Automated applicant tracking system accelerates the hiring process by enabling recruiters to handle multiple tasks efficiently and save time and money, increases productivity, enhances efficiency, and provides structure for staffing operation. Increasing demand for mobile applicant recruitment system and growing importance of machine learning and artificial intelligence in automating administrative tasks are expected to offer opportunities in applicant tracking system market during the forecast period.
However, integration issues of the application with current tools, high maintenance cost and lack of awareness about the system among the organizations are likely to hamper the applicant tracking system market growth.
Global applicant tracking system market has been segmented majorly on the basis of component, deployment, organization size, and vertical.
On the basis of Component, market is segmented into software and service. Services related to the applicant tracking system includes, maintenance of the software including optimization, error correction, and deletion of discarded features, a timely update of software, and software licensing. Enhancement of such characteristic features of the ATS system is likely to influence the market growth in the segment.
Based on deployment, market is segmented into cloud and on-premise. Introduction of application programming interface code in cloud computing allows recruiters to customize software widgets and dashboards as per their needs in applicant tracking system fueling the market grow in the segment.
By Organization Size, market is segmented into small and medium-sized enterprises and large enterprises. An applicant tracking system provides solutions to manage recruitment procedures in small and medium-sized businesses by allowing companies to filter CVs and identify suitable candidates easily and quickly. Recent technological advances in cloud revolution have made applicant tracking system convenient, flexible, and suitable for managing recruitment in SMEs.
Based on Vertical, market is bifurcated into BFSI, telecom & IT, retail, manufacturing, and other. Applicant tracking system helps to screen applicant personality through scorecards using a rating system for simplification. Additionally, applicant tracking software helps retail business to plan for seasonal shifts and determine the number of employees that should be hired by viewing historical data.
Geographically, global applicant tracking system market is divided as North America, Europe, Asia-Pacific, and LAMEA.
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North America is estimated to hold the largest share of the global application tracker system market. This is attributed to technological advancement, complete security while operating in physical, cloud, or virtual environment, integration of social media with applicant tracking system, and increase in the employers use of applicant tracker system for recruitment.
Europe is a prominent application tracking system market owing to increasing adoption of artificial intelligence and machine Learning in applicant tracking system.
Asia Pacific is the fastest growing regions in the global applicant tracking system market due to increasing need for efficient recruiting process and growing automation and digitization. Additionally, recruiters are seeking for cost-effective and time saving solution. China, India, and South Korea are transforming from agrarian to modern and industrialized societies, which is expected to drive the ATS market during the forecast period.
Whereas in LAMEA, concerned stakeholder including business owners and policymakers are looking for the skilled and sustainable workforce, which a positive factor for the growth of the applicant tracking system market. Moreover, The Inter-Ministerial Committee of Africa on Public Employment Programmes (PEP-IMC) is expected to create 6 million work opportunities by 2019 in Africa.
Some of the most prominent players in Applicant tracking system market are BambooHR LLC, Cornerstone OnDemand, IBM Corporation, iCIMS, Inc. and others.
Applicant Tracking System Market Segmentation TOC