1. In an interview can we predict whether the candidate, if selected, will stay with us for at least 18 months? to be expressed as a probability for each candidate; Action: select those with .7 or higher probability

2. Will the engagement level of my employees increase because of the salary revision being effected? to be expressed as a table showing percentage of increase versus percentage level of engagement Action: to determine an appropriate percentage of increase from a given range of possibilities

3. What are the chances that the particular employee will be successful in the new role to which she is being promoted? to be expressed as a probability against given conditions Action: to decide on what support will ensure that the candidate is successful after promotion

4. How effective will a given L & D program be in increasing productivity? to be shown as a graph of percentage of participants versus productivity increase in their area Action: To tweak the content or andragogy of a program in order to make it more effective

5. Which of the given HR Strategies will be most effective in increasing the retention of employees? expressed as percentage increase in retention for each of the alternative strategies Action: To choose the optimum strategy

• In an interview can we predict whether the candidate, if selected, will stay with us for at least 18 months?

• to be expressed as a probability for each candidate

• Action: select those with .7 or higher probability Decision Making

Prediction

Issue or Problem Statement

A HR Analytics Primer• The core of HR Analytics is the "metric"

• Metrics can be understood as data that conveys meaning in a given context

• Metric is to be distinguished from measures or numbers

Example:

– employee turnover is 13.5% p.a.

– There is a 4 percent point rise in attrition rate on a year to year basis

– Inappropriate Leadership styles of select managers resulted in higher attrition of 3% on a comparable basis

Measure or Data

Metric

Analytic

A HR Analytics Primer

• Checklist, Dashboard, HRIS

– All of these are tools to collate and display information

• Hypothesis

• Variables: Dependent and Independent

• Statistical Models

– E.g. Regression, ANOVA

HR Analytics• Analytics is not so much about numbers, as it is to do with logic

and reasoning

• Analytics is different from Analysis, which is the equivalent of number crunching. Analytics uses analysis but then builds on it to understand the 'why' behind the figures and / or to predict decisions. Analytics is the methodology of logical analysis

• Analytics requires the use of carefully constructed metrics

• HR Analytics is data based; it uses past data to predict the future

• It is not about the quantity of data churned; it is about the logic used to link metrics to results

The Arunav HR Analytics Model

The HR Analytics Model appliedProblem Parameter: Minimum period of stay after joining, in months

ParametersW1 W3W2

Candidate Profile Hiring MethodologyInterviewer Acumen

Determinants

The HR Analytics Model applied

• As the next step, Relevant metrics for each of the determinants has

to be identified

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Example – we take the determinant “Career History to date”

Metrics

Average (Modal) stay per organisation as percentage of total career span Weight 60%

Number of jobs held with less than 24 months of service against total number of jobs held Weight 30%

Longest stay in a job as a ratio of average (arithmetic mean) stay in a job Weight 10%

• Convert above metrics to a single index to represent career history to

date using the weights given