How Data Helps Recruiters Achieve Better Results

How Data Helps Recruiters Achieve Better Results Sponsored by Perengo “What gets measured gets improved.” â€" Peter Drucker. Data analysis, or analytics, enables business managers and planners to capture and interpret large datasets to guide business decisions.  A correct implementation of analytics techniques and tools can boost business performance by putting useful information in front of decision-makers and thereby improve profitability. In the field of recruitment, data analytics offer essential benefits to HR practitioners, such as: Reviewing the hiring process performance in detail Analyzing conversion rates along the application funnel Identifying well-performing applicant profiles Uncovering areas for improvement It can be challenging to work with complex datasets, especially if there is no clear focus.  A fundamental concept is “Garbage In = Garbage Out” or GIGO: if the collected data or analytical model is incorrect, the information retrieved will be meaningless as well. It will also lead to false or no conclusions. Useful data for recruitment campaigns would be: Data: Number of users across the several steps of the recruitment funnel; time spent on those steps; drop-off rates across these steps; etc. Model: Proper tracking via  multi-touch attribution These are the kind of relevant recruitment data that give recruiters the capability to perform the right analyses, gain better insights, and make well-informed decisions during the talent acquisition process. Recruitment analytics should always produce actionable insights to improve campaign performance and optimize recruitment and hiring processes. The application funnel: a recruiter’s optimization problem The  purchase funnel  is a concept that has been in use in marketing since the late 19th century. It is used to model the different stages of customer interaction with a product; a process also called “the customer journey.” It was first proposed by Elias St. Elmo Lewis in 1898 and depicts these stages after what has been called the  â€œAIDA”  model: Awareness: Customers first learn about the product’s existence Interest: Customers express an initial interest in the product Desire: Customers actively want to buy the product Action: Customers finally take action and acquire the product A funnel usually has three sections: top, middle, bottom. The funnel-like shape of this model stems from the fact that often, at every stage â€" from top to bottom â€" there is a drop-off going from awareness to action, as not every customer will be interested. Then another set of these will not develop a desire to purchase it. Finally, yet another group of customers will drop off before buying the product. In the recruitment field there is the  application  (or  recruitment) funnel, which describes the candidate journey through the hiring process in five stages: Each one of these stages has relevant metrics  related to the different ratios of drop-off or churn between steps, such as visitors to applicants, applications to interviewees, interviews to offers, and offers to hires. As with other funnels, an important consideration when working through the application funnel is that friction needs to be minimized between stages. Friction is anything that keeps the user from making progress along the application process: Sourcing problems   Websites with poor branding   Usability issues   Ineffective hiring process Unattractive offers Both the  user experience  (UX) and the  candidate experience  need to be streamlined and optimized to increase effectiveness. The goal of the HR department is to achieve recruitment goals with the best possible ROI. This is done by obtaining the right volume of hires through an optimized funnel with reduced drop-off rates. In other words: by hiring the correct number of qualified candidates, which improves costs and optimizes the recruitment process. Hard data informs decision making Useful recruitment analytics require a proper data collection, an application funnel with a logical flow and optimized stages, as well as a focus on the right numbers. Not all metrics are equally valuable, some of them might be even misleading. Organizations need to focus on data that will let them make better decisions and improve ROI. The right tools can help manage data. A programmatic recruitment platform helps to analyze data to implement better buying decisions. These insights are leveraged to optimize  job ad  placements for the best performing recruitment channel mix for the particular organization. The following metrics can help improving results: Volume of applicants: Number of applicants entering the top-of-funnel stages. This will inform about the effectiveness of the application website, branding, and sourcing initiatives. Volume of applicants per funnel stage: Observing the number of candidates per stage will help to identify possible friction points and opportunities for optimization across the process. Volume of hires: Amount of hires starting to work. The ultimate goal of the recruitment process is to achieve an optimal volume of hires with a high retention rate. Cost per Lead (CPL): Cost of a job seeker landing in the application funnel Cost per Applicant (CPA): Cost of a job seeker performing some action within the application funnel (e.g., contact info submit; background check submit; etc.) Cost per Hire (CPH): Defined as the sum of all recruitment costs (internal and external), divided by the total number of hires during a specific period. Internal costs are all the costs of the recruitment process inside the company (HR staff, organization, capital), whereas external costs are all the expenses related to external vendors involved in recruitment. Lifetime Value (LTV): This metric is particular to each business as every organization has its definition of  quality of hire. However, some measurements are persistently used such as performance levels, time to productivity of new hire and retention rates, among others. By carefully tracking and optimizing these metrics, the recruitment process will continuously improve, helping organizations to meet their business goals. Bottom Line: Recruitment Analytics Is a Competitive Advantage HR professionals need to have a thorough understanding of their organization’s business goals. This will let them define their application funnel and optimize the recruitment process as a whole. Choosing a recruitment platform with  true programmatic recruitment  capabilities and a focus on the needs of recruiters (rather than the need of publishers) can give organizations a deeper understanding of data across the entire application funnel. The resulting insights can help to achieve recruitment goals while improving the bottom line. About the author:  Mike Kofi Okyere is founder and CEO of Perengo, a programmatic recruitment platform for performance-conscious recruiters working for high-growth businesses and Fortune 1000 companies. Mike is applying his years of experience in the world of e-commerce and adtech to improving the world of recruitment through algorithms and machine learning. Previously, he served as the head of performance advertising for AdMob (SEA/AU NZ), before its acquisition by Google in 2010. At Google, he drove the strategy and execution for mobile display advertising as head of mobile advertising for Australia/New Zealand, and then head of mobile display advertising for Google Asia. Follow Mike on his HR Technologist blog on Medium.