It is becoming increasingly difficult to recruit new employees, and every recruitment process involves time and financial costs. For this reason, companies are always looking for ways to optimize. One solution is data-driven recruitment. Targeted data is collected, analysed, linked up, measured and evaluated to increase efficiency and attract more candidates. In an interview with Sarah Bucher, we learn how PostFinance’s data-driven recruitment is improving the candidate journey and improving processes.
You are here:
Data-driven recruitment optimizes the application process
Who is applying for which position? Which channels are used? At what point do candidates drop out of the recruitment process? Data-driven recruitment covers these and many other questions. Data is systematically collected and analysed in order to optimize both efficiency and the experience for the candidates.
Sarah Bucher has worked as Head of People Attraction at PostFinance since 2020. Since 2022, she and her team of 13 people have been advancing data-driven recruitment.
Sarah, what role does data-driven recruitment play at PostFinance?
In May 2022, PostFinance reorganized its recruitment and added a dedicated data-driven recruitment specialist to its team. Data is an important building block for a successful recruitment process. The goal of data-driven recruitment is to enable efficient and effective personnel recruitment while also providing candidates with the most positive experience possible along all touchpoints in the candidate journey.
How can data-driven recruitment be used to find suitable candidates?
Data help us to better understand the channels via which candidates become aware of our positions, leading us to valuable conclusions for future recruitment. For example, we now know that we don’t need to advertise IT positions on the usual job portals, but only on LinkedIn and our careers page. We also try to match candidates who have applied for a position with other suitable vacancies in order to avoid the need for repeat advertisements. This is a win-win situation for both sides. Last year, we began evaluating the channels per target group, and we are constantly collating and evaluating the data so that we can hire the right employees quickly and cost effectively.
How can the candidate journey be optimized in a data-driven manner?
We ask candidates how they found the process: what did they like? What was missing? What was their timeline? We combine this qualitative feedback with quantitative data: how long did the process take? When did candidates drop out and why? Where do we still have potential? Thanks to this consistent data collection, we are able to get an accurate impression of the candidate journey. Our goal is to constantly optimize the experience for candidates and make it even more positive.
Which technologies are used in data-driven recruitment, and to what extent is artificial intelligence used?
Automating recurring processes and using artificial intelligence would make our everyday lives easier. The many different systems currently in use represent a challenge, but initial steps have already been taken. We use CV parsing, for example. The software compares CVs received online with our requirements and provides a matching score. We can then use the rating when making our selection. Conversely, with candidates’ consent, it is possible to match their CVs with open positions within our company. This allows us to suggest other suitable job offers at our company to interesting candidates.
How is the success of data-driven recruitment evaluated?
To measure success, we use numerous key figures, aggregated KPIs and performance indicators. Among other things, we evaluate the performance of our channels (e.g. where did the applications come from? What was the quality of the dossiers?) and our processes (e.g. how many candidates came to the first interview? How many made it to the second interview? Did any candidates withdraw from the process?). But there are other important metrics such as diversity. We evaluate aspects like the recruitment funnel. And we check our attractiveness as an employer on the market every year.
What advantages does data-driven recruitment offer companies?
Data-driven recruitment provides several advantages: budgets can be used where they bring the greatest return. This makes it possible to optimize costs. Recruitment is conducted where potential employees are, enabling an increase in efficiency.
It is also possible to improve processes, positively impacting the use of resources and the candidate experience – which in turn affects PostFinance’s attractiveness as an employer.
What are the challenges?
We work with many different systems. This is a challenge for us when evaluating data. If reliable data can be extracted from the systems, it is important to link up that data in a meaningful way in order for it to be useful. This takes time and expertise. Companies shouldn’t expect HR professionals or recruiters to do this as an afterthought. Data preparation and analysis require a dedicated specialist with specific knowledge of the recruitment tool landscape and data analysis as well as the processes involved in recruitment. For example, anomalies must be detected and understood for the data to be meaningful. Furthermore, the data must be prepared and visualized in order to work with it, and it also needs to be kept up to date. Reliable data management is paramount.
What do HR managers need to consider when using data-driven recruitment?
Data is ideal for further development and process optimization and as the basis for discussions and decisions. But it must be constantly enriched with experience and reflected in people.
What will recruitment at PostFinance look like in the future?
Data-driven recruitment is here to stay. We want our key figures and KPIs to be available on a daily basis and as automatically as possible so that we can use them for our everyday work. Working with managers, we can find the most efficient methods to find suitable employees and thus fill our open positions with the right people. And we want to use the data to align our candidate journey even more closely with candidates’ needs. As a company, we will also increasingly be required to look to the future and anticipate the skills of tomorrow in order to attract people with these competencies at an early stage. One of several useful tools for this is Talent Relationship Management.
Important KPIs – what gets measured in data-driven recruitment
The aim of data-driven recruitment is not only to collect, but also to measure and analyse key performance indicators (KPIs). These provide information on the success of recruitment measures and help to improve the process. There are many different KPIs. Among the most important are the following:
- The “click-through rate” measures how often the job advert was displayed and how often it was clicked on.
- The “cost per click” KPI shows the costs for performance-based ad placement. With this model, the advertising company pays for every click on the job advertisement.
- The “conversion rate” indicates how many of the clicks on the job advertisement led to an application.
- It is also helpful to know which recruitment channels are used, how many applications have been received for the position to be filled and how many of them are of good quality.
- The “time to hire” indicator shows how long it took for the position to be filled successfully.
- The “cost per hire” KPI shows all costs incurred for filling the position.