Win Probability

Administrators can manually specify attributes to drive the machine learning for Win Probability analytics. However, administrators may not have insight to which attributes are the real predictor of a win. Beginning in Release 18B, administrators can utilize machine learning to automatically choose the attributes to be used in predicting Win Probability. In addition to the pre-existing behavior (now labeled 'Custom'), administrators may select a "Choose For Me" option, which uses numeric and non-numeric driver attributes in order to address use cases where other, non-pricing-related factors are key determinants of the likelihood that a quote will become a closed sale. For example, win probability may vary significantly for different industries or geographies independent of the discounts offered.

Administrators can also select attributes to be excluded from the automatic driver selection (i.e. Excluded Driver Attributes) in order to avoid circular logic or address other business requirements. For example, the Commerce Step for a Transaction may indicate that the Transaction has already been converted to a sales order. As a result, this attribute will likely be identified as a driver attribute, even though it is of no value in predicting the outcome of an 'in progress' transaction. Administrators can select numeric, non-numeric and the following system attributes for Excluded Driver Attributes: System Number of Step Transitions, System Transaction Author Currency Preference, System Transaction Author Language Preference, System Transaction Update Count, and System Transaction Author User Id.

After an administrator creates a Win Probability analytic, the Choose For Me option will display as the default selection for the Driver Selection menu. When Choose For Me is selected, the option to select a Primary Driver Attribute is not available, but administrators can add and select Excluded Driver Attributes.

After the Status Attribute and the Won Value are selected, the administrator initiates a training session to acquire driver attributes derived from machine learning.

Perform the following steps to use machine learning to select driver attributes for a Win Probability analytic:

Perform the following steps to define a Win Probability analytic:

1. Navigate to the Admin Home page.

2. Click Process Definition in the Commerce and Documents section.

   The Processes page opens.

3. Select Analytics from the Navigation menu, next to the applicable process, then click List.

   The Analytics List page opens.

4. Click Create Analytic.

   The Analytic wizard opens.

5. Scroll through the filmstrip to the Win Probability option, then click Create.

   

   The Analytics Definition Editor opens.

   

6. Define the following variables:
   1. Name: Enter the name for the analytic.
   2. Variable Name: Enter the variable name for the analytic.
   3. Description: (optional) Enter the description for the analytic.

   4. Document: Select the Commerce main document or sub-document (e.g. Transaction or Transaction Line).

      • When the main document is selected, only main documents attributes and actions are available for analytic settings.
      • When the sub-document is selected main document and sub-document attributes and actions are available for analytic settings.
   5. Title: Enter the title for the chart.

7. Click Next.

   The "with Optimization" setting is not selected.
   This setting is only selected for Price Optimization analytics.

   

8. Select Choose for Me from the Driver Selection drop-down.

   The options for custom driver selection are displayed.

   

9. Click the Train tab.

   The Training History is displayed.

10. Click the Train button.

    After the training session is complete, the training session is listed in the Training History on the Analytics Definition Editor page under the Train tab.

    Use the Refresh button to update the Training History results.

    Note: A minimum of thirty Transactions are required to complete a training session.

    

11. Click the training session link.

    The Training Details page opens.

    The Training Details page displays the Test, Accuracy, Train Accuracy, Won Records, Lost Records, Training Time, and Drivers Selected.

12. Evaluate the training session results.

13. Click Back to return to the Training History.

    Re-initiate training if the results are unacceptable.

14. Click the Build tab.

    The Driver Selection, Status Attribute, Won Value, and Lost Value attributes are now read-only.

    Note: To edit the Driver Selection, Status Attribute, Won Value, and Lost Value attributes following a successful training session, you can delete that training session and then editing is re-enabled for the attributes.

15. Define Filters (optional).

    

    1. Date Filter: Select the Commerce date attribute used to filter historical transactions for the analysis. System attributes such as 'System Last Modified Date' may also be selected.

    2. Duration: Select the time period prior to today's date to include in the analysis. The data selection duration is cumulative. Administrators may select from options including Last 30 days, Last 90 days, Last 6 months, Last 12 months, and All Transactions.

    3. Advanced Filters: Click Add Row to add additional filters.
       Advanced Filters provide a means to select the transactions to be included in the optimization. Only those transactions meeting the filter criteria will be included in the machine learning to build the predictive model. Attributes mapped to a Data Column will display in the drop-down list of available attribute names, the Data Column label is not displayed. The administrator can perform the following:

       • Select one or more Query Attributes from Data Columns,

       • Select an Operator to be applied, and

       • Select or define the Specified Value:

         • Select a value from a drop-down list (Menu or Boolean attributes),

         • Select a date (Date attributes),

         • Enter a value (Text or Numeric attributes) to be used in the query filter.

       Operators relevant to the query data type display to enable administrators easy and accurate editing of filters. Attributes without values can also be filtered out using the 'Is Empty' and 'Is Not Empty' operators, providing administrators with flexibility to assure that only clean data is included in their predictive model.

       Note: Administrators can create an unlimited number of filters for each Optimization analytic.

16. Define Output Attribute Settings.

    

    1. Trigger Action - Select the Commerce Modify-type Action that will be used to update the Win Probability Output attributes.

       Note: The displayed Output attributes are only updated when the Action is invoked, whereas the analytic charts are updated real-time as data on Transaction are edited.

    2. Win Probability Value Prediction Attribute - Select the Commerce Attribute to be populated with the predicted Win Probability.

17. Click Update.

The Training Details page is displayed when an administrator navigates to the Analytics Definition Editor Training tab and clicks a training session link. This page displays the Test Accuracy, Train Accuracy, the number of Won Records, the number of Lost Records (if the Lost attribute value is defined), and Training Time. If the "Choose For Me" option has been selected for this Win Probability analytic, the Drivers Selected by machine learning will also display as shown below:

Preview Win Probability Analytics Generated Using the "Choose For Me" Option

The Win Probability Analytic Preview allows administrators to preview how driver attributes impact the analytic before displaying the results in a Commerce Transaction. Beginning in Oracle CPQ Release 18B, the Win Probability Analytic Preview will also provide a preview for analytics that are generated using the drivers selected using the "Choose For Me" option.

Perform the following steps to build a Win Probability analytic using "Custom" Driver Selection:

Perform the following steps to define a Win Probability analytic:

1. Navigate to the Admin Home page.

2. Click Process Definition in the Commerce and Documents section.

   The Processes page opens.

3. Select Analytics from the Navigation menu, next to the applicable process, then click List.

   The Analytics List page opens.

4. Click Create Analytic.

   The Analytic wizard opens.

5. Scroll through the filmstrip to the Win Probability option, then click Create.

   

   The Analytics Definition Editor opens.

   

6. Define the following variables:
   1. Name: Enter the name for the analytic.
   2. Variable Name: Enter the variable name for the analytic.
   3. Description: (optional) Enter the description for the analytic.

   4. Document: Select the Commerce main document or sub-document (e.g. Transaction or Transaction Line).

      • When the main document is selected, only main documents attributes and actions are available for analytic settings.
      • When the sub-document is selected main document and sub-document attributes and actions are available for analytic settings.
   5. Title: Enter the title for the chart.

7. Click Next.

   The "with Optimization" setting is not selected.
   This setting is only selected for Price Optimization analytics.

   

8. Select Custom from the Driver Selection drop-down.

   The options for custom driver selection are displayed.

   

9. Define the following Win Probability Settings:

   1. *Primary Driver Attribute - Select the attribute name for the primary input value from the Transaction. Any numeric-type Main Document Data Column attribute may be selected from the drop-down (example: ‘Discount %’).

   2. Additional Driver Attributes Select the attribute name(s) for the secondary input value(s) from the Transaction. One or more numeric or text-type Main Document Data Column attributes may be selected (example: ‘Customer Segment’).
   3. *Status Attribute - Select the name of the Main Document attribute that indicates if the Transaction was won or lost
   4. *Won Value - Select the Status Attribute value that indicates that a Transaction was won. The drop-down displays all possible values for the Status Attribute.
   5. Lost Value - Select the Status Attribute value that indicates that a Transaction was lost. The drop-down displays all possible values for the Status Attribute.

10. Define Filters, (if desired).

    

    1. Date Filter: Select the Commerce date attribute used to filter historical transactions for the analysis. System attributes such as 'System Last Modified Date' may also be selected.

    2. Duration: Select the time period prior to today's date to include in the analysis. The data selection duration is cumulative. Administrators may select from options including Last 30 days, Last 90 days, Last 6 months, Last 12 months, and All Transactions.

    3. Advanced Filters: Click Add Row to add additional filters.
       Advanced Filters provide a means to select the transactions to be included in the optimization. Only those transactions meeting the filter criteria will be included in the machine learning to build the predictive model. Attributes mapped to a Data Column will display in the drop-down list of available attribute names, the Data Column label is not displayed. The administrator can perform the following:

       • Select one or more Query Attributes from Data Columns,

       • Select an Operator to be applied, and

       • Select or define the Specified Value:

         • Select a value from a drop-down list (Menu or Boolean attributes),

         • Select a date (Date attributes),

         • Enter a value (Text or Numeric attributes) to be used in the query filter.

       Operators relevant to the query data type display to enable administrators easy and accurate editing of filters. Attributes without values can also be filtered out using the 'Is Empty' and 'Is Not Empty' operators, providing administrators with flexibility to assure that only clean data is included in their predictive model.

       Note: Administrators can create an unlimited number of filters for each Optimization analytic.

11. Define Output Attribute Settings.

    

    1. Trigger Action - Select the Commerce Modify-type Action that will be used to update the Win Probability Output attributes.

       Note: The displayed Output attributes are only updated when the Action is invoked, whereas the analytic charts are updated real-time as data on Transaction are edited.

    2. Win Probability Value Prediction Attribute - Select the Commerce Attribute to be populated with the predicted Win Probability.

12. Click Update.

    Proceed to Initiate Win Probability Machine Learning to train the machine learning system.

Perform the following steps to train a Win Probability analytic:

1. Navigate to the Admin Home page.

2. Click Process Definition in the Commerce and Documents section.

   The Processes page opens.

3. Select Analytics from the Navigation menu, next to the applicable process, then click List.

   The Analytics List page opens.

4. Select the applicable Win Probability analytic link.

   The Analytics Definition Editor page opens.

   

5. Click the Train tab.

   The Training History is displayed.

   

6. Click Train to initiate a training session.

   When the training session is finished, the results will be displayed in the Training History.
   
   The Refresh button may be used to refresh the training results.

7. Evaluate the training session results. Re-initiate training if the results are unacceptable.

Administrators can now preview a Win Probability analytic before displaying it in a Commerce Transaction.

Perform the following steps to preview a Win Probability analytic:

1. Navigate to the Admin Home page.

2. Click Process Definition in the Commerce and Documents section.

   The Processes page opens.

3. Select Analytics from the Navigation menu, next to the applicable process, then click List.

   The Analytics List page opens.

4. Click the Name link for the appropriate analytic.

   The Analytics Definition Editor page opens.

   

5. Click on the Train tab.

   The Train tab displays the Training History.

   

6. Click on the link for a completed session to view the Training Details for that machine learning session.

   The Training Details page opens.

   

7. Enter a BS_ID (the identifier for an individual Commerce Transaction) for a Transaction, and then click Preview.

   Administrators may interact with the Win Probability chart by hovering for additional details, just as in the sales user UI.

Administrators enable the Win Probability chart by creating a Commerce HTML attribute and selecting a Win Probability analytic as the default value on the attribute Default tab.

Once enabled, the associated HTML attribute displays as a Related Attribute in the Analytics Definition Editor page.

Administrators can copy Scatter Plot, Win Probability, Price Optimization, or Price Guidance analytics. This functionality allows administrators to quickly define and preview alternative analytics to evaluate the results with differing inputs.

Complete the following steps to copy an analytic:

1. Navigate to the Admin Home page.

2. Under Commerce and Documents, click Process.

   The Processes page opens.

3. Select Analytics from the Navigation menu, next to the applicable process.

4. Click List.

   The Analytics List page opens.

5. Select the checkbox next to the applicable analytic.

   

6. Click Copy.

   The Analytics Definition Editor page opens.

7. Update the analytic properties as desired.
8. Initiate machine learning to train the new analytic.
9. Preview the new analytic to analyze the results.

The Migration Center is the user interface where all migration takes place within Oracle CPQ. Deal Management Analytics are displayed in the Analytics folder under the applicable Commerce process. Deal Management Analytics are migrated when the parent Commerce process is migrated.