Published on Sep 29,2015
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The purpose of analytics is not just to understand why you lost an employee but how you can prevent from losing one. Insight is the soul of Predictive Analytics. How organizations create and use data is changing the process of life, work or leisure.

This webinar blog focuses on how smarter organizations are adopting Predictive Analytics and rightly so! It also dwells on the following topics:

  • Business Intelligence VS Business Analytics.
  • Types of Analytics.
  • Why Predictive Analytics?
  • Domains where Predictive Analysis is creating magic.
  • Benefits of HR Analytics.
  • Real-time examples on HR Analytics.

Business Intelligence (BI) VS Business Analytics (BA):

BI is basically what is happening to your business. It is used for visibility.  Data Warehousing and visualization dashboards are enablers of BI. While BA is why it is happening; what is likely to happen in future. It is used for investigation, prediction and prescription. Data Analytics and Data Science are enablers of BA.

Types of Analytics:

There are three types of analytics:

  • Descriptive: Analytics that help you understand how things are going.
  • Predictive: Analytics that help you forecast future performance and results.
  • Prescriptive: Analytics that suggest a prescribed step or action.

Next generation analytics:

This extends beyond measuring and describing the past to predicting what is likely to happen and optimizing what should happen.

Analytics in future:

Analytics in future

What is Predictive Analytics?

Predictive Analytics is the analysis of data by using statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data.

Predictive analytics

Lifecycle of Predictive Analytics:

Let’s look at the image below to understand the lifecycle of Predictive Analytics:

Predictive analytics lifecycle

Advantages of Predictive Analytics:

Predictive Analysis helps analyze past business performance in order to gain insight that can drive business decisions and actions.

Advantages of Predictive Analytics

Why Predictive Analytics?

According to Forbes, the top objective for between two-thirds and three-quarters of executives is to develop the ability to model and predict behaviours to the point where individual decisions can be made in real time, based on the analysis at hand.

Major domains using Predictive Analytics:

Predictive Analytics is no longer the sole domain of data scientists. As predictive analytics software is getting easier to use, it’s no surprise the technology is being adopted across industries. Some of the major domains using Predictive Analytics are banking, e-commerce, HR, retail, transport, healthcare, IT industry among many others.

Let’s find out how Predictive Analytics can prevent employee attrition prevention:

What is churn or attrition?

Churn or attrition is when your customers reduce their usage or completely stop using your products or services. They leave your brand and might shop with your competitor. Churn prediction is a common application where the number of churners is typically small compared to the number of customers that stay.

Why an HR needs analytics?

Let’s take a look at the graphics below to understand how analytics can help an HR:

Why HR needs analytics

From predicting attrition among high performers to predicting how compensation values will pan out, an HR can benefit enormously from Predictive Analysis :

Why HR needs analytics

Stages in analytics:

The image below explains the different stages involved in analytics:

Stages in analytics

What is normally measured by an HR:

What is normally measured

What can be measured by Predictive Analysis?

Apart from the previous factors, an HR should pay attention to:

What can be measured

Common HR mistakes to avoid:

1. Keeping a metric live even when it has no clear business reason.

2. Relying on just a few metrics to evaluate employee performance. Smart employees can play with the system.

3. Insisting on 100% accurate data before an analysis is accepted — which amounts to never making a decision.

4. Assessing employees only on simple measures such as grades and test scores, which often fail to accurately predict success.

5. Using analytics to hire lower-level people but not when assessing senior management.

6. Analyzing HR efficiency metrics only, while failing to address the impact of talent management on business performance.

Predictive Analytics is a game changer:

Predictive Analysis can precisely identify the value of a 0.1% increase in employee engagement among employees at a particular store. For example, at Best Buy the value is more than $100,000 in the store’s annual operating income.

Many companies favor job candidates with stellar academic records from prestigious schools — but AT&T and Google have established through quantitative analysis that a demonstrated ability to take initiative is a far better predictor of high performance on the job.

Sprint has identified the factors that best foretell which employees will leave after a relatively short time. In three weeks, Oracle was able to predict which top performers were predicted to leave the organization and why. This information is now driving global policy changes in retaining key performers and has provided the approved business case to expand the scope to predicting high performer flight.

Advanced and Predictive Analysis application:

Problem statement:

An Indian MNC has a linear growth model. It wants to identify relationship between % revenue growth and % headcount growth. They have revenue and headcount details for the past 10 years.

Solution approach:

  • Identify the correlation co-efficient based on the type of data and plot a scatter plot.
  • Given that revenue growth is estimated at X% for the next year, we can predict headcount growth.

Problem statement:

An HR manager identifies 20 variables such as educational qualification, college, age, gender, nationality etc. that predicts the hiring effectiveness. He wants to identify mutually exclusive variables which affect hiring effectiveness.

Solution approach:

  • Using factor analysis , mutually exclusive factors can be identified.

Key to success:

  • Develop culture of data-driven decision-making.
  • Transparency of business and workforce information.
  • Empower line leaders, not just HR and L&D.
  • Analytics should be considered as a journey, not an end.

Here’s the PPT presentation:

Got a question for us? Mention them in the comment section and we will get back to you. 

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