Data-driven recruiting: the selection process of the future

This method gives companies that invest in it a strategic advantage: they identify the ideal candidates faster and more precisely. Data-driven recruiting makes it possible to stand out from the competition by promoting well-founded, evidence-based decisions.

What exactly is data-driven recruiting?

Data-driven recruiting means relying on data throughout the entire recruitment process. No gut decisions, no sympathies, no nepotism - just data that is unemotional and precise. Traditional methods, which were often based on intuition and subjective judgments, often led to mistakes. Data-driven recruiting is based on collecting, analyzing and evaluating data.

Modern analysis tools enable companies to gain deeper insights into the qualifications, experience and potential of candidates. The aim is to objectify the recruitment process, minimize bias and make personnel selection more efficient and cost-effective. Data-driven recruiting is becoming increasingly important as the labor market becomes more complex: A wide range of qualifications among a small number of applicants makes it difficult to select the right candidates quickly. Data helps to identify patterns and trends that would traditionally be overlooked.

How does it work?

Data collection

First, the company must collect and combine data in order to obtain a coherent picture of the candidates. Important data sources for this are, for example

• Applications and CVs: Information on qualifications, professional experience, educational background and skills.
• Introductory and exhibition talks: use systematized surveys and documentation of the talks as a feedback opportunity.
• Social networks: Platforms such as LinkedIn, Xing or even Facebook offer insights into a candidate's professional network and activities.
• Online tests and assessments: Many companies rely on standardized tests to objectively evaluate the skills of candidates.
• Data from job interviews: This includes notes, assessments and the results of structured interviews.
• Data from market research institutes
• Own surveys: For example, for acceptances, rejections and integrated into other regular communication.
• Data from public sources

Data is not only collected on candidates, but also on job advertisements that have already been placed. This makes it possible to see which job ads work well and where, and which are either in the wrong place or fall through with the target group:

• How many applications were received for a position and where did these applications come from?
• Where have we placed job advertisements that generate hardly any applications and what could be the reason for this?
• Conversion rate: How many clicks on a job ad actually result in an application? And if not, at what point do candidates abandon their candidate journey?
• Click-through rate: How often do candidates click on a job ad displayed to them?

Data analysis for good decisions

The collected data must then be organized and evaluated. The market offers a wide range of analysis tools and algorithms that help to identify patterns and correlations. This also includes machine learning. This allows companies to predict which candidates are likely to be successful in the company based on data from previous top performers, for example. Modern recruiting tools can use the collected data to speed up the recruitment process and improve the quality of decisions at the same time: For example, by adapting interview questions or readjusting communication channels and even the tone of voice when communicating with applicants. By adapting communication, the selection process is continuously improved in real time so that more and more ideal candidates "bite" and stay on board after being hired.
The right communication channels are very important because this is where a wrong decision costs the most money. If you know exactly where the desired candidates are, you can use a limited budget in a more targeted manner and fill a position with the ideal person at a pre-calculated cost in a predictable time frame.

Once the job ad has been designed in the right tone of voice and placed in the appropriate channels, it is important to monitor the resulting data, as mentioned above. How many people see the ad, how many click, how many apply? Are the applicants ideal candidates? If not, why not? How do they differ from ideal candidates? And: How many click away again?

Retargeting

The candidates whose interest has been aroused are interesting for retargeting: Perhaps the job description was suitable, but not the benefits? Or the number of hours? Or the tone? The compulsory attendance? There are many reasons why someone looks and then looks away again. Often it's not the job, the ad or the benefits at all, but the fact that someone is not yet ready to reorient themselves and is "just looking around once"?

Just like people who "just look around" in the store, often return later and buy something after all, candidates may think about the option longer, discuss it with their family and with their pillow, but are really interested. These candidates are receptive to automated retargeting. If they do decide to switch after some time, they will have the company with the retargeting first in mind or even decide to apply for a change BECAUSE of the constant retargeting.

What's the point?

Let's start with the most obvious:

Lower costs, more efficiency

Data-driven recruiting not only saves money by not investing in useless communication channels (where the desired candidates are not even present). Further savings are made because companies avoid expensive bad hires thanks to the efficient use of technology and data-based decisions. Furthermore, the selection process is shortened with data-driven recruiting because promising candidates are much more likely to apply and recruiters therefore have less "sifting" to do.
Support with data analysis and optimized interview scheduling also reduce manual administrative effort. This reduces the time needed to select a suitable person.

Better matchmaking

Meeting all the technical requirements is one thing, but does the person also fit in with the corporate culture? After all, even if the job goes smoothly and is successful, no one stays there for long if they don't feel comfortable. By evaluating data such as previous work experience, the working atmosphere there, work habits and personal preferences, companies can assess which candidates will be successful and satisfied in the long term and put down roots in the company.

Better candidate experience, more fairness, less bias

If candidates are met with a job ad tailored to their tastes/needs (e.g. videos for Gen Z, technical details for tech-savvy candidates with experience and looking for challenges) in a tone that suits them with a design that appeals to them, the application process is more pleasant for the candidates and puts them in a positive mood for the company in advance. Even if this application does not result in a hire, candidates will remember the company positively and are more likely to apply again or recommend it to others. This creates positive employer branding.

Subjective decisions are a major danger in recruiting. No matter how hard we try, no one is safe from them. Due to our socialization, we all carry around a backpack full of bias and stereotypes, and even workshops and good will can only change that to a limited extent. Data, on the other hand, is neutral. It does not favour or discriminate against anyone, but shows what IS, whether we like it or not.
Data-based selection procedures minimize bias and are therefore fairer than traditional procedures.

If a job advertisement does not perform as desired or is not seen, you will find out in real time with the right data-based recruiting tools and can reschedule during the ongoing process.

What challenges does it pose?

Like everything in life, data-driven recruiting also has a second side:

Only as good as the data used
Data-driven recruiting can only be as good as the data used for it. If it has not been collected professionally, it is little more than a good old gut feeling and can also contain bias. Incomplete data can also lead to companies perhaps placing their job advertisement in the right place, but not designing it to suit their target group - or vice versa. Companies must therefore ensure that they collect their data correctly or obtain it from professional sources with guaranteed quality. They also need data analysis tools and know-how to filter out the important information from the data and then draw the right conclusions.

Internal resistance to innovations
"But we've always done it this way." Do you know this? This sentence is often heard when old processes are replaced by new ones. Some of the workforce will be enthusiastic, some will shrug their shoulders and accept the change as "part of the job" and some will protest or at least make it known that they would prefer to carry on "as usual".
A radical change such as that from traditional recruitment methods to data-driven recruiting is often accompanied by a change in corporate culture. Here it is important that a) all those affected understand WHY the change is necessary (instead of simply imposing it), HOW the change will take place and how they themselves will benefit from it.
Apart from changes in the corporate culture, the introduction of new technologies that are often unknown to individual employees is the biggest resistance factor. Here, management faces the challenge of selecting the right, user-friendly tools and carefully training employees in how to use them. This is best paired with an open-door policy or an opportunity to address questions and clarify them at eye level. If good tools are introduced and used incorrectly in a company, it can lead to processes not being improved as hoped. Then the "let's carry on as before" faction would have an argument against further innovations.
Working with change personas is a very good option for picking up and involving employees who have doubts and fears or do not fully understand the benefits of an innovation.

Data protection
All data collection and use must comply with the General Data Protection Regulation (GDPR) in the EU. Furthermore, companies must store and process all data transparently and securely. In this way, you firstly avoid legal problems and secondly build the necessary trust to be able to collect data successfully in the future. After all, the most valuable data is the data that customers and candidates actively share with companies.

No decision automation
AI supports recruiters best by taking over routine tasks, helping with data analysis - and thus achieving one thing above all: enabling recruiters to focus their work on what they do best: Putting people at the center of their work.

Personas as an important tool for data-driven recruitment

Of all the options for introducing data-based selection processes, candidate personas are probably the most user-friendly and will therefore meet with the least resistance from the workforce. data-driven personas delivers the ideal candidates on a silver platter, so to speak, ready to use and easy to understand for everyone involved:

A candidate persona is a fictitious, data-based representation of the ideal applicant for a specific position or target group. It is based on findings about demographic characteristics, professional qualifications, career goals, personal interests and preferred communication channels. By using the persona to understand the needs and motivations of the ideal candidate, it is much easier to optimize the approach and selection of talent.

The data required to create such personas can come from/be collected by the company itself or be purchased externally, e.g. from renowned market research institutes. At the Persona Institute, we do both for you and more: we deliver ready-made, data-based candidate personas and introduce them into the company using workshops and persona playbooks. For employees, this feels less like a new tool and more like the introduction of a new team member and is therefore easy to digest.

Data-driven recruiting with candidate personas is a powerful tool that helps you make better and faster recruitment decisions and significantly improve the quality of candidates. This reduces costs and leads to more objective, transparent recruitment. Companies that get involved at an early stage benefit in the long term from more efficient processes and better employee compositions.

If you have any questions about candidate personas and data-driven recruitment, we are always happy to help.

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