Reference Check Software Market

The key drivers for reference check software market are the growing need to have a seamless reference checking processes and surge in demand for Integration of reference checking with applicant tracking systems. Additionally, with the technological advancements and growing adoption of disruptive technologies such as machine learning and artificial intelligence market is likely to pave a way for reference check software market during the forecast period. However, rising concerns related to unreliability and inaccuracies is one of the major restraining factors, which is expected to limit the market growth.

The conventional reference check process is time-consuming as it involves several steps. In-depth reference checks include talking to references to gain insights about the qualification, skills, and abilities of the prospective employee. The employer has to call each reference listed by the applicant, which incurs high costs and is time-consuming. This has led to the urgent need to simplify the reference checking process. Reference checking software helps save time by minimizing the number of phone calls required. It also reduces the cost and time associated with checking reference data. Reference checking software allows the recruiter to send the candidate a survey with questions related to the job, which helps in gaining insights into the candidate’s skill level and abilities. Such features of reference checking software eliminate the need for traditional in-depth reference checks and simplify the overall process.

Machine learning technology is changing the recruiting process by making it easier and faster for recruiters to identify the appropriate applicants for even the most complicated roles. During hiring drives, recruiters face extra workload. Reference check software can utilize machine learning capabilities to automate pre-employment reference checks and candidate scoring. The use of machine learning in reference check software helps recruiters gather references, rank and score candidates, reduce costs by hiring the right talent from the various applicants, and offer a positive hiring experience to candidates, references, and employers. Machine learning also enables the seamless integration of reference checking with applicant tracking systems. Certain market players have begun to recognize the benefits of using machine learning for reference checks; for instance, Jointl uses machine learning and AI capabilities in its reference check software which is a highly scalable solution that helps save time and stress, avoid bad hiring, and make better hiring decisions.

Reference check software offers a wide array of advantages in terms of cost and time saving; however, the accuracy and reliability of the software depend largely on online surveys, based on which the software generates a report. This generated report provides a competency-based scoring on the basis of which recruiters identify potential candidates. The survey takes five to 10 minutes to complete and is not comprehensive enough to determine the eligibility of the candidate. Moreover, the software is keyword-sensitive, and applicants could take advantage of this by using certain keywords to be considered a good fit for the position. Keyword sensitivity could also impede suitable applicants by disqualifying them based on the absence of keywords in their resumes. These factors are expected to hamper the growth of the global reference check software market during the forecast period.