Two Day Seminar: Metrics & Predictive Analytics in Human Resources-Reducing Risk and Improving Productivity (Burlingame CA, United States - November 2-3, 2017)

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Aug 08, 2017, 16:00 ET

DUBLIN, August 8, 2017 /PRNewswire/ --

The "Metrics & Predictive Analytics in Human Resources-Reducing Risk and Improving Productivity" conference has been added to Research and Markets' offering.

This seminar is designed for participants who wish to learn and apply what is taught on day 1 to their own data. On day 2 of the seminar, participants can create metrics that are business-focused and easy to create. Consequently, participants are encouraged to collect internal data and bring their laptops to the seminar. We will be using excel to create and analyze the data, and to create metrics. In addition, the instructor will demonstrate how to use statistical software to identify "drivers", selected variables drive or predict impact on various metrics.

Learning Objectives:

Difference between analytics and metrics
How to pick high-powered metrics
What comes after establishing metrics
How analytics can be used to significantly help the business
Case studies to demonstrate real life projects
How to develop an analytics model
Data needed and how to use Excel to develop analytic formulas
Using analytics in selection process, the biggest opportunity in the HR area

Areas Covered:

Availability and use of HR data
HR Metrics
Hypothesis generation
Pre-identifying the desired outcome
What to do with Predictive Analytics findings
Selecting the right interventions
Mixing art and science

Agenda:

Day 01 (8:30 AM - 5:00 PM)

08.30 AM - 08.45 AM: Meet & Greet

8:45 AM - 9:00 AM: : If you could see the future, how would you use this gift in your job?

9:00 - 10:00: Introduction to HR Analytics.

What is HR Analytics
How is it different from marketing, risk and other analytics
How is Analytics different from metrics

10:00 - 11:00: Case Studies on HR Analytics

Capital One
Micron
RE/MAX
Starbucks

11:00- 12:00 Designing the Analytics Models

Assessing the availability and veracity of data
What is your outcome variable (Y Variable)
How to select the Y Variable
How to determine the X variables
Examples of X and Y variables

12:00 - 1:00: Lunch

1:00 - 1:30: Collecting the data

Output data versus subjective data
Developing a powerful and validated survey
Open-ended (narrative) questions
Engagement Survey, bi-product of the process
The need for calibration

1:30 - 2:00: Processing the data

Excel
SPSS
New Software
How to select the data processing tool

2:00 - 4:00: Let's talk (Small Groups)

What is your desired Y Variable?
Why did you select it?
What is the business case to for this project?
How solid is your Y variable data
What is your hypothesis on what drives the Y variable?
Do you have the X variable data?

4:00 - 4:30: Group Debrief

4:30 - 5:00: Summary, Reflections and Agenda for Tomorrow

Day 02 (8:30 AM - 5:00 PM)

8:30 - 8:45: Meet and Greet

8:45 - 9:30: Basic statistics and analytical tools

9:30 - 11:30: Small Group exercise - developing powerful analytic model for your project

Describe your proposed project with partner(s)
Elicit feedback from partners on the project. (How solid is the data? How available is it? What is your desired outcome variable (Y variable). How will you generate the X variables? How will you test them?
Select 3 or 4 projects among all of them and assign a team for each project.
Begin the analyses using excel and/or other statistical software.
Identify preliminary findings

11:30 - 12:00: Present to other participants

12:00 - 1:00: Lunch

1:00 - 2:00 Completing the Diagnostic Phase: What is next?

Discuss presentations. What was done well? Was the data there? Good Y variable? Did they miss any possible X variables? How was their analysis and use of analytic software?

2:00 - 2:30: New trends in HR Analytics

2:30 - 3:30: Other uses for HR Analytics

Training
Motivating others
Manpower Planning

3:30 - 5:00: Selecting the right intervention after completing HR Analytics

For more information about this conference visit https://www.researchandmarkets.com/research/kmh7qv/metrics_and

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