So I am neck deep in my studies for my Masters in Business Analytics at the Stern School of Business at NYU (meanwhile running a major analytics company with some big assignments LOL). Classes started on Monday.
How about this for a starting point >> Andrew, write up a brief on how you would address 20% attrition in renewals for a mobile provider in the Mid Atlantic region of a company using data analytics?
That was fun. It was like coming up with a plan for addressing retention for employees. Of course, the assignment had the context that getting new customers was difficult, so we had to think about how to use data to identify retention strategies. AWESOMENESS.
The overtones for talent acquisition analytics is very thick here. So, what did I propose? After talking about setting up the right data lake configuration for analysis (not monitoring), I suggested the following data sources as for analysis. My goal? Turnover a list of customers that we going to be released to marketing for produce and service engagement so they don’t leave.
Can you tell I am already having fun?
Step 1: Financial Analysis. How much is the 20 percent actually costing us? Are there customers that we SHOULD let go because they don’t refer, don’t engage, or don’t produce earnings or profitability. What does THAT sound like heads of recruiting?? Don’t just measure losing people, measure LOSSES.
Step 2: Determine who is likely to leave anyway from operations. In this assignment, its based on contract. For employment, we should be looking at anniversaries, both joining and promoting. Then look at when bonuses are paid out, and when projects are going to be accomplished. These are the most likely times for employees to jump out.
Step 3: Review Key Account longevity and conditions. In the assignment, I noted how key accounts or groups needed to be evaluated and aggregated in the analysis. If we can mitigate many losses by correcting a problem with one group, we sidestep lots of losses. Same goes for retention. Are there entire groups we can address for retention, so we dont have to increase our recruiting efforts in general.
Step 4: Review engagement data. Here I outlined the importance of CRM tracking, tracking opening of emails, social engagement blah blah blah. Lots of clicks or lots of no clicks. That makes sense for retention. Which of your employees are actual advocates, follow you on social, spread the work and more. Who shows up at events and who does not? What is interesting is how I also outlined cross referencing the employee engagement data for customer analysis. I would do the same here. If there are engagement problems in certain ranks, that means you might have employment vacancies in those areas soon.
Step 5: Review Customer Service Data. Who logged complaints or open tickets? Its amazing how HR does a poor job of doing that (or remembers selectively). Who are the people engaging HR and asking for help? If they are not receiving it, you might be recruiting for their replacement when they leave.
Step 6: Review User Adoption of Services – in the assignment, I talk about all the mobile services, but in the case of retention for employees the data analysis is similar. Who are the people NOT enrolled in your programs? They maybe the greatest flight risk. Your total rewards program can be hard to replace if it is very good – but if it is weak, then its harder to leave. Meanwhile, if someone is not in your benefits program, not contributing to 401k and hardly takes vacation, then how attached are they to your business? Probably not enough to make it painful to leave.
Step 7: Review Platform Adoption – for class I talk about how mobile users might be leveraging services on phone , tablet, and multiple devices and operating systems, and how logins and usage on all helps understand engagement and entanglement. The same can be said for your own teams with how they entangle themselves in your social programs, culture, special project teams, and recognition programs.
All in all, a good first assignment and I thought the overtones to our world of employment were great. Right now my classes are in Big Data, Decision Modeling, Digital Marketing Analytics, Dealing with Data, Foundations of Statistics Using R, and the Introduction to Business Analytics.