Demand for technical talent is at an all-time high, and this trend shows no sign of slowing down anytime soon. In this article, we’ll explore why an optimized recommendation-to-interview ratio can lead to a competitive advantage for your staffing firm.
In fact, according to a report published by the U.S. Bureau of Labor Statistics, “employment of computer and information technology occupations is projected to grow 13 percent from 2016 to 2026, faster than the average for all occupations.” Employers across all industries need highly qualified engineers who can develop innovative software applications, harness big data, design scalable systems, and maintain security in an increasingly connected business landscape.
To keep up with growing client demand for technical talent, many staffing firms are focused on expanding their portfolio of developers. Having a solid pool of technical talent is no doubt important, but it’s not always the best place to start. Rather, making incremental improvements to existing processes could yield immediate value and set the table for future growth.
What is the Recommendation-to-Interview Ratio?
Before we discuss best practices for streamlining your recommendation-to-interview ratio, it might be helpful to provide a basic definition for this metric:
Recommendation-to-Interview Ratio: The percentage of candidate recommendations made by your staffing firm that result in an interview with the client.
Real World Example: Let’s say that one of your clients is a small or medium sized software company. Last month, the company’s hiring manager requested your firm’s assistance in filling an open position for a full stack developer. Over the course of the engagement, your recruiters have recommended fifteen coders, but the hiring manager has only interviewed five candidates (and rejected the other ten). In this situation, your recommendation-to-interview ratio equals 33%. Obviously, your rejection rate is 66%, but we’ll discuss that topic in a future article.
Hiring managers appreciate an efficient recommendation-to-interview ratio, as it allows them to engage technical talent faster with less administrative effort and, ultimately, make smarter staffing decisions.
Better recommendations lead to more placements for your staffing firm. Placements lead to increased revenue, improved customer loyalty, and a healthier reputation in the industry.
Why Technical Placements Have Low Recommendation-to-Interview Ratios
In a perfect world, your recruiters would deliver a handful of prequalified, hire-ready engineers for each job vacancy. Instead, recruiters end up sending dozens of unqualified candidates because they lack the proper tools to assess technical ability. Each rejected candidate negatively impacts your company’s recommendation-to-interview ratio, thereby causing unnecessary friction in the relationship.
Why can’t your recruiters be more efficient when it comes to filling technical jobs? What are the reasons for such a low recommendation-to-interview ratio?
Here are a few possibilities:
Shortage of Technical Talent:
Most experts agree that we’re in the midst of a tight labor market. As pointed out in a recent Bureau of Labor Statistics survey, there is approximately one unemployed person per job opening in the United States (as of June 2018), down significantly from the July 2009 ratio of 6.6 unemployed persons per job opening. In other words, the pool of available candidates is shrinking, and technical talent is no exception to the rule.
Vague Requirements from the Client:
Hiring managers aren’t always the most technically-minded people. Prior to submitting requirements into their vendor management system (VMS), many hiring managers consult with in-house technical resources to gain a better understanding of needs. When in-house engineers are too busy to chat (which is common), a hiring manager may have no other choice but to commence and rely on partial information. This makes life especially difficult for your recruiters.
Lack of In-House Technical Knowledge:
With more than 250 known programming languages to potentially recruit for, how can your team be expected to consistently make informed recommendations? Without the right tools, they can’t. And, when clients ask for engineers with specialized skill sets, such as Ruby on Rails expertise, merely developing a quorum of viable candidates can feel like an impossible task. Tapping into adjacent talent pools would be nice, but your current database of candidates doesn’t offer such flexibility.
No Data to Support Your Recommendations:
How to Improve Your Recommendation-to-Interview Ratio
Remember, hiring managers aren’t impressed by the number of engineers you can throw at them. They want high-caliber technical talent, and they expect data-driven reports to back up your recruiters’ recommendations.
To give hiring managers what they really want, your staffing firm needs a technical assessment platform. Such systems improve your recommendation-to-interview ratio by helping you:
Get Better Pre-Screened Candidates
Resumes and LinkedIn profiles provide minimal insight into a developer’s true skill level. By implementing a flexible testing platform, your recruitment team will feel empowered to create, customize, send, and evaluate technical assessments that simulate an on-the-job environment. Rather than basing their recommendations purely on a “gut feeling,” your team will have an objective, scalable method for evaluating a developer’s technical competencies and potential job fit (before an introduction is ever made to the client).
Utilize Predictive Scoring
Artificial intelligence is revolutionizing how staffing firms predict on-the-job success for developers. Unlike simple answer matching formulas, machine learning algorithms reduce recruiting bottlenecks by analyzing code based on multiple variables, including accuracy, simplicity, and speed of completion. Some platforms go one step further, delivering a comprehensive coding score for an unbiased indication of each developer’s technical expertise.
Improve Candidate Turn-Around Time
Instead of clicking through hundreds of resumes, your recruiters gain instant access a segmented list of developers who fit the client’s needs. Recruiters can reallocate this time savings to value-added activities, such as conducting real-world programming assessments and engaging engineers in real time.
Build a Skills-Based Candidate Profile
Deliver clients an enhanced level of service by sharing candidate profiles, assessment scores, and other analytics captured during the pre-screening process. Give clients the full transparency they need to make informed staffing decisions.
Search for Adjacent Skills
For those hard-to-fill roles, some platforms make it easier to identify candidates with adjacent skills. Look for a system that supports domain scoring and in-depth programming language assessments, which provide a new layer of context for your recruiting team.
In short, by implementing a technical assessment system, your staffing firm can deliver additional value to clients by making data-driven staffing recommendations. Doing so is bound to result in an improved recommendation-to-interview ratio, which should make a noticeable impact on client satisfaction.
Scale Your Staffing Firm
With demand for technical talent on the rise, your company needs a more scalable solution for identifying, evaluating, and recommending candidates. After all, clients are far too busy to sort through countless resumes, only to settle for subpar results.
Schedule a demo of CodeSignal and learn why our Predictive Coding ScoreTM is rapidly becoming the new standard for successfully evaluating and placing technical talent. The CodeSignal Predictive Coding ScoreTM improves your recommendation-to-interview ratio by identifying developers who are likely to perform well in technical interviews (based on a candidate’s problem-solving abilities, skills, speed and accuracy and other important factors).
CodeSignal also makes it easy to conduct real-world programming assessments, identify talent, and share assessment results with clients. Learn more about improving your recommendation-to-interview ratio with CodeSignal.