Churn Prediction Software: 6-10 weeks Implementation

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.

𝗢𝘂𝗿 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗶𝗻𝗴 𝗽𝗮𝗰𝗸𝗮𝗴𝗲 𝗼𝗳𝗳𝗲𝗿𝘀 𝗮 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗖𝗶𝘁𝗶𝘇𝗲𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 𝘁𝗼: ◦ Upload the ‘Train’ and ‘Test’ data ◦ Define Parameters that contribute to the prediction ◦ Choose Prediction Models as per accuracy ◦ Alter parameters to identify Prescriptive measures ◦ View Actual Results of the Churn

𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗦𝘁𝗮𝗰𝗸:

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

𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝗮𝗯𝗹𝗲𝘀 𝗼𝗳 𝘁𝗵𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻: 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

𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗩𝗮𝗹𝘂𝗲: Helps predict churn and take proactive measures to prevent it.