Predictive Analytics for Recruitment vs Descriptive Analytics for Recruitment

As the competition heats up for top talent in the job market, companies are turning to data analysis and HRM software to get an edge in the recruitment process. However, with so many different types of analytics available, it can be challenging to determine which one will be the most helpful for your recruitment process. In this blog post, we compare predictive analytics with descriptive analytics when it comes to recruiting.

Descriptive Analytics

Descriptive analytics for recruitment involves reviewing and analyzing historical data to gain insights about past hiring processes. For instance, reviewing the number of applicants, candidate qualifications, pre-hire assessments and the time and cost of recruitment. It helps to identify trends, patterns, and anomalies in the recruitment process. The most common forms of descriptive analytics include bar charts, line graphs, pie charts, and frequency distributions.

One of the significant advantages of descriptive analytics is that it is easy to collect and analyze. For instance, HR data is analyzed through excel sheets, manual data-processing tools or database management systems. It provides visual insights, which help recruiters make more informed decisions when it comes to hiring. Some of the benefits of descriptive analytics for recruitment include:

• Identifying the bottlenecks in the recruitment process,
• Determining how long it takes to fill a specific position,
• Shortlisting best-fit candidates, and
• Tracking the performance, retention and turnover rate of employees.

Disadvantages

However, descriptive analytics alone may not provide a comprehensive view of the recruitment process as it only provides insights into past trends and not future process predictions. By looking only at past performance, it may not necessarily highlight the hiring patterns that work best.

Predictive Analytics

Predictive analytics for recruitment, on the other hand, involves using historical data to make predictions about future events. It identifies the relationships between various data points and investigates patterns in the data to anticipate future results. Machine learning models and other algorithms are trained and tested on historical data to generate accurate predictions.

Advantages

The most significant advantage of predictive analytics is that it helps to forecast hiring trends, predict candidate quality, and the workforce requirements for future expansion. Predictive analytics can provide early warnings of problems to come so that recruiters can implement remedies before these problems occur. It significantly reduces blind hiring, and companies can evaluate each candidate's suitably before hiring.

For instance, by analyzing the data collected from previous hires, predictive analytics can identify the ideal candidate, which helps recruiters to look for the candidate possessing those traits. It ensures that the right candidate is hired, which also significantly reduces turnover rates.

Disadvantages

However, adopting predictive analytics has disadvantages too. The primary disadvantage is its cost. Implementing this type of analytics requires investment in HRM software, data management systems, and skilled professionals to operate them. It could also lead to data bias or even discrimination towards certain candidates due to the use of algorithms or factors that act as indicators.

Conclusion

While both forms of analytics have their pros and cons, it's safe to say that a combination of both is ideal. Companies must use descriptive analytics as an initial stage to recognize recruitment hindrances and then use predictive analytics to forecast hiring trends, cut down costs and improve employee retention rates. With the right balance, recruiters can ensure that they are making informed decisions during the hiring process while also providing a better candidate experience.

References

• Coffman, J., & Guo, C. (n.d). Predictive Analytics Simplified Talent Acquisition. Two Point Conversions.
• Jason Averbook. (2019). HR Analytics for Cost Reduction and Operational Efficiency. LeapGen. Retrieved on May 22, 2022.
• Omer Molad. (2020). The Pros and Cons of Predictive Hiring. Vervoe. Retrieved on May 22, 2022.