Data and Analytics for Talent Acquisition

For a long time, HR processes have been based on individual perceptions, ideas, intuitions and specific cultural practices rather than actual data from the ground. Using an approach that does not always derive from or marry facts and data results in adverse outcomes that manifest themselves in different ways. For example, see the results of a survey by CareerBuilder (2017):Infographic-1


Hence, it is important to look at talent and related processes objectively through data.

In this blog, I have tried to highlight a few scenarios on how capturing and interpreting different types of data at different Talent Acquisition stages can help improve outcomes. 


Demographics such as age, location, gender, educational level etc. of candidates are quite easily available across stages of the hiring process. Many aspects like performance, stability, motivation vary by demographics within the organization or even within the same job role.

Using inferences from the demographic data of already hired candidates can help recruiters figure out the hotspot of favourable pool in terms of quality or quantity of candidates geographically. HR Managers can compare the performance of candidates by demographics. This may lead them to discover differences by job role type and derive a success profile based on demographics, and thus focus on success profile for that role.

According to a study done by the Department of Lifelong Learning (India), the performance of employees generally increases with age up to 45 years and decreases gradually after that. HR should evaluate performance by age groups for different roles and target favourable ones to optimize processes and save cost.

Here is a sample analysis of average assessment performance (scores, let’s say) which can help focus on specific age group for a role/level to save bandwidth while conducting assessments for hiring. Low performance on assessment indicates low suitability for that role (higher experience level candidates in below example).


Talent Characteristics

Skills, knowledge, abilities, and personality are the most critical attributes of any employee leading to high job performance and thus organization growth, and so these should be considered while hiring a candidate. But how can this data be obtained?

Assessments and interviews/ personal interaction can capture a lot of data on talent characteristics of employees or candidates. For example, for a job role, assessments data can help organizations get the required type and level of cognitive ability or preferred behavioural traits. By looking at concurrent data of incumbents, recruiters can also benchmark for internal standards or get a typical candidate profile that is desirable to hire.

Many organizations working with us use different score benchmarks for the same job role with different experience levels. For example, an organization may accept candidates above a certain cutoff on a skill assessment for a role with 0-2 years’ experience, while using a higher benchmark for candidates experienced for 2-4 years.

In summary, key metrics that HR can get leveraging talent characteristics data are:

  • Competency types
  • Competency levels by roles
  • Benchmarks, Norms for different roles

Selection Data

Offering the right fit candidate is not the endpoint of getting data; the journey should continue and deep-dive analysis into selection data can help optimize the process further.

Here are some typical data points that can easily be considered:

1. Selection/ Rejection numbers: The overall pipeline and bottlenecks of the process can be figured out. Bottlenecks can be sourcing, assessments, interviews (technical or HR round) etc. and steps can be taken to improve the conversion at that stage.

Typical analysis can look as shown below:Infographic-2

Metrics like offer ratio, joining ratio, assessment throughput, footfall for interviews etc. can provide insights on bottlenecks or stages where improvement is required. This can be done by comparing organization-specific numbers with industry averages for similar roles.

HR can also capture subjective rejection reasons during interviews and come up with filtering criteria that can reject candidates with that profile beforehand to save interview bandwidth and hiring cost.

2. Time to hire: Time is money, and this holds true for recruitment as well. The process should be quick enough to avoid any business loss due to unfilled critical job roles and thus it is essential to figure out the steps consuming more time than expected.

According to a survey conducted by the Society for Human Resource Management, the average time taken to fill a position is 41 days. But with a tighter talent market and increased hiring volume, recruiting teams are facing increasing pressure to reduce their time to fill vacancies.

Tracking the time to hire by role, level, geography and comparing with benchmarks in the industry can improve their hiring cycle by optimizing processes for specific roles, geography etc.

3. Cost of hire: It is crucial to have a solid estimate of the cost of hire, including paid sources, assessment cost, HRIS systems, recruiting events, etc. This figure can help you make smarter investment decisions, define your referral bonuses and save the organization money.

4. Attrition data: Attrition data assists in ascertaining some specifics, like how long employees worked before leaving, reasons for departure, positions worked and types of employees who left. These specifics represent areas that need more attention and improvement, thus optimizing the hiring model.

Here is a possible scenario which can help figure out the profiles to be focused on reducing the overall high attrition rate in the organization. Clearly, Profile 1 and Profile 2 have significantly high attrition rate versus others.


Performance Data

Measuring employee performance through captured data can help identify under-performers and outstanding employees and thus manage compensation & benefits accordingly to make efficient teams.

Performance data can also be used for assessment validation, job analysis and benchmarking while designing assessments thus putting together a correct selection model in place.

Many of our clients conducted assessment validation (running customized assessment on their internal employees) and data showed that some of the competencies highly recommended for concerned job role had quite low correlations with actual job performance. Many factors including organization culture, market type etc. may lead to stark differences in an ideal job role and linked competencies. Using such analyses can help in correcting incorrect perceptions around job roles specific to organizations.

Challenges with HR Data and the Way Out

  1. Bringing Together Data from Different Places

Data initiative requires HR to acquire data from all the different departments within the business. They have to acquire, sanitize, unify, and analyze data from multiple departments as well as from multiple business functions.

To solve this problem, HR needs to have people with skills to gather and prepare data, in addition to performing analysis.

    2. Worries About Privacy and Compliance

When HR collects data on a candidate, particularly data from outside the company, the department has to consider privacy.

However, a lot of data can be used post whitelisting (without personal details) which can serve the purpose of analysis, without breaching someone’s privacy.

   3. Business buy-ins and approvals

Getting approvals around data sharing with assessment partners for deeper analysis is sometimes a challenge. Bottlenecks on such approvals and buy-ins lead to insufficient data availability, and eventually to low impact analytics.

HR managers need to be able to show end outcomes and partner with business for the entire life-cycle of the candidate, rather than have a relationship limited to delivering hired candidates. Data sharing expectations should be established at the beginning of cycle/hiring process along with expected outcomes.

   4. Low confidence in model/ data-based processes

To many HR managers, the idea of implementing people analytics equals letting computers decide whom to hire.

Although the desire to be ethical by sidelining computers is commendable, using all available tools to hire the right people for the right jobs is the ultimate goal for HR.

Glimpses of the real world

We have been helping clients figure out their specific Talent Acquisition problem through data and help with a solution to get rid of that. Here are a few examples of multiple scenarios.

Case 1:

Challenge: Recruiting over 700 employees per year from 7,00,000+ applications

Solution: A realistic job preview was designed and built into the client’s careers portal to help applicants decide themselves whether they wanted that job or not




Case 2:

Challenge: Facing more than 100% attrition at a specific level of front-line sales role


  1. A customized predictive hiring assessment was designed through detailed job analysis and internal validation
  2. The hiring managers were trained on system administration & interviewing skills
  3. Talent Acquisition Analytics was carried out – Candidate Success profiling & factors influencing the quality of hire



Case 3:

Challenge: Hire 9000+ candidates for a Business Process Outsourcing role at 7 different locations in India through a scientific and consistent process across regions, bringing in better efficiency and adding value to operations and candidate recruitment


  1. Custom designed Cognitive ability assessment and benchmarks with a short administration duration of 20-25 minutes
  2. Assessments conducted at client offices across 7 locations in India through a structured walk-in process
  3. Webcam enabled online proctoring ensured that candidates were monitored


  1. Approximately, 9600 candidates were offered from the larger pool of assessed candidates
  2. 4X improvement in hired candidate quality in a year

About the Author:

Rishabh Saxena is the Senior Talent Analyst at Aon’s Assessment Solutions. He is an expert in helping organizations across industries and specializations solve talent problems through data based insights.


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