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Engagement and Churn Score

Engagement and Churn Score

Algorithm
Engagement and Churn Score
Use Cases
Determine users at risk of churn, star users and regular users

Prerequisites

Before you reach this stage, ensure you’ve:
  1. Created a schema
  1. Integrated code to process your data and installed Begin’s Python SDK
  1. Created a project with the objective “Engagement & Churn Score”
Learning usually takes about 30 minutes. Once complete, your machine learning algorithm is ready to be used on your mobile device, browser, connected vehicles, or server applications.

Procedure

In order to get engagement scores, follow the initialization step:
import beginai as bg
worker = bg.BeginWorker(app_id=APP_ID, license_key=LICENSE_KEY)
 
To use your trained algorithm, input the following code:
worker.engagement_score(project_uuid, target_object_uuid, start_date, end_date)
 
This will return a dictionary with data in the following format:
  • avg_bottom_10_slope: Average bottom 10 slope for the given project’s target object in the date range
  • avg_top_10_slope: Average top 10 slope for the given project’s target object in the date range
  • ranked_slope: String ranked slope score, as a percentage
  • slope: Slope score as a floating point number
  • classification: Slowly Disengaging, Dropping Off, Rising Star or Stable based on the behaviour analysis
 
In case the provided target object id doesn’t exist, the string NOT_FOUND will be returned