Y
yii
Can anyone share where I can find examples of where statistics can be applied in Human Resources?
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Rick Goodson
yii,
There are a number of uses for statistics in human resources such as analysis of training data, attendance, etc. Could you gives us a little more information. Why are you trying to do this? The fact that statistics can be used in human resources doesn't necessarily mean we should use it.
Regards,
Rick
There are a number of uses for statistics in human resources such as analysis of training data, attendance, etc. Could you gives us a little more information. Why are you trying to do this? The fact that statistics can be used in human resources doesn't necessarily mean we should use it.
Regards,
Rick
Y
yii
Hi,
We have lots of data such as employees resigning at every quarter and the reasons for leaving. We're just trying to see how statistics can help us in making this data more helpful to us in reducing the turnover rate.
Besides that, we're also encouraging participation of every department in QC projects using QC & statistical tools. That's why we're exploring in what area of HR we can successfully apply statistics in our work.
We have lots of data such as employees resigning at every quarter and the reasons for leaving. We're just trying to see how statistics can help us in making this data more helpful to us in reducing the turnover rate.
Besides that, we're also encouraging participation of every department in QC projects using QC & statistical tools. That's why we're exploring in what area of HR we can successfully apply statistics in our work.
A
Al Dyer
Statistics are a great tool but there is a drawback, stats only tell you what happened. The next step is to use the data to define reactions to the problem.
After systems are put in place as a reaction to the stats, then you can remeasure the stats and prove that your reactions had a positive affect.
Continuous Improvement!
After systems are put in place as a reaction to the stats, then you can remeasure the stats and prove that your reactions had a positive affect.
Continuous Improvement!
We look at percentages of personnel attending mandatory classes, is there one dept. that lags? Are certain contracted classes attended more often? Is it because of the trainer, or the subject? Do some types of training bring better attendance results, and is it because the subject is taught in a different or more interesting format? Anything that we can put some numbers to and hopefully make decisions on how to better present the information, or what types of classes people want to see. That is....beyond the manager saying "you'll be there if you want to keep your job"
i think that we are really dealing with statistics (Data) as input to a team Problem Solving activity. Suggest that you use some problem solving techniques to define the issue(s), try to contain the negative ones, hilite the positive ones, determine the root cause (Systemic and process) for both, come up with some corrective actions, implement them, and then monitor the data.
This type of activity may take some time. I do not believe there is a "fast track" solution here.
I think you need to determine what your successes are as well as failures. Use the techniques/processes that work in other areas that are problematic.
JMHO
This type of activity may take some time. I do not believe there is a "fast track" solution here.
I think you need to determine what your successes are as well as failures. Use the techniques/processes that work in other areas that are problematic.
JMHO
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Bill Pflanz
I have had a couple of opportunities to use data analysis on HR data. One example involved using employee attrition data to determine its impact on lost business. Since the business was felt to be dependent on relationship building, the hypothesis was that there would be a decline in business if an employee left.
I had three years of customer attrition data and survey data from lost customers on why they left. It was known that there were a number of reasons why customers left with one of them being the loss of their sales contact. The control chart of the client attrition showed a variable but stable process. The employee attrition was not a large number and occurred sporadically over the year. Other attempts had been made to do regression analysis of employee and customer attrition but it did not show any clear correllation. The analysis used national data.
Since I had access to regional and district data, I did some data segmentation to look at the problem at a more local area. With the local data it was easier to see a pattern of an employee loss and then a loss of customers a few months later. Since it took some time for the employee to re-establish themselves somewhere else, the shift in the impact of the customer loss made sense. Further studies indicated that if an employee left but stayed within the local marketing area, than it would take about 3 months to see the customer losses begin and within 9 months the employee had taken what customer they could. Originally HR had proposed enforcing a policy of making employees sign a confidentiality agreement but it was discarded since the sellers did not need to take any company files with them since they knew enough from servicing the customer that all they needed to remember were the primary names and numbers.
A couple of marketing areas did not fit the pattern. One district had experienced employee turnover but not significant customer losses. An interview with the manager indicated that they had instituted a program that involved immediate contact with the customers as soon as they found out an employee was leaving. The contact could include taking the exiting employee in with the new seller and have the person "introduce" the new person and "officially" turn the business over to ensure the change would be seamless to the customer. That made it more difficult for the ex-employee to steal away the customer. A recommendation was made to develop a similar approach nationwide.
Another marketing area did not have much employee turnover but had high customer attrition. Since I had data on who the business was being lost to, I was able to show that local market competition was a larger factor in their customer losses. The district manager started working with marketing to develop better methods to attract and retain customers.
By doing the data segmentation, it also explained why the original regression analysis did not work. Some marketing areas had developed solutions to reduce attrition caused by employee turnover while other districts had other causes that were more significant than employee turnover.
Bill Pflanz
I had three years of customer attrition data and survey data from lost customers on why they left. It was known that there were a number of reasons why customers left with one of them being the loss of their sales contact. The control chart of the client attrition showed a variable but stable process. The employee attrition was not a large number and occurred sporadically over the year. Other attempts had been made to do regression analysis of employee and customer attrition but it did not show any clear correllation. The analysis used national data.
Since I had access to regional and district data, I did some data segmentation to look at the problem at a more local area. With the local data it was easier to see a pattern of an employee loss and then a loss of customers a few months later. Since it took some time for the employee to re-establish themselves somewhere else, the shift in the impact of the customer loss made sense. Further studies indicated that if an employee left but stayed within the local marketing area, than it would take about 3 months to see the customer losses begin and within 9 months the employee had taken what customer they could. Originally HR had proposed enforcing a policy of making employees sign a confidentiality agreement but it was discarded since the sellers did not need to take any company files with them since they knew enough from servicing the customer that all they needed to remember were the primary names and numbers.
A couple of marketing areas did not fit the pattern. One district had experienced employee turnover but not significant customer losses. An interview with the manager indicated that they had instituted a program that involved immediate contact with the customers as soon as they found out an employee was leaving. The contact could include taking the exiting employee in with the new seller and have the person "introduce" the new person and "officially" turn the business over to ensure the change would be seamless to the customer. That made it more difficult for the ex-employee to steal away the customer. A recommendation was made to develop a similar approach nationwide.
Another marketing area did not have much employee turnover but had high customer attrition. Since I had data on who the business was being lost to, I was able to show that local market competition was a larger factor in their customer losses. The district manager started working with marketing to develop better methods to attract and retain customers.
By doing the data segmentation, it also explained why the original regression analysis did not work. Some marketing areas had developed solutions to reduce attrition caused by employee turnover while other districts had other causes that were more significant than employee turnover.
Bill Pflanz
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