The Power of Predictive Analytics in Hiring

The Power of Predictive Analytics in Hiring

In an ever-increasing corporate landscape, making quality hires is critically important for organizations looking to improve their bottom line. The U.S. Department of Labor estimates that the cost of a bad hire is in excess of 30% of that employee’s first-year earnings.

To reduce the occurrence of bad hires, a growing number of businesses are turning to predictive analytics and big data. Using algorithms to analyze past and current data, these businesses more effectively can predict and adapt to future trends. From the sports world to big-box retailers, predictive analytics in hiring is shifting the paradigm of hiring decisions away from resumes and traditional metrics and towards data-driven analysis and advanced simulations.

The Moneyball Phenomenon

The 2001 Oakland Athletics baseball team enjoyed one of the most successful regular seasons in franchise history. Led by Tim Hudson, Mark Mulder, and Barry Zito, a trio of excellent starting pitchers, the team won 102 games. Their 63-18 record during the 2nd half of the season following the All-Star break today remains the best in MLB history.

Despite the momentum going into the post-season, the A’s faltered in a five-game series against the New York Yankees in the American League Divisional Series. The stage was then set for ...

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From Buzz to Brass Tacks: Data-backed Strategies to Improve Sales Performance

From Buzz to Brass Tacks: Data-backed Strategies to Improve Sales Performance

No sales team would pass up an opportunity to improve its overall productivity and profitability. Most also realize that big data and analytics are game changers. The question isn’t whether to harness the power of big data and analytics. It’s deciding what provides the most value to individual salespeople and the entire organization.

While the majority of today's organizations may recognize the potential of big data, most struggle with how to properly apply the skills of data scientists:

Most businesses that do hire data scientists often lacks an idea how to effectively utilize their skills. Most data scientists are stuck in maintenance mode organizing and collating data, rather than actually analyzing it.

Below are several practical applications of big data that can be used to boost sales performance in an organization.

Real-time Performance Visibility and Optimization

Companies that consistently post record sales quarter after quarter have learned how to close the gap between CRM and the real-time performance of sales teams. One reason why is because big data gives management the visibility to more completely see what’s going on with the entire sales team and their processes.

Sales platforms driven by big data give managers more information to work with when it comes to coaching their ...

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The Impact of Predictive Analytics on Horse Racing

The Impact of Predictive Analytics on Horse Racing

Predictive analytics and big data have been used in sports for well over a decade. Using advanced statistical analysis allows professional sports franchises to better identify talent and predict future player performance.

In 2002, the trend entered the mainstream when Billy Beane, the Oakland A's general manager, used it to create a cost-effective baseball roster. He used the data to predict that a player's on-base percentage was more important for team success than traditional stats such as their batting averages or home runs.

Beane’s strategy was successful and led to sustained success by the A’s, a small-market team with a payroll exponentially smaller than many of their competitors such as the New York Yankees. Since Beane’s “Moneyball” era, predictive analytics and big data have taken over the sports world.

Beyond baseball, another sport where such tools are especially prominent is horse racing. Data mining, statistics, modeling, machine learning, and a variety of other tools are used by owners, trainers, race course management, bookies, and bettors alike.

Equibase and the Origins of Big Data in Horse Racing

In 1990, the Jockey Club and the Thoroughbred racing associations collaborated to form Equibase as means to collect and share thoroughbred data. In the two and a half decades ...

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The Disruptive Impact of Big Data on Retail

The Disruptive Impact of Big Data on Retail

The retail shopping experience has changed dramatically since its inception in the early 1900s. Competition is fiercer than ever with a growing array of brick-and-mortar and online sellers for a  consumer to choose from/

Today’s retailers are learning to embrace the changes brought by technology rather than lose customers to the convenience of online shopping. Encouraged by a 2014 study that reported 90 percent of shoppers prefer to buy in person, brick-and-mortar retailers have increased their reliance on big data to ensure that they remain relevant. Here are a few of the many new technological changes that retailers have implemented over the past few years:

Touch Screens: Customers interested in a particular product can learn more about it with a quick touch of a screen. The kiosk programs are interactive based on a customer’s responses.
Mobile Point of Sale: Sales clerks can travel throughout the store to ring up a customer’s purchase so he or she does not have to walk to the counter or wait in a long line.
Beacons: This technology picks up signals from mobile devices to allow retailers to send shoppers a coupon, direct them to a specific location, and track shoppers’ movements to understand their buying habits.
Radio Frequency Identification ...

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