Industry
High Tech
High Tech Solutions to Drive Business Transformation through Technology
Solution
Data and Analytics
Harness the Power of Data & Analytics to Accelerate Business Outcomes
Highlights
- Traditional recruitment strategies aren’t effective anymore, especially in the case of acquiring top talent.
- Recruitment firms must use data better while making recruiting decisions to scale up and meet market demands.
- Firms should use elaborate datasets of the candidates to arrive at their lifetime value and propose the right fitment for better candidate and customer satisfaction.
The future of staffing
Technology offers staffing firms an array of opportunities to source and groom talent.
However, most recruitment and staffing firms are yet to capitalize on these opportunities, resulting in a shortage of skilled staff in the organized and unorganized sectors.
Traditional recruitment strategies rely on human input to source candidates and are therefore incapable of allowing recruitment firms to scale up and meet the market demand. A recent study by Forbes suggests that around 57% of companies worldwide lack the data to make decisions on talent, making it imperative for recruitment firms to adopt a data-driven approach to capture untapped revenue.
Recruitment firms primarily handle two types of entities—customers and candidates—and store a good amount of data, which helps them to make data-driven decisions. However, to become a truly data-driven digital organization, recruitment firms should look at developing and maintaining elaborate datasets to arrive at a candidate’s lifetime value (CLTV). This will help them better evaluate candidates and propose the right fitment for a customer organization, thereby meeting the expectations of all stakeholders.
Decoding CLTV
The candidate lifetime value is the potential value a candidate can generate during their entire association with a recruitment firm.
The longer the duration of the association, the higher the lifetime value. For example, if a candidate changes jobs ten times in their career, the recruitment firm that is associated with them will generate 11 placement opportunities. Moreover, if the employee would like to refer candidates for job opportunities, they will prefer a recruitment agency that has maintained a good relationship with them.
As per the Bureau of Labor Statistics (BLS), there were around 11.245 million job vacancies in the US as of June 2022, indicating a significant gap in the skilled and non-skilled talent supply chain. Recruitment firms are looking for ways to tap into these vacancies and be able to increase their revenue.
Investing in a candidate with a higher lifetime value can potentially increase revenue and improve feedback about the firm. A recruitment agency loses ten placement opportunities if a candidate has had a bad experience during their first job search. As per Forbes, the average recruiter fee is about 15%, which means around 150% of the average salary of a candidate is lost (assuming the candidate changes 10 jobs in his entire career).
Putting theory into action
We recommend an analytical framework to define a candidate's lifetime value.
For this, staffing firms must assess candidates based on the recent interactions with the recruitment firm and their potential monetary value.
Figure 1 illustrates the indicative overall solution architecture, which should enable them to arrive at a candidate lifetime value solution. The existing data management platform can be augmented to reach the target steady state.
Figure 1: High-level architecture of a candidate lifetime value solution
Before implementing the candidate lifetime value assessment framework, firms must consolidate structured and semi-structured data into a single repository. They need to -
Gather candidate information from both internal and external sources in a data lake.
Develop a data mart with relevant candidate information required for the CLTV model.
Develop either a rule-based or machine learning (ML)-based model.
1) Rule-based: The formula should be the weighted average of the above data points. The weightage of the variable will vary from customer to customer and require some exploratory data analysis.
2) ML-based: The ML model will utilize the same data points to determine a value. However, firms need to score candidates based on their initial experience. This score can be used to train the model using ML.
Segment the candidate based on the lifetime value and develop a strategy around each of the candidates, which can potentially improve candidate satisfaction.
Develop a 360° view using various graph technologies to gain insights on the candidates (which customers the candidate has worked with, feedback from the customer, duration of the stay with various customers, and the geographical spread of the customers, among others).
What you can gain
CLTV optimizes the candidate search, match, and merge process, improving efficiency and outcomes.
Figure 2 presents some of the direct and indirect benefits of the candidate lifetime value approach.
Figure 2: The candidate lifetime value approach offers a range of benefits
Benefits of the candidate lifecycle
Further, a matured candidate lifetime value process maximizes a recruitment firm’s opportunity to gain additional benefits, such as the ability to:
Suggest skill gap recommendations and better opportunities
Monitor market trends
Offer the right job at the right point in a candidate’s career
Provide guidance on upskilling to adapt to the ever-changing job market
Additionally, a mature process can also result in fewer interviews and higher financial gains as candidates are placed on the right job. Therefore, knowing the candidates in depth will help build better relations and positively impact the revenue line of the recruitment firms.
