CRM RECRUITMENT SOFTWARE USING MACHINE LEARNING

International Journal of Commerce and Management Studies , 2024

This chapter explores the integration of Customer Relationship Management (CRM) systems into talent management processes, highlighting their transformative potential in enhancing recruitment, onboarding, performance management, and employee retention. It provides a comprehensive overview of talent management, the evolution and core functions of CRM systems, and the benefits of leveraging CRM technology in HR operations. The chapter also outlines best practices for effective CRM implementation, identifies common challenges and solutions, and discusses emerging technological trends shaping the future of CRM in talent management. Through data-driven insights and practical recommendations, this chapter aims to demonstrate the strategic importance of CRM systems in optimizing human capital management and driving organizational success.

Businesses manage and assess client interactions and data using a collection of procedures, strategies, and tools referred to as customer relationship management (CRM). The dynamics of the commercial world have altered as a result of artificial intelligence. As AI solutions change the way marketers do business, customers are discovering it more and more impossible to ignore the significance of employing and investing in them. To successfully react to client inquiries and help increase customer loyalty, businesses have used AI-based CRM. This study employs machine-learning algorithms to analyze personalized and behavioral data from customers to provide companies with an upper hand through rising customer retention rates. When a company attempts to CRM, various stages occur, such as identifying customers depending on geography or financial benefit, retaining the customer, and attracting more customers. A CRM system that can analyze various types of customer data for the benefit of companies has been effectively constructed.

AI-enabled recruiting systems have evolved from nice to talk about to necessary to utilize. In this article, we outline the reasons underlying this development. First, as competitive advantages have shifted from tangible to intangible assets , human capital has transitioned from supporting cast to a starring role. Second, as digitalization has redesigned both the business and social landscapes, digital recruiting of human capital has moved from the periphery to center stage. Third, recent and near-future advances in AI-enabled recruiting have improved recruiting efficiency to the point that managers ignore them or procrastinate their utilization at their own peril. In addition to explaining the forces that have pushed AI-enabled recruiting systems from nice to necessary, we outline the key strategic steps managers need to take in order to capture its main benefits.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Finding qualified candidates for a vacant post can be difficult, notably if there are many applications. It can stifle team growth by making it difficult to hire the right person at the right time. "A Resume Recommendation System" has the potential to significantly simplify the time-consuming process of fair screening and shortlisting. It would certainly enhance candidate selection and decision-making. This system can handle a large number of resumes by first classifying them using multiple classifiers and then recommending them based on the job description. We offer research to improve data accuracy and completeness for resource matching by combining unstructured data sources and incorporating text mining algorithms. Our method identifies categories by extracting and learning new patterns from employee resumes. I.

Informatica, 2021

The task of finding the best job candidates among a set of applicants is both time and resource-consuming, especially when there are lots of applications. In this concern, the development of a decision support system represents a promising solution to support recruiters and facilitate their job. In this paper, we present an intelligent decision support system named I-Recruiter, that ranks applicants according to the semantic similarity between their resumes and job descriptions; the ranking process is based on machine learning and natural language processing techniques. I-Recruiter is composed of three sequentially connected blocks namely 1) Training block: which is responsible for training the model from a set of resumes, 2) Matching block: that is responsible for matching the resumes to the corresponding job description, and 3) Extracting block: that is responsible for extracting the top n ranked candidates. Experimental results for accuracy and performance showed that I-recruiter is capable of doing the job with high confidence and excellent performance. Povzetek: Predlagan je inteligentni sistem za podporo odločanju (IDSS) za pregledovanje in razvrščanje življenjepisov prosilcev na podlagi strojnega učenja in obdelave naravnih jezikov..

VII. INTERNATIONAL BATTALGAZİ SCIENTIFIC STUDIES CONGRESS, 2022

The selection of the most suitable personnel is very important for the effectiveness of human resources management in order for organizations to be successful in the globalizing competitive environment. Businesses and organizations are questioning the efficiency of their current recruitment processes more and more in order to attract the workforce that will add value to their personnel selection. For this reason, they want to contribute to efficiency and effectiveness by applying different recruitment methods. Personnel selection constitutes the process of finding suitable candidates for the qualifications required by the business. Different jobs and positions in businesses require different qualities, abilities, and personality traits. This situation has brought with it the necessity of employing suitable and qualified people in enterprises. In order to ensure harmony between the employee and the job, the suitability of both the individual's abilities and the personality of the individual to the job is necessary. Recently, with technological developments, machine learning algorithms have started to be seen very often in business life. With machine learning, it is possible to achieve both faster and more accurate results in personnel selection. In this study, information will be given about the methods of using machine learning in personnel selection.

Research Square (Research Square), 2023

Job recruitment is the process of picking a qualified candidate for a vacant position in an organization. Suitable job selection is a challenging task, especially for freshers. Every year, millions of students complete their graduation and come up with many options for choosing their jobs. This job selection procedure depends on various factors. Considering these issues, this research aims to build a model to predict whether a job is suitable or not suitable for a candidate according to their skills, experiences, and desires for the job. In this situation, Machine Learning approaches can be useful. Data were collected from 120 people currently appointed to a job in various fields. Distinct machine learning techniques are used to predict whether they are satisfied with their current job or not. We also find the other performance matrices and compare them with other algorithms to evaluate the performance of the best model. The results show that the random forest technique is the most effective method for forecasting appropriate job selection, with 92% accuracy and 8% error. Based on the findings, this research will become an effective tool for selecting a suitable job based on people's desires.

2020

Artificial intelligence and machine learning have recently made its way into human resources management. In this context, humans can train machines to identify a job candidate that would be a good match for the company among thousands of candidates, at least this the intention of this approach. While we can anticipate benefits of implementing such solutions there are many controversies surrounding this technology. In this research we are probing some key questions and principles that will shape the HR practice in the near future and how the workforce interacts with companies. Future workforce can expect being screened, interviewed and evaluated by a machine. Can such an approach be an honest sign that companies are trying to gain a better insight, reduce risk and even deliver valuable propositions of individuals themselves? Our research is showing that this process ought to be used with care and ethically so that the trust, and perception of fairness between job candidates and compa...

International Journal of Engineering and Management Sciences, 2021

This paper aim is to review the implementation of artificial intelligence (AI) in the Human Resources Management (HRM) recruitment processes. A systematic review was adopted in which academic papers, magazine articles as well as high rated websites with related fields were checked. The findings of this study should contribute to the general understanding of the impact of AI on the HRM recruitment process. It was impossible to track and cover all topics related to the subject. However, the research methodology used seems to be reasonable and acceptable as it covers a good number of articles which are related to the core subject area. The results and findings were almost clear that using AI is advantages in the area of recruitment as technology can serve best in this area. Moreover, time, efforts, and boring daily tasks are transformed to be computerized which makes a good space for humans to focus on more important subjects related to boosting performance and development. Acquiring a...

Journal of Intelligence Studies in Business

In this paper, we proposed a decision support tool for recruiters to improve their hiring decisions of suitable candidates for such a vacancy post. For this purpose, we proposed the use of the Artificial Neural Network (ANN) method from Artificial Intelligence (AI), thus we used real data from a semi-public recruitment agency in Morocco. However, for the adopted methodology, we used the process opted by the methods and techniques related to Data Mining. As a result, after completing the modelling process, we were able to obtain a model capable of predicting the decision to accept or reject such a candidate for such a vacancy. However, we obtained a model with an accuracy of 99% as well as with a very low error rate. However, our results show that Artificial Intelligence techniques can provide a better decision support tool for recruiters while minimising the cost and time of processing applications and maximising the accuracy of the decisions made.