Long gone are the days of traditional recruitment methods involving manually reviewing resumes and placing call after call to schedule interviews. Data-driven recruitment streamlines the hiring process and measures outcomes for each analyzed metric for improved strategies.
Utilizing analytics to make hiring decisions is more objective than the usual subjective methods involved with traditional hiring avenues. The shift towards modern-day recruitment processes lessens bias and bases hiring on factual evidence rather than personal opinions. Let’s analyze more general information about this recruitment trend and analytics’ role in the process.
Understanding Data-Driven Recruitment
Data-driven recruitment involves taking into account various analytics throughout the hiring process. Companies can gain the insights they need to make more objective recruitment decisions by analyzing candidate data, hiring metrics, and candidate sourcing. This approach enhances overall candidate quality while streamlining hiring procedures.
Such quantifiable metrics obtained through hiring analytics identify trends and evaluate each candidate who submits an application and resume. Regularly reviewing these metrics can evaluate the current effectiveness of the company’s recruitment strategy and start the road for making improvements.
The Role of Analytics in Recruitment
Analytics in recruitment is a powerful resource that gathers insights into every stage of effective hiring with data. Here are the various roles that analytics play during recruitment.
Improved Candidate Sourcing
The recruitment team can improve the company’s candidate sourcing channels by evaluating their performance. Any failing candidate sourcing channels can be removed from the applicant tracking system with more focus placed on the successful ones.
For example, if LinkedIn and Indeed are your top two sourcing channels for finding the best hires, then you can keep these in your arsenal and omit the lesser-performing ones. Depending on your ATS software’s integration capabilities, do not be afraid to try new candidate-sourcing channels.
The client’s job board integration is also affiliated with Monster and ZipRecruiter. Hence, it’s best to give new sources a try to evaluate if they are effective for your company’s candidate sourcing portfolio or not.
Enhanced Candidate Assessment
Analytics contribute to enhanced candidate assessment techniques. AI-driven resume parsing and skills assessments ensure your recruitment team selects the highest quality candidates and weeds out the chaff in the candidate pool.
CareerBuilder reported in 2017 that the cost of a bad hire could be upwards of $15,000 while a good hire leaving the company can cost double that. Taking into account today’s inflation, that amount could be much higher depending on your company’s industry and operations. Hence, it’s best to regularly review analytics during recruitment to reduce the chances of onboarding a bad hire.
Recruitment Process Optimization
A company’s recruitment process should be optimized periodically to identify complexities with current methods.
Is there a high candidate drop-off rate? If so, evaluate each part of the application process to ensure there are no repetitiveness or lengthy portions.
Is the cost-per-hire metric too high? If so, evaluate current spending for each stage of recruitment. Refine your candidate sourcing channels as explained above. Consider publishing evergreen job postings to build a well-rounded candidate pool over time rather than spending money on sponsored posts to attract applicants to open requisitions.
Implementing Smart Recruitment Strategies
Now that you know more about general data-driven recruitment and the role that analytics plays in it, let’s learn how to implement smart recruitment strategies.
Define Key Metrics
Which key metrics matter the most to your organization? Time-to-hire and time-to-fill are a couple of the best metrics to focus on to onboard talent quicker to maintain productivity after turnover. Time-to-hire is the length of time measured between when a candidate applies for a job to when they accept an offer. Optimizing time-to-hire means ensuring the ATS software is user-friendly and that communication channels are open to schedule interviews quickly.
Time-to-fill is measured between the time an open requisition is posted to when a quality candidate fills it. Optimizing time-to-fill involves making clearer job descriptions, implementing a video interviewing platform, and utilizing AI-driven solutions to select potential candidates quickly.
Utilize Predictive Analytics
Using hiring analytics software will help your company to learn the difference between a bad hire and a good one. Evaluating candidate data from past hires will assist the predictive analysis to unveil the experience, qualifications, and skill sets necessary to be a good fit for all your company’s roles.
During current recruitment campaigns, you can evaluate the best candidates that will stay with the company long-term. Your ATS software can help you with that step. However, it’s important to also note that candidates with long gaps in their resume or who have stayed with past jobs for 2 or fewer years may not be good long-term candidates.
Leverage Recruitment Metrics
Besides evaluating time-to-fill and time-to-hire, your recruitment team should leverage other key performance indicators (KPIs) to unveil the current recruitment strategies’ effectiveness. From there, you can make improvements accordingly to streamline the hiring process.
The candidate conversion rate is the percentage of applicants that go through every recruitment stage. Evaluating this metric can show where candidates are dropping off while applying. More than likely, it may be at the application process, but it could also be pre-interview or post-interview. At these times, candidates may have found better opportunities and refuse to interview or take an offer.
Quality of hire is an especially important metric that evaluates candidate quality. The company can note whether they have onboarded bad hires in the past to not do it again. The recruitment team can also build a candidate profile to ensure they know the best skills and qualifications future candidates should have to make better hiring decisions.
Embrace AI and Automation
Various advancements in AI can automate tasks while enhancing candidate engagement. For example, candidates can access the company website’s chatbots to start the application process, schedule an interview, or do another task in the recruitment process.
Matching algorithms can comb through candidates’ applications and resumes. The algorithms can note the soft skills and qualifications necessary for your open roles and match them to the qualifying candidates’ data for the utmost effectiveness during onboarding.
Foster Data Collaboration
Data collaboration amongst human resources and recruitment is important during the hiring process. After each stage of recruitment, these teams should be meeting to discuss how candidates are performing. Especially post-interview stage, the teams should host an in-person or virtual meeting to share their notes about each candidate.
Remote teams can communicate on virtual channels such as Slack to collaborate on their thoughts about each candidate. Even in-house teams can still use virtual communication channels to share their thoughts when they are not all in the conference room together.
Conclusion
Remember to always monitor and adapt your recruitment process to recent hiring trends and needs for the company. For example, if your company is on a tight budget, you may have to phase out a specific position in future hiring campaigns. In this manner, you will need people in other positions to do cross-training to complete multiple tasks. Don’t be afraid to make changes and modify accordingly if they don’t go as planned.
