictor ID, which you can use to make API requests affecting the Predictor.
  • The Dataset upon which the Predictor is based.
  • The start and end dates for the data on which the Predictor is based.
  • The Model or Models created for this Predictor.
  • The Predictor status.

PredictorInfoPane.png

Procedure: Update a Predictor


Steps

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.

PurgePredictorData.png

Composite Predictors

You can create composite Predictors by combining two or more simple Predictors—that is, standard Predictors such as those described in this topic so far. The composite is defined using an arithmetical function that works on the set of target metrics used in the selected simple Predictors. This enables you to score agents based on multiple metrics rather than just one.

The composite Predictor takes the scoring outcomes for each of the included Predictors and then applies the arithmetical expression you specify to those results. This composite result is then sent back to the routing strategy to be used when determining the best match between waiting interactions and available agents.

Example

Assume you have three agents with the following individual scores:

Agent Predicted NPS Score Predicted CSAT Score
Agent 1 8 7
Agent 2 8 8
Agent 3 6 5

If you just use NPS to select the best agent, Agent 1 and Agent 2 appear equally likely to handle the interaction well. But if you want to maximize your CSAT as well, the additional parameter acts as a differentiator. The aggregated score for Agent 2 (16) is higher than that for Agent 1 (15).

Important
  • If a Predictor you include has multiple active Models associated with it, the Model used is determined in the same way as it normally is when you have multiple Models.
  • The Lift Estimation report, which evaluates potential lift based on a specific Model, does not support composite Predictors. If you need to create a complex metric for use in the Lift Estimation report, see Creating Complex Metrics in Genesys Predictive Routing for guidance.

The advantage of starting with separate Predictors over pre-computing the final metric in a common Dataset is to maximize data usage. For example, you probably have much more data for talk duration than for NPS, which requires customers to take a survey. It is also easier to create the desired calculation when you combine individual, already-created metrics.

  • Example 1:
    You might create a composite Predictor out of three simple ones by combining them. Target metrics contain only numeric or Boolean values, so that you can combine the outcome using basic arithmetic operations. Assuming that the target metrics for the respective Predictors are the following:
    • Predictor1 - NPS
    • Predictor2 - CSAT
    • Predictor3 - TALK_DURATION
    You can write an expression such as the following:
    (Predictor1 + Predictor2) / Predictor3
    In this example, the agent score is (predicted NPS + predicted CSAT) / predicted talk duration. That means agents with higher NPS and CSAT get higher scores if call duration is lower. Agents with high NPS and CSAT scores that also have high talk durations are scored lower. As a result, you can take into account several aspects of agent performance when scoring.
  • Example 2:
    You might use different simple Predictors depending on a context variable passed in a scoring request. You can create a composite Predictor that would use one simple Predictor for customers in a high-value category (value >= 50) and a different simple Predictor for lower-value customers (value <= 50). The idea is to offload this logic from the strategy into a Predictor to reduce the need to modify your strategy. To create such a Predictor, define the following expression for your composite Predictor:
    • int(value > 50) * Predictor1 + int(value <= 50) * Predictor2
    The logic here is the following: the int(value > 50) function converts the Boolean value from true/false to 1/0. Whichever Predictor is multiplied by 0 is ignored and the remaining Predictor is used for routing.
Important
When creating a scoring request for use with a composite Predictor, the request must contain at least one Customer Feature that is common for all the simple Predictors included in the composite Predictor.

Procedure: Create a composite Predictor


Steps

  1. Navigate to the Predictors list page in Settings.
  2. Select the check boxes next to at least two simple predictors. The Create Composite button appears. You must keep in mind the following constraints when selecting simple Predictors:
    • All of the simple Predictors that are going to be used in a composite Predictor should have the Agent Identifier field set to Agents.
    • The Agent ID field should be identical in all of the simple Predictors, and should be the same as that in the Agent Profile schema. For example, if in the Agent Profile schema the Agent ID is the Agent_Id field, the Agent ID in each Predictor must also be set to the Agent_Id field.
  3. Click Create Composite, which opens the Composite Predictor page, pre-populated with the Predictors you selected.
  4. Fill out the Expression field. This field is mandatory for composite Predictors.
    You can use a Suggestion menu triggered by the following keyboard shortcuts to configure your expression: [Shift + @] (to open context fields) or [Shift + #] (to open a list of functions). The menu lists only the applicable fields for a given composite Predictor, thus reducing the chance of error when composing an expression.

Your new composite Predictor now appears in the list of predictors. Note that information about Predictor status, such as the number of associated Models, when it was last run, and its quality, are available only for simple Predictors.

CreateCompositePredictor.png

This page was last modified on July 12, 2019, at 12:21.

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