Churn Prediction Software: 6-10 weeks Implementation

Polestar Solutions & Services India Pvt Ltd

Predict Employees Likely To Leave & Take Preventive Measures Before Its Too Late

Whether it is high performing employees/loyal customers, they undoubtedly have a pivotal role in contributing to the growth of any organization. But sometimes they just quit/drop out, at the time when you least expected it to happen. This could take a hit on the ongoing business projections and ultimately lead to revenue loss for the organizations.

Early Warning Signals using Azure ML libraries based predictive analytics to help clients (like you) to find the employees/customers at risk. At the same time, one can identify the underlying reasons for this churn via interactive Power BI dashboards. All this happens by identifying the hidden patterns in the historic data (fetched, trained and tested using Azure Data Factory) along with the help of some sophisticated predictive algorithms (KNN Classification, Logistics Regression, Naive-Bayes, XGBoost)

Being aware of the underlying parameters that could be responsible for attrition gives you the flexibility and time to act accordingly. Now, you can take prior preventive measures to avoid the churn before it's late, and help you save costs since the cost of retaining an existing employee/customer is far less than acquiring a new one.

๐—ข๐˜‚๐—ฟ ๐—ฐ๐—ผ๐—ป๐˜€๐˜‚๐—น๐˜๐—ถ๐—ป๐—ด ๐—ฝ๐—ฎ๐—ฐ๐—ธ๐—ฎ๐—ด๐—ฒ ๐—ผ๐—ณ๐—ณ๐—ฒ๐—ฟ๐˜€ ๐—ฎ ๐—ณ๐—ฟ๐—ฎ๐—บ๐—ฒ๐˜„๐—ผ๐—ฟ๐—ธ ๐—ณ๐—ผ๐—ฟ ๐—–๐—ถ๐˜๐—ถ๐˜‡๐—ฒ๐—ป ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐˜๐—ถ๐˜€๐˜๐˜€ ๐˜๐—ผ: โ—ฆ Upload the โ€˜Trainโ€™ and โ€˜Testโ€™ data โ—ฆ Define Parameters that contribute to the prediction โ—ฆ Choose Prediction Models as per accuracy โ—ฆ Alter parameters to identify Prescriptive measures โ—ฆ View Actual Results of the Churn

๐—ง๐—ฒ๐—ฐ๐—ต๐—ป๐—ผ๐—น๐—ผ๐—ด๐˜† ๐—ฆ๐˜๐—ฎ๐—ฐ๐—ธ:

  1. Dashboarding - Power BI
  2. Data upload/training - Azure Data Factory
  3. Predictive Modelling - Azure ML (could use Python libraries as well)

๐——๐—ฒ๐—น๐—ถ๐˜ƒ๐—ฒ๐—ฟ๐—ฎ๐—ฏ๐—น๐—ฒ๐˜€ ๐—ผ๐—ณ ๐˜๐—ต๐—ฒ ๐˜€๐—ผ๐—น๐˜‚๐˜๐—ถ๐—ผ๐—ป: Descriptive Analytics - Workforce Analytics: Level/Band spread, age spread, department spread Predictive Analytics - Prediction of Churn with reason and probability of Churn Prescriptive Analytics - What if analysis: simulation across reasons for churn to prevent churn

๐—–๐˜‚๐˜€๐˜๐—ผ๐—บ๐—ฒ๐—ฟ ๐—ฉ๐—ฎ๐—น๐˜‚๐—ฒ: Helps predict churn and take proactive measures to prevent it.