Treasure Data Predictive Segments API
Using Treasure Data’s predictive scoring model, based on predictive segments, marketers can predict profile behavior such as who is likely to churn, purchase, click, or convert in the near future. A predictive model is a set of rules that makes it possible to predict an unmeasured value from other, known values. The form of the rules is suggested by reviewing the data collected. Training is then used to make some predictions. Predictive modeling uses statistics to predict outcomes. Predictive modeling is a typically used statistical technique to predict future behavior. Predictive modeling solutions analyze historical and current data and the generated model helps predict future outcomes. In predictive modeling, data is collected, a statistical model is formulated, predictions are made, and the model is validated (or revised) as additional data becomes available. For example, risk models can be created to combine member information in complex ways with demographic and lifestyle information from external sources to improve underwriting accuracy. Predictive models analyze past performance to assess how likely a customer is to exhibit a specific behavior in the future. This category also encompasses models that seek out subtle data patterns to answer questions about customer performance, such as fraud detection models. Predictive models often perform calculations during live transactions—for example, to evaluate the risk or opportunity of a given customer or transaction to guide a decision. Treasure Data’s predictive scoring model uses predictive segments to customize predictive scoring models for a particular segment.
GET
/audiences/{audienceId}/predictive_segments
Retrieve list of predictive scoring models
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POST
/audiences/{audienceId}/predictive_segments
Create predictive scoring model (legacy)
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GET
/audiences/{audienceId}/predictive_segments/{predictiveSegmentId}
Retrieve predictive scoring model
PATCH
/audiences/{audienceId}/predictive_segments/{predictiveSegmentId}
Update predictive scoring model (legacy)
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DELETE
/audiences/{audienceId}/predictive_segments/{predictiveSegmentId}
Delete predictive scoring model (legacy)
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GET
/audiences/{audienceId}/predictive_segments/{predictiveSegmentId}/executions
Retrieve predictive scoring model executions
GET
/audiences/{audienceId}/predictive_segments/guess_rule_async
Retrieve guessed rule
GET
/audiences/{audienceId}/predictive_segments/{predictiveSegmentId}/model/columns
Retrieve column list
GET
/audiences/{audienceId}/predictive_segments/{predictiveSegmentId}/model/features
Retrieve column list of features
GET
/audiences/{audienceId}/predictive_segments/{predictiveSegmentId}/score_histogram
Retrieve histogram
POST
/audiences/{audienceId}/predictive_segments/{predictiveSegmentId}/run
Train predictive scoring model (legacy)
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GET
/entities/segments/{id}/predictive_segments/guess_rule_async
Retrieve predictive scoring rules
POST
/entities/predictive_segments
Create predictive scoring model
GET
/entities/predictive_segments/{id}
Retrieve predictive scording model by ID
PATCH
/entities/predictive_segments/{id}
Update predictive scoring model
DELETE
/entities/predictive_segments/{id}
Delete predictive scoring model
POST
/entities/predictive_segments/{id}/run
Run predictive scoring model
GET
/entities/predictive_segments/{id}/executions
Retrieve executions of predictive scoring model
GET
/entities/predictive_segments/{id}/model/features
Retrieve features of predictive scoring model
GET
/entities/predictive_segments/{id}/model/columns
Retrieve columns of predictive scoring model
GET
/entities/predictive_segments/{id}/model/score
Retrieve scores of predictive scoring model