- What is Talent Acquisition Data Analytics?
- Use Cases of Talent Acquisition Data Analytics
- Technologies Used for Talent Acquisition Analytics
- Metrics Used for Talent Acquisition Analytics
- Examples of Talent Acquisition Analytics
- Data driven Talent Acquisition
- Predictive Analytics in Talent Acquisition
- HR Analytics in Talent Management
- Talent Acquisition Data Analyst
- Final Words
- Topics in Data Analytics
Inarguably, talent acquisition (TA) has changed into a highly competitive ecosystem over the decade. The data-driven and technically advanced recruitment module has led to more quests for the right source and hiring top talent. Thus, the talent acquisition data analytics came into the picture for leveraging deeper insights into the critical operation and outcome process. According to the study of BBC Research, the organization can witness a growth of up to 20% per year by putting AI-driven software to recruitment use. Recently, the talent acquisition analytics journey is advancing from descriptive to predictive and prescriptive to suit the long-term industrial needs and requirements.
What is Talent Acquisition Data Analytics?
In general, talent acquisition refers to the process of filling the skill gaps with the right talent at the right time. Unlike, the traditional hiring process, the modern-day talent acquisition process focuses on strategic goals by developing a data-driven recruitment process. Thus, talent acquisition data analytics is the refined use of data to execute hiring strategies with the help of accurate predictions based on past events and future possibilities. Reportedly, the study of LinkedIn’s Global Recruiting Trends 2018, has confirmed that 50% of the hiring professionals deploy big data analytics in the recruitment process. Furthermore, it has resulted in overall employee retention by 56%.
With the adoption of more advanced technologies like data analytics, repositories, etc, the leading companies do not merely rely on an applicant tracking system (ATS) for seeking reports on transactional data . The integrated approach has impacted the TA processes and practices, which includes data extraction from the following reliable sources to make required predictions.
- Skill Inventories to identify the surplus and shortfall.
- Social networks to turn passive candidates into potential candidates.
- Candidate Relationship Management (CRM) to derive talent pool insights.
- Considering Core HR, learning, talent, performance management, and compensation systems for demographic information.
- Regular operational and financial systems to estimate cost involvement.
- Focusing on employee engagement surveys, evaluate the existing candidate’s feedback.
Thus, collection, analysis and prediction drawn from Big Data is a highly comprehensive one and serves all the stages of talent acquisition proficiently.
Use Cases of Talent Acquisition Data Analytics
The talent acquisition analytics helps to improve employee and employer relationships and attain organizational objectives.
|Predict and Prevent Employee Turnover||On average employee turnover costs the business 150% of an employee’s salary as it involves heavy time and money involvement. The happy fact is, out of all the employee turnover, 75% are purely preventable. Yes, the prevention is followed by prediction and prediction is a result of predictive data analytics. Talent acquisition analytics observes employee traits, behavior, activities and uses them to identify the dark spots and fix them at the earliest. As a result, organizations are easily able to maximize retention and minimize onboarding.|
|Workforce Forecasting||Instead of relying on heavy data, reports, and research papers to make workforce studies, the process can be simplified with the deployment of AI-based technology like predictive analytics. Here, the predictive analytics reflect on workforce factors, financial satisfaction, growth opportunities, etc, and ensures filling the skill gaps if any.|
|Talent Sourcing||Over the year acquisition cost has increased by leap and bounds, which make the hiring process more competitive. Thus, organizations have to rely on agile methods to capture and onboard the right methods. The data analytics fill the process with a talent pipeline and get the organization a rightly diversified fit. An AI-based talent sourcing algorithm also helps to understand the retention and turnover factors of an employee to the greatest extent.|
|Predicting Performance||Using people analytics, companies draw attention to employees’ behavior patterns, develop a persona for a similar set of employees, and predict their future performance. According to the study of Frank L. Schmidt, job experience merely allows 16% accuracy in predicting the candidate’s performance whereas the combination of basic personality and experience with cognitive ability helps to predict future performance by 78%.|
|Employee Benefits||Talent analytics is a tool that helps to see unseen, understand unsaid, and predict undiscovered. Basically, with the detailed behavioral study, it helps to focus on key potential areas which benefit the employees and ensure their workplace satisfaction.|
Technologies Used for Talent Acquisition Analytics
|Purpose||Tools and Technologies|
|Candidate Sourcing||The sourcing helps to identify passive candidates and drive them towards great job possibilities. |
|Candidate Screening||The tools based on NLP, deep learning and machine learning match, and rank candidates automatically to ease out the recruitment process. |
|Candidate Assessment||The pre-assessment tools help to assess candidate personality and skills to fit the professional and cultural environments. |
|Interview Process||The computer intelligence bases interview process becomes worthwhile with the following prominent platforms. |
|Offer and Onboarding Process||These few prominent platforms make the Onboarding process smooth and seamless. |
Metrics Used for Talent Acquisition Analytics
The talent acquisition metrics serve as the standard measure for meeting the recruitment goals and bridging the skill gaps in the organization. Thus, there are few essential data-driven metrics which talent acquisition leaders are advised to track in the forthcoming years.
|Time and Steps to Fill Vacancy||Ideally, there is no standard time duration to fill the vacant positions in the company as it depends on several factors which vary for each company differently. Yet the Talent Acquisition Benchmarking Report by SHRM says, “An average time to fill a vacancy is around 36 days.” Therefore, it is important to improve the time to fill required posts because it provides immense benefits such as enhanced productivity, lesser time, and cost wastage owing to delay. Also, it is important to identify shortfalls in the recruitment process to avoid hiring cycle delays.|
|Quality of Hire||The healthy contribution of the new talent in the organization leads to long-term success because these candidates undergo several cohesive screening and interviewing processes. As a result, the talent acquisition metric helps to track and improve employee’s performance, internal career progression and meet management’s expectations.|
|Offer Acceptance Rate||It refers to determining the total number of candidates who accepted the job out of all the offered ones. If the offer acceptance rate of the organization is less than an average, it indicates the need for in-depth analysis to spot demographic trends. The offer acceptance metric can be improved by properly assessing the job description, reevaluating salaries, clear depiction of healthy corporate culture, focusing on job flexibility, and considering the competitor’s approach.|
|Cost Per Hire and Vacancy||The talent acquisition process involves several diversified activities such as travel, infrastructure, agency, relocation. Thus, it is crucial to determine the aggregate cost and find out the cost per hire thereupon. Alongside, there exists another term cost per vacancy where the company’s revenue per employee based on the working days is derived. Evidently, the longer the process takes, the more the resource involvement is giving rise to the close correlation between time to fill and cost per hire.|
Thus, these essential talent acquisition metrics have a direct impact on the entire organizational operations and understanding the performance of talent acquisition methodology.
Examples of Talent Acquisition Analytics
Talent acquisition is the core process of every business entity, but every industry has different acquisition norms to bridge the skill gaps. Thus, there is a varied set of predictive talent acquisition analytics suitable for all business types. Let’s focus on a few standard tools with their real-time applications here.
|Example||How Talent Analytics Helped?|
|HIrevue – Interview Process||An AI-powered platform, HIrevue is the best-known AI-powered platform and is used by 700+ companies across the world. Some prominent companies on the list are Unilever, Vodafone, Oracle, and PWC. Here the proprietary algorithm helps to identify ideal candidates based on their speech pattern, body language, tone, facial expression, vocabulary. As a result, with the deployment of an AI-driven tool Unilever annually saved £1M, followed by a 90% reduction in hiring time and a 16% increase in diversified candidates.|
|Entelo – Sourcing Tool||It is considered as an apt tool for all recruiters for sourcing candidates based on the required gender, veteran status, ethnicity, etc. Lyft, PayPal, and Target have Entelo build a powerful team using its predictive analytics and NLP-based algorithm.|
|Mya Systems – Chatbots||The conversational AI algorithm of Mya System helps to streamline the recruitment process for companies like as L’Oreal, Adecco, Hays, and Deloitte. These renowned companies have deployed an efficient tool for ensuring healthy conversation with the candidates. As a result, have turned up adding proficient candidates to the team and enjoying cost benefits too.|
Data driven Talent Acquisition
Data-driven talent acquisition is becoming the core for improving and optimizing the complete hiring process. The skills and abilities measured using data-driven technologies are completely unbiased, non – adverse and fix the broken recruitment funnel too. The measurement of candidate skills begins with a non –biased assessment which is largely driven by accurate data prediction and prescription. Some of the basic steps for incorporating data-driven analytics in the recruitment process are:
Understanding the impact of data-driven recruitment
The data analytics power the recruitment process and helps to identify the benefits of data-driven recruitment for each department. Moreover, a lot of data analytics technology ensures building powerful products with great ease.
Analyzing and mapping the existing recruitment process
The data-driven recruitment process plays a crucial role in identifying the precise role and responsibilities of each department. It includes identifying the role, establishing a job description, posting, and generating suitable reviews. Moreover, it also considers establishing a suitable environment for screening calls, in-person interviews, reference checks, etc.
Developing new data-driven recruitment process
The precise data-driven concept helps to develop and design the right strategy and procedures for hiring a suitable candidate. As no organization prefers incurring extra costs for the recruitment process, therefore, it ensures that all the policy implementations are well–researched and result–oriented. As a result, it helps to fills the skill gap with great proficiency.
Understanding and implementing the assessment options
There are several tools and technologies to optimize the assessment process and achieve desired results. While working with the custom assessment platform the team can certainly enjoy the benefit of specialized positioning. Moreover, the data-driven recruitment process creates an unbiased environment for assessing the performance of the candidates.
Analyzing the results
After all the strategies and procedures are implemented in the hiring process, it becomes important to compare and analyze the output. The analysts can analyze the prevailing gaps and fill them with suitable strategies to create the right mix of the hired candidates, Moreover, the data-driven analytics also focus on creating providing a healthy experience to the candidates.
Predictive Analytics in Talent Acquisition
The widespread concept of predictive analytics is the strongest funnel for the recruitment process where it drives efficiency and effectiveness in the organization. The predictive analytics in talent acquisition encompasses all the steps relating to the candidate screening to selection to onboarding. As a result, predictive analytics add strength to all the recruitment elements and eliminate the considerable barriers to goal accomplishment. The thorough analysis of the recruitment funnels focuses on learning the approximate number of applicants to make required hires. The more the data is, the better will be the prediction relating to talent acquisition.
Additionally, the application of predictive analytics in talent acquisition and management is improving the quality of hire, time –to–fill, and reducing the cost –per –hire. The proper evaluation and analysis of the existing data pave the way for meeting organizational goals with great ease. Moreover, the algorithm of predictive analytics is best –suited for the sourcing process where it becomes easy to pick the right candidate from the crowd during campus –placement, post–probation period, etc. Alongside, the data analytics also focus on determining the recruitment advertising and marketing plans to yield strong results. It all begins with the collection and analysis of talent acquisition data which is further used for optimizing the complete HR ecosystem.
HR Analytics in Talent Management
HR analytics which is also termed People Analytics, Talent Analytics, or Workforce Analytics involves the collection, analysis, and reporting of HR data to the team. The tool has emerged as the strongest support of the industrial sectors that are keen to measure performance and maintain healthy employer-employee relationships. The introduction of the data-driven HR analytics concept in talent management has shaped the operational discipline of the companies into a more profound strategic approach. Additionally, the term has been improvised to be called Strategic Human Resource Management (SHRM).
The use cases of HR Analytics in Talent Management revolves around two fundamental aspects which help to revolutionize the complete marketing aspect.
Decision – making
The strength of the decision lies in creating a healthy environment for HR intervention. As a result, HR Analytics act as a comprehensive concept to take organizations closer to their long–term goal achievement. Moreover, HR Analytics tests the effectiveness of the process and helps to facilitate better decision–making.
Operational to strategic partnership
The strategic partnership towards the common goal achievement is driven by proper recording and storage of employee data. The availability of such data which is derived from Data Analytics serves as the base of progressive partnership besides staying centered on the operational aspects of the organization.
Talent Acquisition Data Analyst
The talent acquisition data analysts focus on managing day–to–day operations of the business entities, It largely includes employee questions, administrative processes, maintenance, analyzing candidate experiences, and driving them towards organizational goal achievement. The core responsibility of the talent acquisition data analysts is to streamline the organizational goals with the application of the right human resources in each department.
The major responsibilities of talent acquisition data analyst are:
- Analyzing the human resource requirements in each department of the company and providing the talent acquisition team with the relevant data.
- Producing several reports each week to ensure effective measurement of the HR initiatives and policies.
- Communicating all recruitment-related information by thoroughly analyzing the applicant tracking system.
- Managing the candidates throughout the interview process and help them establish a healthy organizational rapport.
- Partnering with several external and internal stakeholders to coordinate during the interview process.
- Conducting the reference checks for the final candidates and issue offer letters to the candidates.
- Ensuring healthy onboarding of the candidate after the final stage of the recruitment and facilitating training program for them.
- Followed by onboarding, the talent acquisition team now focuses on the holistic development of the employee and ensures timely training is provided.
The emergence of cognitive technology for talent acquisition has enabled robust decision-making while selecting the candidates and planning the recruitment budget. Thus, the use of data analytics in talent acquisition has brought a huge revolution in the hiring culture. Moreover, 77% of the professionals claim the use of data analytics for workforce planning and talent acquisition in their company. Also, the LinkedIn report conveys that organization with mature analytics leverage two times healthy results than an organization that does not. So, evidently, data analytics is highly potent, here to stay and be the change-maker for the sustainable growth of the industries.
Topics in Data Analytics
- Advanced Data Analytics
- Clinical Analytics
- Credit Risk Analytics
- Cyber Risk Analytics
- Data Analytics for Customer Behavior and Customer Experience
- Data Analytics for Customer Journey
- Data Analytics for Fraud Detection
- Data Analytics for Human Resources
- Data Analytics for Logistics and Supply Management
- Data Analytics for Risk Management
- Data Analytics for Talent Acquisition and Management
- Data Analytics in Asset Management
- Data Analytics in Digital Marketing
- Data Analytics in Healthcare – Use Cases, Metrics, Techniques, Companies and More
- Data Analytics in Manufacturing
- Data Analytics in Pharmaceutical Industry
- Descriptive Analytics – Definition, Types, Examples, and More
- Digital Analytics
- Financial Data Analytics
- Financial Risk Analytics
- HealthCare Claim Analytics
- Insurance Risk Analytics
- Insurance Risk Analytics
- Population Health Analytics
- Portfolio Risk Analytics
- Revenue Cycle Analytics
- Risk Assessment Analytics