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Manage predictive lead and account scoring in ۶Ƶ Real-Time Customer Data Platform, B2B Edition

NOTE
Only users with Manage B2B AI permission can create, change, and delete score goals.

This tutorial walks you through the steps to manage score goals of the predictive lead and account scoring service. Score goals can be for either person profile or account profile

Create a new score

To create a new score, select the Services in the sidebar and select Create score.

plas-new-score

The Basic information screen appears, prompting you to select a profile type, enter a name, and an optional description. When finished, select Next.

plas-enter-basic-information

The Define your goal screen appears. Select the dropdown arrow and then select a goal type from the dropdown window that appears.

plas-select-a-goal

The Goal specifics dialogue opens. Select the dropdown arrow and then select goal field name from the dropdown window that appears.

plas-select-a-goal-field-name

The Goal conditions selection appears. Select the dropdown arrow and then select condition from the dropdown window that appears.

plas-goal-specifics-condition

The Goal value field appears. Next, configure your Goal specifics. Select the Enter Field Value panel and enter your goal value.

NOTE
Multiple goal values can be added.

plas-goal-specifics-field-value

To add additional fields, select Add field.

plas-goal-specifics-add-event

To configure the prediction timeframe, select the dropdown arrow and then select your timeframe of choice.

plas-prediction-timeframe

The selected merge policy determines how the field values of a person profile are selected. Using the dropdown arrow select your merge policy of choice and then select Finish.

The Scoring setup is complete dialogue appears confirming the new score has been created. Select OK.

plas-score-complete

NOTE
It can take up to 24 hours for each scoring process to complete.

You are returned to the Services tab where you can see the new score created in the list of scores.

plas-score-created

Select the score to view details and additional information about the last run details.

plas-score-additional-information

For more detailed information about the error codes that can be seen under the last run details, please refer to the section on leads AI pipeline error codes in this document.

Edit a score

To edit a score, select a score from the Services tab and select Edit from the additional details panel on the right side of the screen.

plas-edit-score

The Edit instance dialogue appears, where you can edit the description for the score. Make your changes and select Save.

plas-edit-save

NOTE
The score configuration cannot be changed as this will trigger model retraining and re-scoring. It is the equivalent of deleting the score and creating a new score. To edit the configuration of the score, you will need to clone this score or create a new score.

You are returned to the Services tab. Select the score to view the updated description details in the additional details panel on the right side of the screen.

Clone a score

To clone a score, select a score from the Services tab and select Clone from the additional details panel on the right side of the screen.

plas-clone-score

The Basic information screen appears. The profile type, name, and description is cloned from the original score. Amend these details and select Next.

plas-clone-basic-info

The Define your goal screen appears. Complete the goals section as you would when creating a new score and select Finish.

You are returned to the Services tab where you can see the newly cloned score in the list.

NOTE
The Define your goal section is not cloned from the original score.

Delete a score

To delete a score, select a score from the Services tab and select Delete from the additional details panel on the right side of the screen.

plas-delete-score

The Delete documentation confirmation dialog appears. Select Delete.

plas-delete-score-confirmation

NOTE
Deleting the score definition would also delete all the predicted scores on person profile or account profile, but not the field group created for the score definition. The field group will be left “orphaned” in the data model.

You are returned to the Services tab where you can no longer see the score in the list.

Leads AI pipeline error codes

Error code
Error message
401
ERROR 401. Leads AI pipeline stopped: not enough valid accounts for account scoring. Count of accounts: {}.
402
ERROR 402. Leads AI pipeline stopped: not enough valid contacts for contact scoring. Count of contacts: {}.
403
ERROR 403. Leads AI pipeline stopped: not enough activity volume for model training. Count of events: {}.
404
ERROR 404. Leads AI pipeline stopped: not enough conversions for model training. Count of conversions: {}.
405
ERROR 405. Leads AI pipeline stopped: activity too sparse for valid model training. Only {} percent of accounts has activity.
406
ERROR 406. Leads AI pipeline stopped: activity too sparse for valid model training. Only {} percent of contacts has activity.
407
ERROR 407. Leads AI pipeline stopped: scoring data activity types do not match with training data.
408
ERROR 408. Leads AI pipeline stopped: missing rate is too high for activity features. Missing rate: {}.
409
ERROR 409. Leads AI pipeline stopped: test auc is too low. Test auc: {}.
410
ERROR 410. Leads AI pipeline stopped: test auc is too low after parameter tuning. Test auc: {}.
411
ERROR 411. Leads AI pipeline stopped: training data does not have enough conversions to produce reliable model. Conversions: {}.
412
ERROR 412. Leads AI pipeline stopped: test data does not have any conversion to calculate AUC-ROC.
Warning/info code
Message
100
INFO 100. Leads AI quality check: the count of accounts is: {}.
101
INFO 101. Leads AI quality check: the count of contacts is: {}.
102
INFO 102. Leads AI quality check: the count of opportunities is: {}.
103
INFO 103. Leads AI quality check: testing auc is low. Start parameter tuning. Testing auc: {}.
200
WARNING 200. Leads AI quality check: the missing rate of firmographic features is: {}.
201
WARNING 201. Leads AI quality check: the missing rate of activity features is: {}.

Next steps

By following this tutorial, you can now successfully create and manage scores. See the following documents for more details:

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