How to Predict Employee Churn using analytics

How to Know if an Employee is going to Quit before he does

Internal Spies! Yes this was one of the credible source of information. The spies are equipped with some extraordinary talent to grasp information from various sources / relate and then report / conclude (usually she/he leaves this to the boss)

With Lean management .. the human source of information is trimmed and put into better use (really!)

There are various data points that need to be captured to Analyse Employee Churn (We had already spoken a lot about Customer Churn in Customer Attrition Modeling (Analytics))

• Current and Previous Appraisals (this might be a major source)
• Current Pay ?(vis-à-vis Peers and Market)
• Grievances / Issues Reported
• Deviation in Time spent in Office
• Frequency (change) of swipe pattern changes
• Frequency (change) in access to internal information (Intranet etc.)
• Job sites / Social Sites visit patterns (some offices have banned them)
• Feedback from Peers
• Feedback from Supervisors

This list will go on .. Social Media data ...

Employee Churn is costly for the company. It takes months / years to replace the employee with a new one by adequate training etc

Thanks to data analysis, there are tools that help the companies to understand the employees, predict churn and help the manager in retaining the employee (that is the ultimate goal)

SumTotal's Talent Management System is one such system that helps the HR 

Utlimate Software (
www.ultimatesoftware.com) has a tool called Retention Predictor
(TM) for predicting customer Churn

 

Customer Attrition Modeling (Analytics)

Churn or Attrition Modeling

Customer is the key for any business. As Don Peppers once said - the only value that a company adds during the lifetime is the Customer.

Losing customer is losing business. But, how to avoid them moving away?

How to measure Customer Attrition

How to know if the customer is leaving . Probably if he/she is not shopping / visiting / interacting / subscribing like the past, it is an indication that he/she might leave.

Good datamining should measure the customer attrition easily. Predicitve models can be built on data for Customer attrition.

The key data points to be considered for Attrition modeling are the Recency, Latency, Average Spend per Visit, Average Time spent on Website, Duration of Visit (Website) etc

The predictive model then attaches a probabily score to each customer. The least churner to the most ..

How to retain the customer?

Starbucks has its own Loyalty program -My Starbucks Rewards™ . One in four customers use the Loyalty card, which provides a wealth of data for Starbucks analyitcs team. The customers are then analyzed and segmented. The customers who are more Loyal are ignored! Yes, the ones who are about to Churn are Rewarded with appropriate offers. The offers are based on the past purchase history!