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Analytics in Performance Management – 16th HR breakfast

Author: Jan Pavelka Published: 12.june 2017
HRbreakfast16_small2

On Tuesday 6th of June we organised HR breakfast again. This time the main topic was focused on analytical approach in Performance management.

You can get the atmosphere from the pictures in our library.

We started at 9 am with fully crowded room at ČSOB Inspirace building. After the welcome speech and few organizational instructions we asked each participant to introduce themselves. There were 30 attendees so it took some time :), but it was a good investment. We had the opportunity to see guests from various companies such as ŠKODA Auto, ČEZ, GoodCall, QED Group, ČSOB, DPP, ComAp or Accenture.

Then we immediately deep dived into the topic and defined 3 key issues of traditional Performance appraisal process:

1. it is too time and effort demanding so that it is done only once a year

2. connecting personal goals to business outcomes is difficult

3. how to define key drivers of personal performance

Few slides were dedicated to visualisations of the traditional approach and how to find value in charts, 9-boxes or risk matrices. And then we showed the characteristics of continuous feedback and discussed the example from GE. Anyway the most popular is the hybrid model combining the advantages of both options.

Connecting personal goals to business outcomes is quite often based on our assumptions. And those assumptions are often wrong. How to overcome it and directly link goals upwards was demonstrated by Jan on an example of Compliance Manager (typical backoffice role).

Passive metrics derrived from big data sources are so far rare but there are few showcases how to use them (e.g. Česká spořitelna and Google Suite, TEAMsCOM or Microsoft’s internal personal performance app.).

Team goals is another topic to be broadly used. They help to achieve better results in some areas rather than individual targets.

Advanced methods of analyses were described – multiple linear regression, stepwise regression or cluster analysis. We followed step-by-step 2 examples (financial sector and retail) having datasets in Excel files and regression analysis in RStudio. Now each attendee should be able to understand the results and describe what R-squared or p-value is and what are the key predictors of the models.

See the retail case study:

retail1  retail2

The conclusion is: Best results are achieved by women of age 30-34 years with higher number of weeks in the job role in supermarket with less years in formal education.

Several thoughts were dedicated to ethics in analytical studies – are we really allowed to use age, gender, education and other sensitive personal attributes in our models?

Last but not least – Jan’s favorite concept of Employee LifeTime Value. He showed the ways how to move the Benefit’s curve forward and backward and what are the consequences in financials.

Survival analysis for attrition helped several people to better understand what is really going on in their environments.

After approximately two hours we started the managed discussion and sharing what had inspired each other.

We enjoyed this lovely morning very much and we look forward to another HR morning in September! Be prepared for HR IS selection.

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