Enhance Engagement and Retention with People Analytics

Enhance Engagement and Retention with People Analytics

Employee Group

An organization that provides top wages and benefits loses a great employee to a competitor for no apparent reason. We can’t stop employees from leaving unless we have a plan to make them stay.

“Retention is the single most important thing for growth” – Alex Shultz (VP Growth, Facebook)

What is the biggest and most intractable restraint to growth faced by companies doing business today? For many organizations, it’s the lack of appropriate talent. The reason: As more organizations have expanded their operations, the need for talent has skyrocketed. But there isn’t enough skilled labor to fill the demand. As a result, one risks losing the talent to other organizations. And with so many companies drawing on a limited talent pool, the competition is fierce.

Glassdoor’s statistical analysis reveals top three factors that matter most for employee retention.

  • Company culture
  • Employee salary
  • Stagnating for long periods of time in the same job

By examining the survey responses of more than 100,000 employees in numerous organizations, Gallup also discovered common themes among the reasons employees chose to remain with a company or to leave it. The reasons employees chose to stay with a company included the following:

  • I feel my job is important to the company.
  • My supervisor cares about me and gives me regular feedback.
  • I know my job expectations.
  • My opinions count.
  • I have opportunity to do my best work every day.
  • My career development is encouraged.

All the above reasons are part of what is often known is “engagement”. Organizations, or teams with high levels of employee engagement score high in most if not all of these. Higher engagement levels not only significantly affect employee retention, productivity and loyalty, but are also a key link to customer satisfaction, company reputation and overall stakeholder value.

OWEN Analytics, who is are providing AI-based people solutions have developed a robust and comprehensive methodology to measure and enhance retention. They run quick pulse surveys that are a combination of “ME” questions (My opinions count), and “WE” questions (I would like to appreciate the following individuals for helping me in my day-to-day work). Open feedback questions are interspersed as well to understand sentiment and key issues.

This helps understand engagement drivers not only from an individual employee perspective, but also from a team dynamics perspective. After all, our engagement with the organization is actually our engagement with the people in the organization – hence understanding those relationships is critical in better understanding attrition. This is the science of ONA (Organization Network Analysis). The example below illustrates how ONA can be used to understand team dynamics in a pharmaceutical sales organization.

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Clearly, the more cohesive teams have better performance and lower attrition.

Now that we have looked at engagement comprehensively, we need to look at what other factors drive employee turnover, as shown below:

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As per Deloitte, moving beyond the analysis of employee engagement and retention, analytics and AI have come together, giving companies a much more detailed view of management and operational issues to improve operational performance.

Exploring People Analytics

People Analytics, a discipline that started as a small technical group that analyzed engagement and retention, has now gone mainstream as per Deloitte. Organizations are redesigning their technical analytics groups to build out digitally powered enterprise analytics solutions.

OWEN Analytics specializes in helping organizations improve retention using AI driven techniques. As per OWEN, “Machine learning predictions can be sufficiently accurate and thus very effective in enabling targeted interventions for retaining high risk employees. However, using such techniques requires significant expertise in developing predictive models and experience in interpreting the outputs.

HR leaders and aspiring analysts needn’t be disheartened though. One can start with some very simple analyses using nothing more than basic Excel and develop reasonably good retention strategies” Read their blog here: Manage attrition using simple analytics.

OWEN uses a systematic retention approach to understand, predict and drive necessary actions.

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Predictive models are developed using various Machine Learning algorithms (e.g. Decision Trees, Random Forests, Logistic Regression, Support Vector Machines and Artificial Neural Networks) and best fit algorithm based on the accuracy and business context selected to predict flight risk.

Once the predictions are drivers are available, simple action planning templates to develop and track interventions are used to retain high potential employees.

Retention Challenge

The retention challenge is the result of increasing job mobility in the global knowledge economy where workers average six employers over the course of a career, coupled with the baby boomer retirement “brain drain” and a smaller generation of workers entering their prime working age during this time. It is occurring in all types of organizations across all management levels. This study empirically investigates whether the impact of an organization’s strategic orientation toward knowledge management, the learning culture it supports, and specific human resource practices impact knowledge worker retention and organization performance.

The Eight Elements of the High-Retention Organization as per SAS Institute

  • Clear Sense of Direction and Purpose
  • Caring Management
  • Flexible Benefits and Schedule Adapted to the Needs of the Individual
  • Open Communication
  • A Charged Work Environment
  • Performance Management
  • Recognition and Reward
  • Training and Development

As per Asia – Pacific Journal of Research, preventing turnover is a wise step to implement because it saves money, time, and effort. The company should spend a considerable effort and time to prevent turnover. It is better for an organization to keep experienced and productive employees than to hire new ones. It should invest in its employees through training programs, creating a good hiring process, and engrain them with strong organizational vision. To effectively solve turnover problems, every company needs to address the causes of the turnover. The causes of turnover might not be the same for every company. Below are the most common and affecting factors for preventing turnover.

It’s no more a secret that People Analytics plays a vital role for organizations in dealing with challenges of employee engagement and retention.

About the Authors:

Soumyasanto Sen — Blogger, Speaker and Evangelist in HR Technologies. Engaging with OWEN Analytics.

Professional Advisor, Consultant, Investor in HR Tech. Having 12+ years of experience focusing on Strategies, People Analytics, Cloud, UX, Security, Integration and Entrepreneurship in Digital HR Transformation.

Tej Mehta — Founder & CEO of OWEN Analytics.

Entrepreneur, advisor, student of social sciences. Founded i-Cube as an intersection of analytics and social sciences. Previously, as Vice President with Seabury Group, led strategy and operational transformation programs across several clients in the airline and aerospace industries. Aeronautical engineer, MBA from University of Southern California.


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How Tech Is Reshaping ‘Business As Usual’

Kyle Martin, Florida Polytechnic University

Written by Kyle Martin, Content Coordinator at Florida Polytechnic University. Specially for The HR Tech Weekly®.

Change is the only constant, and it’s only accelerating. Technology is taking us closer and closer to the futuristic worlds we once only imagined in science fiction, but questions remain. How is innovative technology changing the way we do business? How will it continue to change in years to come? Keep an eye on the following trends that will continue to reshape business as we know it and introduce brand new STEM careers:

The Machines: They’re Learning!

Artificial Intelligence is more than a buzzword; it’s real and it’s happening now. Systems that can learn and adapt are becoming part of numerous industries, from Salesforce’s analytics service to products like crystal for marketing. Intelligent systems apply learning from one user to improve all users’ experience. (This is rumored to also be coming in digital assistant Siri’s future). Natural language processing allows us to consult AI programs like we would another person, asking questions like “When should I post on Instagram for maximum engagement?” and receive a more accurate answer faster than ever before.

IoT Integration

The Internet of Things (IoT) is a revolution in connectivity; items and devices equipped with sensors and controllers can now communicate with one another. These smart machines assist with everything from household chores to improving supply chain management, and they relay pertinent information about user behavior and machine processes.

Improving supply chain efficiencies is a growing application of the IoT. The IoT allows supply chain managers to meticulously track products and make smarter decisions based on pre-programmed parameters. The IoT also produces real-time data, allowing consumers to know exactly when their packages will arrive, and supply chain managers an understanding of how well each facet of their supply chain is performing.

The IoT offers numerous benefits for managers and consumers alike. Managers can better understand system bottlenecks and prevent machine breakdown. Consumers have the satisfaction of knowing they can monitor their shipment in real-time. While the upfront cost of integrating intelligent machines might initially turn companies off, their ability to improve operations make them more economical in the long term.

New Levels of Co-Creation

One of the many benefits of the Internet is its capacity for widespread collaboration. Today, that collaboration is going one step further. Many companies are now leveraging co-creation, allowing consumers to contribute to the development of new products and services. This new level of cooperation between brands and their buyers empowers consumers to create the exact product or service they need. In return, businesses reduce the costs associated with assessing the current product market and determining what to produce next.

This theme of co-creation also includes companies hiring freelance specialists. Companies are no longer compromising the quality of their employees, and ultimately their business, because of geographic distance. With virtually no more borders or geographic limitations, this new trend is altering the way businesses hire and retain employees.

In the face of a rapidly evolving market, more job seekers are choosing technology-focused degree programs to keep their skills sharp and their knowledge up-to-date. Why? Because business owners and professionals must stay ahead of the changing technology tides in order to ensure continued success.

About the Author:

Kyle Martin brings 11 years of storytelling experience to the content coordinator position at Florida Polytechnic University. In this role, Martin develops original content showcasing the University experience as a way to attract new students and faculty. He also lends editorial direction to University departments launching new projects and campaigns.


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Machine Learning Becomes Mainstream: How to Increase Your Competitive Advantage

First there was big data – extremely large data sets that made it possible to use data analytics to reveal patterns and trends, allowing businesses to improve customer relations and production efficiency. Then came fast data analytics – the application of big data analytics in real-time to help solve issues with customer relations, security, and other challenges before they became problems. Now, with machine learning, the concepts of big data and fast data analytics can be used in combination with artificial intelligence (AI) to avoid these problems and challenges in the first place.

So what is machine learning, and how can it help your business? Machine learning is a subset of AI that lets computers “learn” without explicitly being programmed. Through machine learning, computers can develop the ability to learn through experience and search through data sets to detect patterns and trends. Instead of extracting that information for human comprehension and application, it will use it to adjust its own program actions.

What does that mean for your business? Machine learning can be used across industries, including but not limited to healthcare, automotive, financial services, cloud service providers, and more. With machine learning, professionals and businesses in these industries can get improved performance in a number of areas, including:

  • Image classification and detection
  • Fraud detection
  • Facial detection/recognition
  • Image recognition/tagging
  • Big data pattern detection
  • Network intrusion detection
  • Targeted ads
  • Gaming
  • Check processing
  • Computer server monitoring

In their raw data, large and small data sets hide numerous patterns and insights. Machine learning gives businesses, organizations, and institutions the ability to discover trends and patterns faster than ever before. Practical applications include:

  • Genome mapping
  • Enhanced automobile safety
  • Oil reserves exploration

Intel has worked relentlessly to develop libraries and reference architectures that not only enable machine learning but allow it to truly take flight and give businesses and organizations the competitive edge they need to succeed.
In fact, according to a recent study by Bain [1], companies that use machine learning and analytics are:

  • Twice as likely to make data-driven decisions.
  • Five times as likely to make decisions faster than competitors.
  • Three times as likely to have faster execution on those decisions.
  • Twice as likely to have top-quartile financial results.

Machine learning is giving businesses competitive advantages.

In other words, predictive data analytics and machine learning are becoming necessities for businesses that wish to succeed in today’s market. The right machine learning strategy can put your business ahead of the competition, reduce your TCO, and give you the edge your business needs to succeed.

Background on Predictive Analytics and Machine Learning

You already know that machine learning is essentially a form of data analytics, but where did it come from and how has it evolved to become what it is today? In the past couple of decades, we have seen a rapid expansion and evolution of information technology. In 1995, data storage cost around $1000/GB; by 2014 that cost had plummeted to $0.03/GB [2]. With access to larger and larger data sets, data scientists have made major advances in neural networks, which have led to better accuracy in modeling and analytics.

As we mentioned earlier, the combination of data and analytics opens up unique opportunities for businesses. Now that machine learning is entering the mainstream, the next step along the path is predictive analytics, which goes above and beyond previous analytics capabilities.

The Path to Predictive Analytics

With predictive analytics, companies can see more than just “what happened” or “what will happen in the future.”

Machine learning is a part of predictive analytics, and it is made up of deep learning and statistical/other machine learning. For deep learning, algorithms are applied that allow for multiple layers of learning more and more complex representations of data. For statistical/other machine learning, statistical algorithms and algorithms based on other techniques are applied to help machines estimate functions from learned examples.

Essentially, machine learning allows computers to train by building a mathematical model based on one or more data sets. Then those computers are scored when they may make predictions based on the available data. So when should you apply machine learning?

There are a number of times when applying machine learning can give you a competitive advantage. Some prominent examples include:

  • When there is no available human expertise on a subject. Recent navigation to Pluto relied on machine learning, as there was no human expertise on this course.
  • When humans cannot explain their abilities or expertise. How do you recognize someone’s voice? Speech recognition is a deep-seated skill, but there are so many factors in play that you cannot say why or how you recognize someone’s voice.
  • When solutions change over time. Early in a rush-hour commute,the drive is clear. An hour later, there’s a wreck, the freeway comes to a standstill, and side streets become more congested as well. The best route to getting to work on time changes by the minute.
  • When solutions vary from one case to another. Every medical case is different. Patients have allergies to medications, multiple symptoms, family histories of certain diseases, etc. Solutions must be found on an individual basis.

These are just a few of the uses that you’ll find across industries and institutions for machine learning. Not only is the demand for machine learning growing, though, but there is now an evolving ecosystem of software dedicated to furthering machine learning and giving businesses and organizations the benefits of instantaneous, predictive analytics.

An evolving ecosystem of machine learning software.*

In this ecosystem, Intel is the most widely deployed platform for the purposes of machine learning. Intel® Xeon® and Intel® Xeon Phi™ CPUs provide the most competitive and cost efficient performance for most machine learning frameworks.

Challenges to Adoption of Machine Learning

There are a few barriers to adoption of machine learning that businesses need to overcome to take advantage of predictive analytics. These include:

  • Understanding how much data is necessary
  • Adapting and using current data sets
  • Hiring data scientists to create the best machine learning strategy for your business
  • Understanding potential needs for new infrastructure vs. using your existing infrastructure.

With the right machine learning strategy, the barriers to adoption are actually fairly low. And, when you consider the reduced TCO and increased efficiency throughout your business, you can see how the transition can pay for itself in very little time. As well, Intel is dedicated to establishing a developer and data science community to exchange thought leadership ideas across disciplines of advanced analytics. Through these articles and information exchanges, we hope to further help businesses and organizations understand the power of predictive analytics and machine learning”.

What is your opinion and how do you apply data analytics and machine learning? Let us know what you think.

About the Authors:

Nidhi Chappell is the director of machine learning strategy at Intel Corporation. Connect with Nidhi on LinkedIn and Twitter to find out more about how machine learning can give your business a competitive edge.

 

Ronald van Loon is director at Adversitement. If you would like to read more from Ronald van Loon on the possibilities of Big Data please click ‘Follow’ and connect on LinkedIn and Twitter.

 

 

Intel, the Intel logo, Intel Xeon, and Intel Xeon Phi are trademarks of Intel Corporation in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others. © Intel Corporation.

Sources:
[1] http://www.bain.com/publications/articles/big_data_the_organizational_challenge.aspx
[2] http://www.mkomo.com/cost-per-gigabyte-update

Source: Machine Learning Becomes Mainstream: How to Increase Your Competitive Advantage