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Leveraging machine-learning techniques is crucial to efficiently and effectively understand customer data. Marketers who use Treasure Data do not need to be familiar with machine-learning and data science.


Our predictive scoring enables you to enjoy machine-learning capability in your day-to-day activities with no technical or theoretical expertise. Marketers can predict profile behavior such as who is likely to churn, purchase, click, or convert in the near future.


You can apply Treasure Data’s machine-learning feature, Predictive Scoring, to your customer or account segment. Use Predictive Scoring to assess behaviors and predict future behavior.

Requirements

Our predictive scoring requires that you define segments:

Understanding Predictive Profile Components


  • Population is a set of profiles—what you've probably already defined as a master segment or as a segment. The set is used to build the predictive model; the characteristics of customers or accounts are extracted and patterns are learned, and the predictions is based on the patterns learned from the segment.

  • Positive samples indicate your definition of “conversion”; for example, if you want a customer churn prediction, then this segment is a set of customers who were already churned.

  • Scoring target specifies profiles you are interested in; the prediction is done only for the profiles in this segment.

Depending on the configuration of the segments, you can solve a wide variety of “wants” that predict “promising” profiles. As long as a subset of profiles can be represented as a batch segment in a master segment, Treasure Data can predict customers or accounts who are likely (or, if needed, unlikely) to be in the segment.

As a consequence of scoring, you can see the distribution of scores and corresponding metrics on the dashboard. Additionally, activation for “likely” profiles can be seamlessly implemented from the dashboard:


Scroll down the page to see:

Use cases

This section summarizes possible use cases for the Predictive Scoring feature.

  • Predict future customer churn

  • Predict future conversion in US

  • Predict customers' purchase in 2018 based on 2017’s history


Predict Future Customer Churn


  • Population: All customers in a master segment

  • Positive samples: Already churned customers

  • Scoring target: Not churned customers


Predict Future Conversion in US


  • Population: People in the US

  • Positive samples: Converted customers

  • Scoring target: Not converted yet customers in US


Predict Customers' Purchase in 2018 Based on 2017’s History


  • Population: People who accessed in 2017

  • Positive samples: Purchase log in 2017

  • Scoring target: People who accessed in 2018

Related Links

Learn more about predictive customer scoring at:

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